| 3D Object Proposals for Accurate Object Class Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Bayesian Framework for Modeling Confidence in Perceptual Decision Making |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Complete Recipe for Stochastic Gradient MCMC |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements |
✅ |
❌ |
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❌ |
✅ |
❌ |
✅ |
3 |
| A Dual Augmented Block Minimization Framework for Learning with Limited Memory |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure |
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❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| A Gaussian Process Model of Quasar Spectral Energy Distributions |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice |
✅ |
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✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A Market Framework for Eliciting Private Data |
✅ |
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❌ |
❌ |
❌ |
❌ |
1 |
| A Nonconvex Optimization Framework for Low Rank Matrix Estimation |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| A Recurrent Latent Variable Model for Sequential Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Reduced-Dimension fMRI Shared Response Model |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Structural Smoothing Framework For Robust Graph Comparison |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Theory of Decision Making Under Dynamic Context |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Universal Catalyst for First-Order Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Universal Primal-Dual Convex Optimization Framework |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A class of network models recoverable by spectral clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A fast, universal algorithm to learn parametric nonlinear embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A hybrid sampler for Poisson-Kingman mixture models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Accelerated Mirror Descent in Continuous and Discrete Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Accelerated Proximal Gradient Methods for Nonconvex Programming |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Action-Conditional Video Prediction using Deep Networks in Atari Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Active Learning from Weak and Strong Labelers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Adaptive Online Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Stochastic Optimization: From Sets to Paths |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Prediction Games for Multivariate Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Algorithmic Stability and Uniform Generalization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Alternating Minimization for Regression Problems with Vector-valued Outputs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Analysis of Robust PCA via Local Incoherence |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Approximating Sparse PCA from Incomplete Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Associative Memory via a Sparse Recovery Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Attention-Based Models for Speech Recognition |
❌ |
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✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Automatic Variational Inference in Stan |
✅ |
✅ |
✅ |
❌ |
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✅ |
✅ |
5 |
| BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Backpropagation for Energy-Efficient Neuromorphic Computing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandits with Unobserved Confounders: A Causal Approach |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Barrier Frank-Wolfe for Marginal Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Basis refinement strategies for linear value function approximation in MDPs |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bayesian Active Model Selection with an Application to Automated Audiometry |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization with Exponential Convergence |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian dark knowledge |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond Convexity: Stochastic Quasi-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bidirectional Recurrent Neural Networks as Generative Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| BinaryConnect: Training Deep Neural Networks with binary weights during propagations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Biologically Inspired Dynamic Textures for Probing Motion Perception |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Black-box optimization of noisy functions with unknown smoothness |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bounding errors of Expectation-Propagation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bounding the Cost of Search-Based Lifted Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Calibrated Structured Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Character-level Convolutional Networks for Text Classification |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Closed-form Estimators for High-dimensional Generalized Linear Models |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Collaborative Filtering with Graph Information: Consistency and Scalable Methods |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Collaboratively Learning Preferences from Ordinal Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Color Constancy by Learning to Predict Chromaticity from Luminance |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Column Selection via Adaptive Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Combinatorial Bandits Revisited |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial Cascading Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Communication Complexity of Distributed Convex Learning and Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Community Detection via Measure Space Embedding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Competitive Distribution Estimation: Why is Good-Turing Good |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Compressive spectral embedding: sidestepping the SVD |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Consistent Multilabel Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Convergence Analysis of Prediction Markets via Randomized Subspace Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence Rates of Active Learning for Maximum Likelihood Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence rates of sub-sampled Newton methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Convolutional Networks on Graphs for Learning Molecular Fingerprints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Convolutional spike-triggered covariance analysis for neural subunit models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Copeland Dueling Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Copula variational inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cornering Stationary and Restless Mixing Bandits with Remix-UCB |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Data Generation as Sequential Decision Making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Decomposition Bounds for Marginal MAP |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Deep Convolutional Inverse Graphics Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Knowledge Tracing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Poisson Factor Modeling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Temporal Sigmoid Belief Networks for Sequence Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Visual Analogy-Making |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep learning with Elastic Averaging SGD |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deeply Learning the Messages in Message Passing Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Differentially Private Learning of Structured Discrete Distributions |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Differentially private subspace clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discrete Rényi Classifiers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discriminative Robust Transformation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Submodular Cover: Succinctly Summarizing Massive Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distributionally Robust Logistic Regression |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Compressive Phase Retrieval with Constrained Sensing Vectors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Non-greedy Optimization of Decision Trees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Output Kernel Learning for Multiple Tasks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Efficient Thompson Sampling for Online Matrix-Factorization Recommendation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient and Parsimonious Agnostic Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient and Robust Automated Machine Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Embedding Inference for Structured Multilabel Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| End-To-End Memory Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Enforcing balance allows local supervised learning in spiking recurrent networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Equilibrated adaptive learning rates for non-convex optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating Mixture Models via Mixtures of Polynomials |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Evaluating the statistical significance of biclusters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exactness of Approximate MAP Inference in Continuous MRFs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Expectation Particle Belief Propagation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Explore no more: Improved high-probability regret bounds for non-stochastic bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploring Models and Data for Image Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Expressing an Image Stream with a Sequence of Natural Sentences |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Extending Gossip Algorithms to Distributed Estimation of U-statistics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Bidirectional Probability Estimation in Markov Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Classification Rates for High-dimensional Gaussian Generative Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Fast Convergence of Regularized Learning in Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Fast Distributed k-Center Clustering with Outliers on Massive Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Lifted MAP Inference via Partitioning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Fast Randomized Kernel Ridge Regression with Statistical Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Rates for Exp-concave Empirical Risk Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast Second Order Stochastic Backpropagation for Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Two-Sample Testing with Analytic Representations of Probability Measures |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fast and Accurate Inference of Plackett–Luce Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Guaranteed Tensor Decomposition via Sketching |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast and Memory Optimal Low-Rank Matrix Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast, Provable Algorithms for Isotonic Regression in all L_p-norms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fighting Bandits with a New Kind of Smoothness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Analysis of Projected Langevin Monte Carlo |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| From random walks to distances on unweighted graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GAP Safe screening rules for sparse multi-task and multi-class models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| GP Kernels for Cross-Spectrum Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gaussian Process Random Fields |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generalization in Adaptive Data Analysis and Holdout Reuse |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generative Image Modeling Using Spatial LSTMs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Gradient Estimation Using Stochastic Computation Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Grammar as a Foreign Language |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| HONOR: Hybrid Optimization for NOn-convex Regularized problems |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Halting in Random Walk Kernels |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hidden Technical Debt in Machine Learning Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| High-dimensional neural spike train analysis with generalized count linear dynamical systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Human Memory Search as Initial-Visit Emitting Random Walk |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Inference for determinantal point processes without spectral knowledge |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Infinite Factorial Dynamical Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Information-theoretic lower bounds for convex optimization with erroneous oracles |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Interactive Control of Diverse Complex Characters with Neural Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inverse Reinforcement Learning with Locally Consistent Reward Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Is Approval Voting Optimal Given Approval Votes? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Kullback-Leibler Proximal Variational Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Large-scale probabilistic predictors with and without guarantees of validity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent Bayesian melding for integrating individual and population models |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Learnability of Influence in Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Bayesian Networks with Thousands of Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Causal Graphs with Small Interventions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Continuous Control Policies by Stochastic Value Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning From Small Samples: An Analysis of Simple Decision Heuristics |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Structured Output Representation using Deep Conditional Generative Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Theory and Algorithms for Forecasting Non-stationary Time Series |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Wake-Sleep Recurrent Attention Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning both Weights and Connections for Efficient Neural Network |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning spatiotemporal trajectories from manifold-valued longitudinal data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning structured densities via infinite dimensional exponential families |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Linearize Under Uncertainty |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Segment Object Candidates |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Transduce with Unbounded Memory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning visual biases from human imagination |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning with Group Invariant Features: A Kernel Perspective. |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning with Incremental Iterative Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning with Relaxed Supervision |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning with Symmetric Label Noise: The Importance of Being Unhinged |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning with a Wasserstein Loss |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Less is More: Nyström Computational Regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lifelong Learning with Non-i.i.d. Tasks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Lifted Inference Rules With Constraints |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Lifted Symmetry Detection and Breaking for MAP Inference |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Linear Multi-Resource Allocation with Semi-Bandit Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Causal Discovery of Direct Causes and Effects |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Local Expectation Gradients for Black Box Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Smoothness in Variance Reduced Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Logarithmic Time Online Multiclass prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| M-Best-Diverse Labelings for Submodular Energies and Beyond |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| M-Statistic for Kernel Change-Point Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MCMC for Variationally Sparse Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Matrix Completion Under Monotonic Single Index Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix Completion with Noisy Side Information |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Matrix Manifold Optimization for Gaussian Mixtures |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Max-Margin Deep Generative Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Max-Margin Majority Voting for Learning from Crowds |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Measuring Sample Quality with Stein's Method |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimax Time Series Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimum Weight Perfect Matching via Blossom Belief Propagation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model-Based Relative Entropy Stochastic Search |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Monotone k-Submodular Function Maximization with Size Constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Natural Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nearly Optimal Private LASSO |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Adaptive Sequential Monte Carlo |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| No-Regret Learning in Bayesian Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Non-convex Statistical Optimization for Sparse Tensor Graphical Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Elicitation Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On some provably correct cases of variational inference for topic models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Accuracy of Self-Normalized Log-Linear Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Global Linear Convergence of Frank-Wolfe Optimization Variants |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Optimality of Classifier Chain for Multi-label Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Pseudo-Dimension of Nearly Optimal Auctions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the consistency theory of high dimensional variable screening |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On-the-Job Learning with Bayesian Decision Theory |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Online F-Measure Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Gradient Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Learning for Adversaries with Memory: Price of Past Mistakes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with Adversarial Delays |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Learning with Gaussian Payoffs and Side Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Prediction at the Limit of Zero Temperature |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Linear Estimation under Unknown Nonlinear Transform |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Rates for Random Fourier Features |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Ridge Detection using Coverage Risk |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimal Testing for Properties of Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Orthogonal NMF through Subspace Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parallel Correlation Clustering on Big Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parallelizing MCMC with Random Partition Trees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Particle Gibbs for Infinite Hidden Markov Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Path-SGD: Path-Normalized Optimization in Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Planar Ultrametrics for Image Segmentation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pointer Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Policy Evaluation Using the Ω-Return |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Gradient for Coherent Risk Measures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Practical and Optimal LSH for Angular Distance |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Precision-Recall-Gain Curves: PR Analysis Done Right |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Preconditioned Spectral Descent for Deep Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predtron: A Family of Online Algorithms for General Prediction Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Principal Differences Analysis: Interpretable Characterization of Differences between Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Private Graphon Estimation for Sparse Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Probabilistic Line Searches for Stochastic Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Variational Bounds for Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Rate-Agnostic (Causal) Structure Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Recognizing retinal ganglion cells in the dark |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Rectified Factor Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reflection, Refraction, and Hamiltonian Monte Carlo |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regressive Virtual Metric Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regret-Based Pruning in Extensive-Form Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regularization Path of Cross-Validation Error Lower Bounds |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized EM Algorithms: A Unified Framework and Statistical Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking LDA: Moment Matching for Discrete ICA |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revenue Optimization against Strategic Buyers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust PCA with compressed data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Portfolio Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Regression via Hard Thresholding |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust Spectral Inference for Joint Stochastic Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Saliency, Scale and Information: Towards a Unifying Theory |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Complexity Bounds for Iterative Stochastic Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Complexity of Learning Mahalanobis Distance Metrics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sample Efficient Path Integral Control under Uncertainty |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sampling from Probabilistic Submodular Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scalable Inference for Gaussian Process Models with Black-Box Likelihoods |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Scalable Semi-Supervised Aggregation of Classifiers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Secure Multi-party Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Segregated Graphs and Marginals of Chain Graph Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-supervised Learning with Ladder Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-supervised Sequence Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Shepard Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Skip-Thought Vectors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Smooth Interactive Submodular Set Cover |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Smooth and Strong: MAP Inference with Linear Convergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Softstar: Heuristic-Guided Probabilistic Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Space-Time Local Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sparse Local Embeddings for Extreme Multi-label Classification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse PCA via Bipartite Matchings |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse and Low-Rank Tensor Decomposition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spatial Transformer Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Learning of Large Structured HMMs for Comparative Epigenomics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Norm Regularization of Orthonormal Representations for Graph Transduction |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spectral Representations for Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Spherical Random Features for Polynomial Kernels |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical Model Criticism using Kernel Two Sample Tests |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Statistical Topological Data Analysis - A Kernel Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Expectation Propagation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Online Greedy Learning with Semi-bandit Feedbacks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| StopWasting My Gradients: Practical SVRG |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Min-max Hypergraph Partitioning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming, Distributed Variational Inference for Bayesian Nonparametrics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structured Estimation with Atomic Norms: General Bounds and Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Structured Transforms for Small-Footprint Deep Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Submodular Hamming Metrics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Subset Selection by Pareto Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Subspace Clustering with Irrelevant Features via Robust Dantzig Selector |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sum-of-Squares Lower Bounds for Sparse PCA |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Super-Resolution Off the Grid |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Supervised Learning for Dynamical System Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Teaching Machines to Read and Comprehend |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tensorizing Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Testing Closeness With Unequal Sized Samples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Texture Synthesis Using Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Human Kernel |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Pareto Regret Frontier for Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Poisson Gamma Belief Network |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| The Population Posterior and Bayesian Modeling on Streams |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| The Self-Normalized Estimator for Counterfactual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Time-Sensitive Recommendation From Recurrent User Activities |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Top-k Multiclass SVM |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Tractable Learning for Complex Probability Queries |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Training Very Deep Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unified View of Matrix Completion under General Structural Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unlocking neural population non-stationarities using hierarchical dynamics models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Learning by Program Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variance Reduced Stochastic Gradient Descent with Neighbors |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Consensus Monte Carlo |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Dropout and the Local Reparameterization Trick |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Visalogy: Answering Visual Analogy Questions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| When are Kalman-Filter Restless Bandits Indexable? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Where are they looking? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Winner-Take-All Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| b-bit Marginal Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |