| #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning |
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| A Bayesian Data Augmentation Approach for Learning Deep Models |
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| A Decomposition of Forecast Error in Prediction Markets |
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| A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering |
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| A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning |
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3 |
| A General Framework for Robust Interactive Learning |
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| A Greedy Approach for Budgeted Maximum Inner Product Search |
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2 |
| A KL-LUCB algorithm for Large-Scale Crowdsourcing |
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3 |
| A Learning Error Analysis for Structured Prediction with Approximate Inference |
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5 |
| A Linear-Time Kernel Goodness-of-Fit Test |
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4 |
| A Meta-Learning Perspective on Cold-Start Recommendations for Items |
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| A Minimax Optimal Algorithm for Crowdsourcing |
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2 |
| A New Alternating Direction Method for Linear Programming |
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2 |
| A New Theory for Matrix Completion |
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1 |
| A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent |
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3 |
| A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks |
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2 |
| A Regularized Framework for Sparse and Structured Neural Attention |
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2 |
| A Sample Complexity Measure with Applications to Learning Optimal Auctions |
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| A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis |
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| A Screening Rule for l1-Regularized Ising Model Estimation |
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5 |
| A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening |
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| A Unified Approach to Interpreting Model Predictions |
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3 |
| A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning |
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2 |
| A Universal Analysis of Large-Scale Regularized Least Squares Solutions |
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1 |
| A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control |
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5 |
| A graph-theoretic approach to multitasking |
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| A multi-agent reinforcement learning model of common-pool resource appropriation |
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| A simple model of recognition and recall memory |
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2 |
| A simple neural network module for relational reasoning |
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3 |
| A-NICE-MC: Adversarial Training for MCMC |
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3 |
| ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization |
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3 |
| AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms |
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| ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching |
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| Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds |
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3 |
| Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex |
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3 |
| Accelerated consensus via Min-Sum Splitting |
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1 |
| Acceleration and Averaging in Stochastic Descent Dynamics |
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| Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM |
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3 |
| Action Centered Contextual Bandits |
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| Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples |
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| Active Exploration for Learning Symbolic Representations |
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| Active Learning from Peers |
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4 |
| AdaGAN: Boosting Generative Models |
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| Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition |
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| Adaptive Active Hypothesis Testing under Limited Information |
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| Adaptive Batch Size for Safe Policy Gradients |
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| Adaptive Bayesian Sampling with Monte Carlo EM |
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3 |
| Adaptive Classification for Prediction Under a Budget |
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| Adaptive Clustering through Semidefinite Programming |
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| Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter |
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3 |
| Adaptive stimulus selection for optimizing neural population responses |
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4 |
| Adversarial Ranking for Language Generation |
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3 |
| Adversarial Surrogate Losses for Ordinal Regression |
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3 |
| Adversarial Symmetric Variational Autoencoder |
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| Affine-Invariant Online Optimization and the Low-rank Experts Problem |
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| Affinity Clustering: Hierarchical Clustering at Scale |
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| Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification |
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| Alternating Estimation for Structured High-Dimensional Multi-Response Models |
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| Alternating minimization for dictionary learning with random initialization |
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1 |
| An Empirical Bayes Approach to Optimizing Machine Learning Algorithms |
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4 |
| An Empirical Study on The Properties of Random Bases for Kernel Methods |
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3 |
| An Error Detection and Correction Framework for Connectomics |
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4 |
| An inner-loop free solution to inverse problems using deep neural networks |
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3 |
| Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems |
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4 |
| Approximate Supermodularity Bounds for Experimental Design |
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| Approximation Algorithms for $\ell_0$-Low Rank Approximation |
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1 |
| Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search |
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| Approximation and Convergence Properties of Generative Adversarial Learning |
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| Associative Embedding: End-to-End Learning for Joint Detection and Grouping |
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3 |
| Asynchronous Coordinate Descent under More Realistic Assumptions |
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3 |
| Asynchronous Parallel Coordinate Minimization for MAP Inference |
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3 |
| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin |
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3 |
| Attention is All you Need |
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5 |
| Attentional Pooling for Action Recognition |
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| Avoiding Discrimination through Causal Reasoning |
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| Balancing information exposure in social networks |
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4 |
| Bandits Dueling on Partially Ordered Sets |
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3 |
| Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models |
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4 |
| Bayesian Compression for Deep Learning |
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5 |
| Bayesian Dyadic Trees and Histograms for Regression |
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| Bayesian GAN |
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| Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes |
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4 |
| Bayesian Optimization with Gradients |
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5 |
| Best Response Regression |
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5 |
| Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model |
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3 |
| Beyond Parity: Fairness Objectives for Collaborative Filtering |
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| Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization |
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3 |
| Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting |
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3 |
| Boltzmann Exploration Done Right |
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| Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization |
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4 |
| Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction |
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3 |
| Bridging the Gap Between Value and Policy Based Reinforcement Learning |
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| Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent |
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5 |
| Causal Effect Inference with Deep Latent-Variable Models |
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| Certified Defenses for Data Poisoning Attacks |
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| Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling |
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2 |
| Clustering Billions of Reads for DNA Data Storage |
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4 |
| Clustering Stable Instances of Euclidean k-means. |
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2 |
| Clustering with Noisy Queries |
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1 |
| Coded Distributed Computing for Inverse Problems |
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3 |
| Cold-Start Reinforcement Learning with Softmax Policy Gradient |
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5 |
| Collaborative Deep Learning in Fixed Topology Networks |
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3 |
| Collaborative PAC Learning |
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| Collapsed variational Bayes for Markov jump processes |
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3 |
| Collecting Telemetry Data Privately |
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1 |
| Communication-Efficient Distributed Learning of Discrete Distributions |
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| Compatible Reward Inverse Reinforcement Learning |
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3 |
| Compression-aware Training of Deep Networks |
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4 |
| Concentration of Multilinear Functions of the Ising Model with Applications to Network Data |
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2 |
| Concrete Dropout |
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4 |
| Conic Scan-and-Cover algorithms for nonparametric topic modeling |
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3 |
| Conservative Contextual Linear Bandits |
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2 |
| Consistent Multitask Learning with Nonlinear Output Relations |
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2 |
| Consistent Robust Regression |
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3 |
| Context Selection for Embedding Models |
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5 |
| Continual Learning with Deep Generative Replay |
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1 |
| Continuous DR-submodular Maximization: Structure and Algorithms |
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4 |
| Contrastive Learning for Image Captioning |
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3 |
| Controllable Invariance through Adversarial Feature Learning |
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4 |
| Convergence Analysis of Two-layer Neural Networks with ReLU Activation |
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3 |
| Convergence of Gradient EM on Multi-component Mixture of Gaussians |
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1 |
| Convergence rates of a partition based Bayesian multivariate density estimation method |
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| Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks |
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3 |
| Convolutional Gaussian Processes |
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3 |
| Convolutional Phase Retrieval |
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| Cortical microcircuits as gated-recurrent neural networks |
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3 |
| Cost efficient gradient boosting |
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4 |
| Counterfactual Fairness |
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3 |
| Countering Feedback Delays in Multi-Agent Learning |
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1 |
| Cross-Spectral Factor Analysis |
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2 |
| DPSCREEN: Dynamic Personalized Screening |
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3 |
| Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs |
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❌ |
✅ |
2 |
| Deanonymization in the Bitcoin P2P Network |
✅ |
✅ |
✅ |
❌ |
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❌ |
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3 |
| Decoding with Value Networks for Neural Machine Translation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Decomposable Submodular Function Minimization: Discrete and Continuous |
❌ |
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✅ |
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✅ |
❌ |
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3 |
| Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deconvolutional Paragraph Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Decoupling "when to update" from "how to update" |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Dynamic Poisson Factorization Model |
❌ |
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✅ |
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2 |
| Deep Hyperalignment |
✅ |
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✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Deep Hyperspherical Learning |
❌ |
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✅ |
✅ |
❌ |
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✅ |
3 |
| Deep Lattice Networks and Partial Monotonic Functions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Deep Learning with Topological Signatures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Mean-Shift Priors for Image Restoration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Recurrent Neural Network-Based Identification of Precursor microRNAs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Reinforcement Learning from Human Preferences |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Sets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Subspace Clustering Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Deep Supervised Discrete Hashing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Voice 2: Multi-Speaker Neural Text-to-Speech |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deliberation Networks: Sequence Generation Beyond One-Pass Decoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Detrended Partial Cross Correlation for Brain Connectivity Analysis |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Differentiable Learning of Logical Rules for Knowledge Base Reasoning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiable Learning of Submodular Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Differentially Private Empirical Risk Minimization Revisited: Faster and More General |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially private Bayesian learning on distributed data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Diffusion Approximations for Online Principal Component Estimation and Global Convergence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dilated Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discovering Potential Correlations via Hypercontractivity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Discriminative State Space Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distral: Robust multitask reinforcement learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Diving into the shallows: a computational perspective on large-scale shallow learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Do Deep Neural Networks Suffer from Crowding? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Doubly Stochastic Variational Inference for Deep Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DropoutNet: Addressing Cold Start in Recommender Systems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dual Discriminator Generative Adversarial Nets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dual Path Networks |
❌ |
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✅ |
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✅ |
❌ |
✅ |
3 |
| Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Dualing GANs |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dynamic Importance Sampling for Anytime Bounds of the Partition Function |
✅ |
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✅ |
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❌ |
❌ |
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3 |
| Dynamic Revenue Sharing |
✅ |
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✅ |
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2 |
| Dynamic Routing Between Capsules |
✅ |
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✅ |
✅ |
❌ |
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4 |
| Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dynamic-Depth Context Tree Weighting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| EX2: Exploration with Exemplar Models for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Early stopping for kernel boosting algorithms: A general analysis with localized complexities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Effective Parallelisation for Machine Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Efficient Online Linear Optimization with Approximation Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Second-Order Online Kernel Learning with Adaptive Embedding |
✅ |
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✅ |
❌ |
✅ |
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4 |
| Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient and Flexible Inference for Stochastic Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Eigen-Distortions of Hierarchical Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Elementary Symmetric Polynomials for Optimal Experimental Design |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| End-to-end Differentiable Proving |
✅ |
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✅ |
✅ |
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3 |
| Ensemble Sampling |
✅ |
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2 |
| Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Estimating Mutual Information for Discrete-Continuous Mixtures |
✅ |
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✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Estimation of the covariance structure of heavy-tailed distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Expectation Propagation for t-Exponential Family Using q-Algebra |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems |
✅ |
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❌ |
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❌ |
❌ |
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1 |
| Experimental Design for Learning Causal Graphs with Latent Variables |
✅ |
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❌ |
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❌ |
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1 |
| Exploring Generalization in Deep Learning |
❌ |
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✅ |
❌ |
❌ |
❌ |
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1 |
| Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| FALKON: An Optimal Large Scale Kernel Method |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Fader Networks:Manipulating Images by Sliding Attributes |
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3 |
| Fair Clustering Through Fairlets |
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1 |
| Fast Black-box Variational Inference through Stochastic Trust-Region Optimization |
✅ |
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3 |
| Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe |
✅ |
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1 |
| Fast amortized inference of neural activity from calcium imaging data with variational autoencoders |
❌ |
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4 |
| Fast, Sample-Efficient Algorithms for Structured Phase Retrieval |
✅ |
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❌ |
✅ |
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4 |
| Fast-Slow Recurrent Neural Networks |
❌ |
✅ |
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✅ |
❌ |
❌ |
✅ |
4 |
| Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers |
✅ |
❌ |
✅ |
❌ |
✅ |
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4 |
| Federated Multi-Task Learning |
✅ |
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✅ |
❌ |
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5 |
| Few-Shot Adversarial Domain Adaptation |
✅ |
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✅ |
❌ |
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3 |
| Few-Shot Learning Through an Information Retrieval Lens |
✅ |
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✅ |
✅ |
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❌ |
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4 |
| Filtering Variational Objectives |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fisher GAN |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Fitting Low-Rank Tensors in Constant Time |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Flexible statistical inference for mechanistic models of neural dynamics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| From Bayesian Sparsity to Gated Recurrent Nets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| From Parity to Preference-based Notions of Fairness in Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| From which world is your graph |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| GP CaKe: Effective brain connectivity with causal kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gated Recurrent Convolution Neural Network for OCR |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gauging Variational Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gaussian Quadrature for Kernel Features |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Gaussian process based nonlinear latent structure discovery in multivariate spike train data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Generalization Properties of Learning with Random Features |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generalized Linear Model Regression under Distance-to-set Penalties |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Generalizing GANs: A Turing Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generating steganographic images via adversarial training |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Generative Local Metric Learning for Kernel Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geometric Descent Method for Convex Composite Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| GibbsNet: Iterative Adversarial Inference for Deep Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Good Semi-supervised Learning That Requires a Bad GAN |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Descent Can Take Exponential Time to Escape Saddle Points |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Episodic Memory for Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient Methods for Submodular Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient descent GAN optimization is locally stable |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Graph Matching via Multiplicative Update Algorithm |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Group Additive Structure Identification for Kernel Nonparametric Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Group Sparse Additive Machine |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hash Embeddings for Efficient Word Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hiding Images in Plain Sight: Deep Steganography |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hierarchical Attentive Recurrent Tracking |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hierarchical Clustering Beyond the Worst-Case |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Implicit Models and Likelihood-Free Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Methods of Moments |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| High-Order Attention Models for Visual Question Answering |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Hindsight Experience Replay |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How regularization affects the critical points in linear networks |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Hybrid Reward Architecture for Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hypothesis Transfer Learning via Transformation Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identification of Gaussian Process State Space Models |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Identifying Outlier Arms in Multi-Armed Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Imagination-Augmented Agents for Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Regularization in Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improved Dynamic Regret for Non-degenerate Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Graph Laplacian via Geometric Self-Consistency |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Training of Wasserstein GANs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving the Expected Improvement Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Incorporating Side Information by Adaptive Convolution |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Independence clustering (without a matrix) |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inductive Representation Learning on Large Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inference in Graphical Models via Semidefinite Programming Hierarchies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inferring Generative Model Structure with Static Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Influence Maximization with $\varepsilon$-Almost Submodular Threshold Functions |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Information-theoretic analysis of generalization capability of learning algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Inhomogeneous Hypergraph Clustering with Applications |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Integration Methods and Optimization Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Interactive Submodular Bandit |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Introspective Classification with Convolutional Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Invariance and Stability of Deep Convolutional Representations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Inverse Filtering for Hidden Markov Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Inverse Reward Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Is the Bellman residual a bad proxy? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Joint distribution optimal transportation for domain adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| K-Medoids For K-Means Seeding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Kernel Feature Selection via Conditional Covariance Minimization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernel functions based on triplet comparisons |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Label Distribution Learning Forests |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Label Efficient Learning of Transferable Representations acrosss Domains and Tasks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Language Modeling with Recurrent Highway Hypernetworks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learned D-AMP: Principled Neural Network based Compressive Image Recovery |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learned in Translation: Contextualized Word Vectors |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning A Structured Optimal Bipartite Graph for Co-Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Active Learning from Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Affinity via Spatial Propagation Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Causal Structures Using Regression Invariance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Chordal Markov Networks via Branch and Bound |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning Combinatorial Optimization Algorithms over Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Disentangled Representations with Semi-Supervised Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Efficient Object Detection Models with Knowledge Distillation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Graph Representations with Embedding Propagation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Hierarchical Information Flow with Recurrent Neural Modules |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Linear Dynamical Systems via Spectral Filtering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Low-Dimensional Metrics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Mixture of Gaussians with Streaming Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Multiple Tasks with Multilinear Relationship Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Neural Representations of Human Cognition across Many fMRI Studies |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Overcomplete HMMs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Populations of Parameters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning ReLUs via Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Spherical Convolution for Fast Features from 360° Imagery |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Unknown Markov Decision Processes: A Thompson Sampling Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning a Multi-View Stereo Machine |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning from Complementary Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learning multiple visual domains with residual adapters |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Compose Domain-Specific Transformations for Data Augmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Inpaint for Image Compression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Model the Tail |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Pivot with Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Learning to See Physics via Visual De-animation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning with Average Top-k Loss |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning with Bandit Feedback in Potential Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning with Feature Evolvable Streams |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LightGBM: A Highly Efficient Gradient Boosting Decision Tree |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Time Computation of Moments in Sum-Product Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linear regression without correspondence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linearly constrained Gaussian processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Local Aggregative Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Lookahead Bayesian Optimization with Inequality Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lower bounds on the robustness to adversarial perturbations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MMD GAN: Towards Deeper Understanding of Moment Matching Network |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mapping distinct timescales of functional interactions among brain networks |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| MarrNet: 3D Shape Reconstruction via 2.5D Sketches |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| MaskRNN: Instance Level Video Object Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Masked Autoregressive Flow for Density Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matching neural paths: transfer from recognition to correspondence search |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matching on Balanced Nonlinear Representations for Treatment Effects Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix Norm Estimation from a Few Entries |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Max-Margin Invariant Features from Transformed Unlabelled Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Maximum Margin Interval Trees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Maxing and Ranking with Few Assumptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mean Field Residual Networks: On the Edge of Chaos |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Min-Max Propagation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Minimal Exploration in Structured Stochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimax Estimation of Bandable Precision Matrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimizing a Submodular Function from Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mixture-Rank Matrix Approximation for Collaborative Filtering |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model evidence from nonequilibrium simulations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-Powered Conditional Independence Test |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Modulating early visual processing by language |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Monte-Carlo Tree Search by Best Arm Identification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Armed Bandits with Metric Movement Costs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Information Source Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Multi-Objective Non-parametric Sequential Prediction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Task Learning for Contextual Bandits |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-View Decision Processes: The Helper-AI Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-output Polynomial Networks and Factorization Machines |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Multi-view Matrix Factorization for Linear Dynamical System Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-way Interacting Regression via Factorization Machines |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multimodal Learning and Reasoning for Visual Question Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Multiresolution Kernel Approximation for Gaussian Process Regression |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiscale Quantization for Fast Similarity Search |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Multitask Spectral Learning of Weighted Automata |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Natural Value Approximators: Learning when to Trust Past Estimates |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near Optimal Sketching of Low-Rank Tensor Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Neural Discrete Representation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Expectation Maximization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Program Meta-Induction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Neural Variational Inference and Learning in Undirected Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural system identification for large populations separating “what” and “where” |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NeuralFDR: Learning Discovery Thresholds from Hypothesis Features |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Noise-Tolerant Interactive Learning Using Pairwise Comparisons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Stationary Spectral Kernels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-convex Finite-Sum Optimization Via SCSG Methods |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Non-parametric Structured Output Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonbacktracking Bounds on the Influence in Independent Cascade Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonlinear Acceleration of Stochastic Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonlinear random matrix theory for deep learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric Online Regression while Learning the Metric |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Off-policy evaluation for slate recommendation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Blackbox Backpropagation and Jacobian Sensing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Fairness and Calibration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Frank-Wolfe and Equilibrium Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Optimal Generalizability in Parametric Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Structured Prediction Theory with Calibrated Convex Surrogate Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On clustering network-valued data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| On the Complexity of Learning Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Consistency of Quick Shift |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Model Shrinkage Effect of Gamma Process Edge Partition Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Optimization Landscape of Tensor Decompositions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Power of Truncated SVD for General High-rank Matrix Estimation Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On-the-fly Operation Batching in Dynamic Computation Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| OnACID: Online Analysis of Calcium Imaging Data in Real Time |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| One-Shot Imitation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| One-Sided Unsupervised Domain Mapping |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Convex Optimization with Stochastic Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Online Dynamic Programming |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Learning for Multivariate Hawkes Processes |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Learning with Transductive Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with a Hint |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Prediction with Selfish Experts |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Online Reinforcement Learning in Stochastic Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online control of the false discovery rate with decaying memory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online multiclass boosting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Online to Offline Conversions, Universality and Adaptive Minibatch Sizes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Sample Complexity of M-wise Data for Top-K Ranking |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Shrinkage of Singular Values Under Random Data Contamination |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimistic posterior sampling for reinforcement learning: worst-case regret bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimized Pre-Processing for Discrimination Prevention |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Overcoming Catastrophic Forgetting by Incremental Moment Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| PRUNE: Preserving Proximity and Global Ranking for Network Embedding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parallel Streaming Wasserstein Barycenters |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Parameter-Free Online Learning via Model Selection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Parametric Simplex Method for Sparse Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Permutation-based Causal Inference Algorithms with Interventions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Perturbative Black Box Variational Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Phase Transitions in the Pooled Data Problem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| PixelGAN Autoencoders |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Pixels to Graphs by Associative Embedding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Plan, Attend, Generate: Planning for Sequence-to-Sequence Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Poincaré Embeddings for Learning Hierarchical Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Policy Gradient With Value Function Approximation For Collective Multiagent Planning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Polynomial time algorithms for dual volume sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Population Matching Discrepancy and Applications in Deep Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Pose Guided Person Image Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Position-based Multiple-play Bandit Problem with Unknown Position Bias |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Positive-Unlabeled Learning with Non-Negative Risk Estimator |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Practical Data-Dependent Metric Compression with Provable Guarantees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Practical Hash Functions for Similarity Estimation and Dimensionality Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Practical Locally Private Heavy Hitters |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predicting Scene Parsing and Motion Dynamics in the Future |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Predicting User Activity Level In Point Processes With Mass Transport Equation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Predictive State Recurrent Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Predictive-State Decoders: Encoding the Future into Recurrent Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Premise Selection for Theorem Proving by Deep Graph Embedding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Preventing Gradient Explosions in Gated Recurrent Units |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Principles of Riemannian Geometry in Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Probabilistic Rule Realization and Selection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Process-constrained batch Bayesian optimisation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Protein Interface Prediction using Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Prototypical Networks for Few-shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| QMDP-Net: Deep Learning for Planning under Partial Observability |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quantifying how much sensory information in a neural code is relevant for behavior |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Query Complexity of Clustering with Side Information |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Question Asking as Program Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Random Permutation Online Isotonic Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random Projection Filter Bank for Time Series Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Ranking Data with Continuous Labels through Oriented Recursive Partitions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Real Time Image Saliency for Black Box Classifiers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Real-Time Bidding with Side Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reconstruct & Crush Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reconstructing perceived faces from brain activations with deep adversarial neural decoding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Recurrent Ladder Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Recursive Sampling for the Nystrom Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recycling Privileged Learning and Distribution Matching for Fairness |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reducing Reparameterization Gradient Variance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regret Analysis for Continuous Dueling Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regret Minimization in MDPs with Options without Prior Knowledge |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regularized Modal Regression with Applications in Cognitive Impairment Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning under Model Mismatch |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reliable Decision Support using Counterfactual Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Renyi Differential Privacy Mechanisms for Posterior Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Repeated Inverse Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Revenue Optimization with Approximate Bid Predictions |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Riemannian approach to batch normalization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Conditional Probabilities |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Robust Estimation of Neural Signals in Calcium Imaging |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Imitation of Diverse Behaviors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Optimization for Non-Convex Objectives |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rotting Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Runtime Neural Pruning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SGD Learns the Conjugate Kernel Class of the Network |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Safe Adaptive Importance Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Safe Model-based Reinforcement Learning with Stability Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safe and Nested Subgame Solving for Imperfect-Information Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Saliency-based Sequential Image Attention with Multiset Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable Demand-Aware Recommendation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Scalable Generalized Linear Bandits: Online Computation and Hashing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scalable Levy Process Priors for Spectral Kernel Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Log Determinants for Gaussian Process Kernel Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Model Selection for Belief Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Scalable Planning with Tensorflow for Hybrid Nonlinear Domains |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Scalable Variational Inference for Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SchNet: A continuous-filter convolutional neural network for modeling quantum interactions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Selective Classification for Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Normalizing Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Intrinsic Image Decomposition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-supervised Learning of Motion Capture |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shallow Updates for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Shape and Material from Sound |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sharpness, Restart and Acceleration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simple strategies for recovering inner products from coarsely quantized random projections |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Sobolev Training for Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Solving Most Systems of Random Quadratic Equations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparse Approximate Conic Hulls |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse Embedded $k$-Means Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Sparse convolutional coding for neuronal assembly detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Mixture Kernels for Multi-Output Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectrally-normalized margin bounds for neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spherical convolutions and their application in molecular modelling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stabilizing Training of Generative Adversarial Networks through Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| State Aware Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical Cost Sharing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stein Variational Gradient Descent as Gradient Flow |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Approximation for Canonical Correlation Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Mirror Descent in Variationally Coherent Optimization Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Submodular Maximization: The Case of Coverage Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic and Adversarial Online Learning without Hyperparameters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Straggler Mitigation in Distributed Optimization Through Data Encoding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Sparse Gaussian Process Approximations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Weak Submodularity: Interpreting Neural Networks on the Fly |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Structured Bayesian Pruning via Log-Normal Multiplicative Noise |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Structured Embedding Models for Grouped Data |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Structured Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Style Transfer from Non-Parallel Text by Cross-Alignment |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Subset Selection and Summarization in Sequential Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Subset Selection under Noise |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Subspace Clustering via Tangent Cones |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Successor Features for Transfer in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Targeting EEG/LFP Synchrony with Neural Nets |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Task-based End-to-end Model Learning in Stochastic Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Teaching Machines to Describe Images with Natural Language Feedback |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tensor Biclustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Testing and Learning on Distributions with Symmetric Noise Invariance |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Expressive Power of Neural Networks: A View from the Width |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Importance of Communities for Learning to Influence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Marginal Value of Adaptive Gradient Methods in Machine Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Numerics of GANs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Reversible Residual Network: Backpropagation Without Storing Activations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| The Scaling Limit of High-Dimensional Online Independent Component Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The power of absolute discounting: all-dimensional distribution estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Thinking Fast and Slow with Deep Learning and Tree Search |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time-dependent spatially varying graphical models, with application to brain fMRI data analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tomography of the London Underground: a Scalable Model for Origin-Destination Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Toward Multimodal Image-to-Image Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Accurate Binary Convolutional Neural Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Towards Generalization and Simplicity in Continuous Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tractability in Structured Probability Spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Train longer, generalize better: closing the generalization gap in large batch training of neural networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Training Deep Networks without Learning Rates Through Coin Betting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training Quantized Nets: A Deeper Understanding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Translation Synchronization via Truncated Least Squares |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Triangle Generative Adversarial Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Trimmed Density Ratio Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Triple Generative Adversarial Nets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Unbiased estimates for linear regression via volume sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Unbounded cache model for online language modeling with open vocabulary |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Universal Style Transfer via Feature Transforms |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Universal consistency and minimax rates for online Mondrian Forests |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Image-to-Image Translation Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Learning of Disentangled Representations from Video |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Sequence Classification using Sequential Output Statistics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Transformation Learning via Convex Relaxations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised learning of object frames by dense equivariant image labelling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Uprooting and Rerooting Higher-Order Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| VAE Learning via Stein Variational Gradient Descent |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| VAIN: Attentional Multi-agent Predictive Modeling |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Value Prediction Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variable Importance Using Decision Trees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variance-based Regularization with Convex Objectives |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Inference for Gaussian Process Models with Linear Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Inference via $\chi$ Upper Bound Minimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Laws of Visual Attention for Dynamic Scenes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Memory Addressing in Generative Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Visual Interaction Networks: Learning a Physics Simulator from Video |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Visual Reference Resolution using Attention Memory for Visual Dialog |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Wasserstein Learning of Deep Generative Point Process Models |
✅ |
✅ |
✅ |
❌ |
❌ |
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✅ |
4 |
| Welfare Guarantees from Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Working hard to know your neighbor's margins: Local descriptor learning loss |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| YASS: Yet Another Spike Sorter |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Z-Forcing: Training Stochastic Recurrent Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Zap Q-Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
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2 |
| f-GANs in an Information Geometric Nutshell |
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✅ |
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❌ |
❌ |
❌ |
❌ |
2 |
| k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |