| A Bandit Framework for Strategic Regression |
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1 |
| A Bayesian method for reducing bias in neural representational similarity analysis |
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2 |
| A Bio-inspired Redundant Sensing Architecture |
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1 |
| A Communication-Efficient Parallel Algorithm for Decision Tree |
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5 |
| A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order |
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5 |
| A Consistent Regularization Approach for Structured Prediction |
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2 |
| A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ |
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1 |
| A Credit Assignment Compiler for Joint Prediction |
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2 |
| A Locally Adaptive Normal Distribution |
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3 |
| A Minimax Approach to Supervised Learning |
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2 |
| A Multi-Batch L-BFGS Method for Machine Learning |
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3 |
| A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization |
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2 |
| A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing |
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2 |
| A Non-generative Framework and Convex Relaxations for Unsupervised Learning |
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1 |
| A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics |
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2 |
| A Powerful Generative Model Using Random Weights for the Deep Image Representation |
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3 |
| A Probabilistic Framework for Deep Learning |
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3 |
| A Probabilistic Model of Social Decision Making based on Reward Maximization |
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1 |
| A Probabilistic Programming Approach To Probabilistic Data Analysis |
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3 |
| A Pseudo-Bayesian Algorithm for Robust PCA |
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1 |
| A Simple Practical Accelerated Method for Finite Sums |
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4 |
| A Sparse Interactive Model for Matrix Completion with Side Information |
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4 |
| A Theoretically Grounded Application of Dropout in Recurrent Neural Networks |
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4 |
| A Unified Approach for Learning the Parameters of Sum-Product Networks |
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1 |
| A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control |
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1 |
| A posteriori error bounds for joint matrix decomposition problems |
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1 |
| A primal-dual method for conic constrained distributed optimization problems |
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2 |
| A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification |
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4 |
| A scaled Bregman theorem with applications |
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0 |
| A state-space model of cross-region dynamic connectivity in MEG/EEG |
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2 |
| Accelerating Stochastic Composition Optimization |
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3 |
| Achieving budget-optimality with adaptive schemes in crowdsourcing |
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2 |
| Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation |
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1 |
| Active Learning from Imperfect Labelers |
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1 |
| Active Learning with Oracle Epiphany |
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2 |
| Active Nearest-Neighbor Learning in Metric Spaces |
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1 |
| Adaptive Averaging in Accelerated Descent Dynamics |
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1 |
| Adaptive Concentration Inequalities for Sequential Decision Problems |
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2 |
| Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint |
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3 |
| Adaptive Neural Compilation |
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3 |
| Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy |
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3 |
| Adaptive Skills Adaptive Partitions (ASAP) |
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2 |
| Adaptive Smoothed Online Multi-Task Learning |
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4 |
| Adaptive optimal training of animal behavior |
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1 |
| Adversarial Multiclass Classification: A Risk Minimization Perspective |
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4 |
| Agnostic Estimation for Misspecified Phase Retrieval Models |
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3 |
| Algorithms and matching lower bounds for approximately-convex optimization |
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1 |
| An Architecture for Deep, Hierarchical Generative Models |
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2 |
| An Efficient Streaming Algorithm for the Submodular Cover Problem |
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3 |
| An Online Sequence-to-Sequence Model Using Partial Conditioning |
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3 |
| An algorithm for L1 nearest neighbor search via monotonic embedding |
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4 |
| An ensemble diversity approach to supervised binary hashing |
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2 |
| An equivalence between high dimensional Bayes optimal inference and M-estimation |
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1 |
| An urn model for majority voting in classification ensembles |
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4 |
| Ancestral Causal Inference |
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5 |
| Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm |
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2 |
| Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods |
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0 |
| Architectural Complexity Measures of Recurrent Neural Networks |
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3 |
| Assortment Optimization Under the Mallows model |
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4 |
| Asynchronous Parallel Greedy Coordinate Descent |
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5 |
| Attend, Infer, Repeat: Fast Scene Understanding with Generative Models |
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1 |
| Automated scalable segmentation of neurons from multispectral images |
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2 |
| Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks |
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4 |
| Average-case hardness of RIP certification |
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0 |
| Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction |
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1 |
| Backprop KF: Learning Discriminative Deterministic State Estimators |
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3 |
| Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition |
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| Barzilai-Borwein Step Size for Stochastic Gradient Descent |
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3 |
| Batched Gaussian Process Bandit Optimization via Determinantal Point Processes |
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3 |
| Bayesian Intermittent Demand Forecasting for Large Inventories |
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4 |
| Bayesian Optimization for Probabilistic Programs |
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2 |
| Bayesian Optimization with Robust Bayesian Neural Networks |
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3 |
| Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach |
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1 |
| Bayesian latent structure discovery from multi-neuron recordings |
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4 |
| Bayesian optimization for automated model selection |
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2 |
| Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian |
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4 |
| Beyond Exchangeability: The Chinese Voting Process |
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1 |
| Bi-Objective Online Matching and Submodular Allocations |
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1 |
| Binarized Neural Networks |
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6 |
| Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning |
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1 |
| Blind Attacks on Machine Learners |
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0 |
| Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering |
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2 |
| Boosting with Abstention |
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4 |
| Bootstrap Model Aggregation for Distributed Statistical Learning |
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2 |
| Brains on Beats |
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3 |
| Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation |
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1 |
| Budgeted stream-based active learning via adaptive submodular maximization |
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3 |
| CMA-ES with Optimal Covariance Update and Storage Complexity |
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3 |
| CNNpack: Packing Convolutional Neural Networks in the Frequency Domain |
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4 |
| CRF-CNN: Modeling Structured Information in Human Pose Estimation |
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3 |
| Can Active Memory Replace Attention? |
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4 |
| Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? |
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3 |
| Catching heuristics are optimal control policies |
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1 |
| Causal Bandits: Learning Good Interventions via Causal Inference |
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3 |
| Causal meets Submodular: Subset Selection with Directed Information |
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2 |
| CliqueCNN: Deep Unsupervised Exemplar Learning |
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4 |
| Clustering Signed Networks with the Geometric Mean of Laplacians |
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3 |
| Clustering with Bregman Divergences: an Asymptotic Analysis |
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1 |
| Clustering with Same-Cluster Queries |
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1 |
| Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions |
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3 |
| Coin Betting and Parameter-Free Online Learning |
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2 |
| Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks |
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3 |
| Combinatorial Energy Learning for Image Segmentation |
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3 |
| Combinatorial Multi-Armed Bandit with General Reward Functions |
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1 |
| Combinatorial semi-bandit with known covariance |
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1 |
| Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning |
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0 |
| Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation |
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5 |
| Communication-Optimal Distributed Clustering |
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2 |
| Community Detection on Evolving Graphs |
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1 |
| Completely random measures for modelling block-structured sparse networks |
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2 |
| Composing graphical models with neural networks for structured representations and fast inference |
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4 |
| Computational and Statistical Tradeoffs in Learning to Rank |
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2 |
| Computing and maximizing influence in linear threshold and triggering models |
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1 |
| Conditional Generative Moment-Matching Networks |
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4 |
| Conditional Image Generation with PixelCNN Decoders |
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2 |
| Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making |
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2 |
| Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random |
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1 |
| Consistent Kernel Mean Estimation for Functions of Random Variables |
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1 |
| Constraints Based Convex Belief Propagation |
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3 |
| Contextual semibandits via supervised learning oracles |
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4 |
| Convergence guarantees for kernel-based quadrature rules in misspecified settings |
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1 |
| Convex Two-Layer Modeling with Latent Structure |
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3 |
| Convolutional Neural Fabrics |
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4 |
| Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering |
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4 |
| Cooperative Graphical Models |
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3 |
| Cooperative Inverse Reinforcement Learning |
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1 |
| Coordinate-wise Power Method |
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4 |
| Coresets for Scalable Bayesian Logistic Regression |
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4 |
| Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated |
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3 |
| Coupled Generative Adversarial Networks |
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3 |
| Crowdsourced Clustering: Querying Edges vs Triangles |
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2 |
| Cyclades: Conflict-free Asynchronous Machine Learning |
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❌ |
✅ |
❌ |
✅ |
5 |
| DECOrrelated feature space partitioning for distributed sparse regression |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DISCO Nets : DISsimilarity COefficients Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data Poisoning Attacks on Factorization-Based Collaborative Filtering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data Programming: Creating Large Training Sets, Quickly |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Data driven estimation of Laplace-Beltrami operator |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Deconvolving Feedback Loops in Recommender Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep ADMM-Net for Compressive Sensing MRI |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Exploration via Bootstrapped DQN |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Learning Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Learning Models of the Retinal Response to Natural Scenes |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Deep Learning for Predicting Human Strategic Behavior |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Deep Learning without Poor Local Minima |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Deep Neural Networks with Inexact Matching for Person Re-Identification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Submodular Functions: Definitions and Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DeepMath - Deep Sequence Models for Premise Selection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dense Associative Memory for Pattern Recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Density Estimation via Discrepancy Based Adaptive Sequential Partition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Designing smoothing functions for improved worst-case competitive ratio in online optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dialog-based Language Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Differential Privacy without Sensitivity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Diffusion-Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dimension-Free Iteration Complexity of Finite Sum Optimization Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dimensionality Reduction of Massive Sparse Datasets Using Coresets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Direct Feedback Alignment Provides Learning in Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discriminative Gaifman Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Disease Trajectory Maps |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Disentangling factors of variation in deep representation using adversarial training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Distributed Flexible Nonlinear Tensor Factorization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Domain Separation Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Double Thompson Sampling for Dueling Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Doubly Convolutional Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dual Learning for Machine Translation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual Space Gradient Descent for Online Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Filter Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic Network Surgery for Efficient DNNs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic matrix recovery from incomplete observations under an exact low-rank constraint |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Edge-exchangeable graphs and sparsity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Efficient Neural Codes under Metabolic Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Nonparametric Smoothness Estimation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Second Order Online Learning by Sketching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Efficient state-space modularization for planning: theory, behavioral and neural signatures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Eliciting Categorical Data for Optimal Aggregation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| End-to-End Goal-Driven Web Navigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| End-to-End Kernel Learning with Supervised Convolutional Kernel Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Equality of Opportunity in Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Error Analysis of Generalized Nyström Kernel Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating the Size of a Large Network and its Communities from a Random Sample |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating the class prior and posterior from noisy positives and unlabeled data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exact Recovery of Hard Thresholding Pursuit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Examples are not enough, learn to criticize! Criticism for Interpretability |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exponential Family Embeddings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exponential expressivity in deep neural networks through transient chaos |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| FPNN: Field Probing Neural Networks for 3D Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fairness in Learning: Classic and Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast Active Set Methods for Online Spike Inference from Calcium Imaging |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Algorithms for Robust PCA via Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Distributed Submodular Cover: Public-Private Data Summarization |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Flexible Monotonic Functions with Ensembles of Lattices |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fast and Provably Good Seedings for k-Means |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Fast and accurate spike sorting of high-channel count probes with KiloSort |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fast learning rates with heavy-tailed losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast recovery from a union of subspaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Projection-free Convex Optimization over the Spectrahedron |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Feature selection in functional data classification with recursive maxima hunting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Feature-distributed sparse regression: a screen-and-clean approach |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Finding significant combinations of features in the presence of categorical covariates |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Finite Sample Prediction and Recovery Bounds for Ordinal Embedding |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Flexible Models for Microclustering with Application to Entity Resolution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Full-Capacity Unitary Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| GAP Safe Screening Rules for Sparse-Group Lasso |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gaussian Processes for Survival Analysis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| General Tensor Spectral Co-clustering for Higher-Order Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generating Images with Perceptual Similarity Metrics based on Deep Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generating Long-term Trajectories Using Deep Hierarchical Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generating Videos with Scene Dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generative Adversarial Imitation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Geometric Dirichlet Means Algorithm for topic inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Global Analysis of Expectation Maximization for Mixtures of Two Gaussians |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Optimality of Local Search for Low Rank Matrix Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Graph Clustering: Block-models and model free results |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Graphical Time Warping for Joint Alignment of Multiple Curves |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graphons, mergeons, and so on! |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Greedy Feature Construction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Guided Policy Search via Approximate Mirror Descent |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hardness of Online Sleeping Combinatorial Optimization Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hierarchical Clustering via Spreading Metrics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Question-Image Co-Attention for Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| High Dimensional Structured Superposition Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| High resolution neural connectivity from incomplete tracing data using nonnegative spline regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| High-Rank Matrix Completion and Clustering under Self-Expressive Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Higher-Order Factorization Machines |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| How Deep is the Feature Analysis underlying Rapid Visual Categorization? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Human Decision-Making under Limited Time |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Identification and Overidentification of Linear Structural Equation Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Deep Metric Learning with Multi-class N-pair Loss Objective |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Improved Dropout for Shallow and Deep Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Error Bounds for Tree Representations of Metric Spaces |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Techniques for Training GANs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Variational Inference with Inverse Autoregressive Flow |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving PAC Exploration Using the Median Of Means |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Incremental Variational Sparse Gaussian Process Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inference by Reparameterization in Neural Population Codes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Infinite Hidden Semi-Markov Modulated Interaction Point Process |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Integrated perception with recurrent multi-task neural networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interaction Networks for Learning about Objects, Relations and Physics |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Interpretable Distribution Features with Maximum Testing Power |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Interpretable Nonlinear Dynamic Modeling of Neural Trajectories |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Iterative Refinement of the Approximate Posterior for Directed Belief Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Joint quantile regression in vector-valued RKHSs |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Kernel Bayesian Inference with Posterior Regularization |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kronecker Determinantal Point Processes |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Ladder Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large Margin Discriminant Dimensionality Reduction in Prediction Space |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Large-Scale Price Optimization via Network Flow |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Latent Attention For If-Then Program Synthesis |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Launch and Iterate: Reducing Prediction Churn |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learnable Visual Markers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learned Region Sparsity and Diversity Also Predicts Visual Attention |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Bayesian networks with ancestral constraints |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Bound for Parameter Transfer Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Deep Embeddings with Histogram Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Deep Parsimonious Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Infinite RBMs with Frank-Wolfe |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Influence Functions from Incomplete Observations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Kernels with Random Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multiagent Communication with Backpropagation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Parametric Sparse Models for Image Super-Resolution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Sensor Multiplexing Design through Back-propagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Sparse Gaussian Graphical Models with Overlapping Blocks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Structured Sparsity in Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Transferrable Representations for Unsupervised Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Tree Structured Potential Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning User Perceived Clusters with Feature-Level Supervision |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning What and Where to Draw |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning a Metric Embedding for Face Recognition using the Multibatch Method |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning and Forecasting Opinion Dynamics in Social Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning brain regions via large-scale online structured sparse dictionary learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning feed-forward one-shot learners |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning in Games: Robustness of Fast Convergence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning shape correspondence with anisotropic convolutional neural networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning the Number of Neurons in Deep Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Communicate with Deep Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Poke by Poking: Experiential Learning of Intuitive Physics |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning to learn by gradient descent by gradient descent |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning under uncertainty: a comparison between R-W and Bayesian approach |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning values across many orders of magnitude |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Sparsity for Efficient Submodular Data Summarization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lifelong Learning with Weighted Majority Votes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| LightRNN: Memory and Computation-Efficient Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Linear Contextual Bandits with Knapsacks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linear Feature Encoding for Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Linear Relaxations for Finding Diverse Elements in Metric Spaces |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Linear dynamical neural population models through nonlinear embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Local Minimax Complexity of Stochastic Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Local Similarity-Aware Deep Feature Embedding |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Long-term Causal Effects via Behavioral Game Theory |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Low-Rank Regression with Tensor Responses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mapping Estimation for Discrete Optimal Transport |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matching Networks for One Shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Matrix Completion has No Spurious Local Minimum |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximal Sparsity with Deep Networks? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximization of Approximately Submodular Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Measuring Neural Net Robustness with Constraints |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Measuring the reliability of MCMC inference with bidirectional Monte Carlo |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Memory-Efficient Backpropagation Through Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MetaGrad: Multiple Learning Rates in Online Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimizing Quadratic Functions in Constant Time |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Mistake Bounds for Binary Matrix Completion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mixed Linear Regression with Multiple Components |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mixed vine copulas as joint models of spike counts and local field potentials |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multi-armed Bandits: Competing with Optimal Sequences |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-step learning and underlying structure in statistical models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multimodal Residual Learning for Visual QA |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiple-Play Bandits in the Position-Based Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multistage Campaigning in Social Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multivariate tests of association based on univariate tests |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Natural-Parameter Networks: A Class of Probabilistic Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Near-Optimal Smoothing of Structured Conditional Probability Matrices |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nearly Isometric Embedding by Relaxation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nested Mini-Batch K-Means |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Universal Discrete Denoiser |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| New Liftable Classes for First-Order Probabilistic Inference |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Normalized Spectral Map Synchronization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Object based Scene Representations using Fisher Scores of Local Subspace Projections |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Observational-Interventional Priors for Dose-Response Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Explore-Then-Commit strategies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Mixtures of Markov Chains |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Multiplicative Integration with Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Regularizing Rademacher Observation Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Robustness of Kernel Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Valid Optimal Assignment Kernels and Applications to Graph Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| On the Recursive Teaching Dimension of VC Classes |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Online Convex Optimization with Unconstrained Domains and Losses |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Pricing with Strategic and Patient Buyers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online and Differentially-Private Tensor Decomposition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Only H is left: Near-tight Episodic PAC RL |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Operator Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Architectures in a Solvable Model of Deep Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Binary Classifier Aggregation for General Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Black-Box Reductions Between Optimization Objectives |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Optimal Cluster Recovery in the Labeled Stochastic Block Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Learning for Multi-pass Stochastic Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Sparse Linear Encoders and Sparse PCA |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Tagging with Markov Chain Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal spectral transportation with application to music transcription |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimistic Bandit Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Gittins Indices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimizing affinity-based binary hashing using auxiliary coordinates |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Orthogonal Random Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC Reinforcement Learning with Rich Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PAC-Bayesian Theory Meets Bayesian Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Pairwise Choice Markov Chains |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Parameter Learning for Log-supermodular Distributions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Poisson-Gamma dynamical systems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Preference Completion from Partial Rankings |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Privacy Odometers and Filters: Pay-as-you-Go Composition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Probabilistic Inference with Generating Functions for Poisson Latent Variable Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Linear Multistep Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Probing the Compositionality of Intuitive Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Professor Forcing: A New Algorithm for Training Recurrent Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proximal Deep Structured Models |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pruning Random Forests for Prediction on a Budget |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Quantized Random Projections and Non-Linear Estimation of Cosine Similarity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantum Perceptron Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| R-FCN: Object Detection via Region-based Fully Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Reconstructing Parameters of Spreading Models from Partial Observations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Refined Lower Bounds for Adversarial Bandits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regret Bounds for Non-decomposable Metrics with Missing Labels |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Regret of Queueing Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Regularized Nonlinear Acceleration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relevant sparse codes with variational information bottleneck |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reshaped Wirtinger Flow for Solving Quadratic System of Equations |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Residual Networks Behave Like Ensembles of Relatively Shallow Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Review Networks for Caption Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reward Augmented Maximum Likelihood for Neural Structured Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Spectral Detection of Global Structures in the Data by Learning a Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust k-means: a Theoretical Revisit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Robustness of classifiers: from adversarial to random noise |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Rényi Divergence Variational Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| SDP Relaxation with Randomized Rounding for Energy Disaggregation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SURGE: Surface Regularized Geometry Estimation from a Single Image |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe Exploration in Finite Markov Decision Processes with Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Safe Policy Improvement by Minimizing Robust Baseline Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Safe and Efficient Off-Policy Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Complexity of Automated Mechanism Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sampling for Bayesian Program Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Satisfying Real-world Goals with Dataset Constraints |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Adaptive Stochastic Optimization Using Random Projections |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaled Least Squares Estimator for GLMs in Large-Scale Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Search Improves Label for Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Select-and-Sample for Spike-and-Slab Sparse Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Selective inference for group-sparse linear models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Semiparametric Differential Graph Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sequential Neural Models with Stochastic Layers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Showing versus doing: Teaching by demonstration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simple and Efficient Weighted Minwise Hashing |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Single Pass PCA of Matrix Products |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Single-Image Depth Perception in the Wild |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Solving Marginal MAP Problems with NP Oracles and Parity Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sorting out typicality with the inverse moment matrix SOS polynomial |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SoundNet: Learning Sound Representations from Unlabeled Video |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sparse Support Recovery with Non-smooth Loss Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spatiotemporal Residual Networks for Video Action Recognition |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Spectral Learning of Dynamic Systems from Nonequilibrium Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Split LBI: An Iterative Regularization Path with Structural Sparsity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Statistical Inference for Cluster Trees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Inference for Pairwise Graphical Models Using Score Matching |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Gradient Geodesic MCMC Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Gradient MCMC with Stale Gradients |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Online AUC Maximization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Stochastic Optimization for Large-scale Optimal Transport |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Structured Prediction under Bandit Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Three-Composite Convex Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Variance Reduction Methods for Saddle-Point Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Stochastic Variational Deep Kernel Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Strategic Attentive Writer for Learning Macro-Actions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structure-Blind Signal Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Structured Matrix Recovery via the Generalized Dantzig Selector |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Structured Prediction Theory Based on Factor Graph Complexity |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Structured Sparse Regression via Greedy Hard Thresholding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sub-sampled Newton Methods with Non-uniform Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sublinear Time Orthogonal Tensor Decomposition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Supervised Learning with Tensor Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Supervised Word Mover's Distance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Supervised learning through the lens of compression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Swapout: Learning an ensemble of deep architectures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Synthesis of MCMC and Belief Propagation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Synthesizing the preferred inputs for neurons in neural networks via deep generator networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tagger: Deep Unsupervised Perceptual Grouping |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Tensor Switching Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Forget-me-not Process |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Generalized Reparameterization Gradient |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Limits of Learning with Missing Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Multi-fidelity Multi-armed Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Multiple Quantile Graphical Model |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| The Multiscale Laplacian Graph Kernel |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| The Parallel Knowledge Gradient Method for Batch Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Power of Adaptivity in Identifying Statistical Alternatives |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Power of Optimization from Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Product Cut |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Robustness of Estimator Composition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| The non-convex Burer-Monteiro approach works on smooth semidefinite programs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Threshold Bandits, With and Without Censored Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Threshold Learning for Optimal Decision Making |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Tight Complexity Bounds for Optimizing Composite Objectives |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Conceptual Compression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Unifying Hamiltonian Monte Carlo and Slice Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tracking the Best Expert in Non-stationary Stochastic Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tractable Operations for Arithmetic Circuits of Probabilistic Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Tree-Structured Reinforcement Learning for Sequential Object Localization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Probabilistic Sparse Gaussian Process Approximations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding the Effective Receptive Field in Deep Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Unifying Count-Based Exploration and Intrinsic Motivation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Correspondence Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Domain Adaptation with Residual Transfer Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning for Physical Interaction through Video Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Learning from Noisy Networks with Applications to Hi-C Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Unsupervised Learning of 3D Structure from Images |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Learning of Spoken Language with Visual Context |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Risk Estimation Using Only Conditional Independence Structure |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Using Fast Weights to Attend to the Recent Past |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| VIME: Variational Information Maximizing Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Value Iteration Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Variance Reduction in Stochastic Gradient Langevin Dynamics |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Autoencoder for Deep Learning of Images, Labels and Captions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Variational Bayes on Monte Carlo Steroids |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Inference in Mixed Probabilistic Submodular Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Information Maximization for Feature Selection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Verification Based Solution for Structured MAB Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Visual Question Answering with Question Representation Update (QRU) |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Wasserstein Training of Restricted Boltzmann Machines |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| What Makes Objects Similar: A Unified Multi-Metric Learning Approach |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Without-Replacement Sampling for Stochastic Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| k*-Nearest Neighbors: From Global to Local |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| “Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |