reproducibilityindex.ai

Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].

Conference on Neural Information Processing Systems (NeurIPS) - 2016

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025)	Documentation Score Global mean is the average score over the seven reproducibility variables for empirical research papers.	% Empirical Percentage of papers that are empirical research vs theoretical research	% Industry Percentage of empirical research papers with at least one author from Industry	Website
NeurIPS	2016	569	0.35	2.88	86.99%	29.09%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
A Bandit Framework for Strategic Regression	✅	❌	❌	❌	❌	❌	❌	1
A Bayesian method for reducing bias in neural representational similarity analysis	❌	✅	❌	❌	❌	❌	✅	2
A Bio-inspired Redundant Sensing Architecture	❌	❌	❌	❌	❌	❌	✅	1
A Communication-Efficient Parallel Algorithm for Decision Tree	✅	✅	✅	❌	✅	❌	✅	5
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order	✅	❌	✅	✅	✅	❌	✅	5
A Consistent Regularization Approach for Structured Prediction	❌	❌	✅	✅	❌	❌	❌	2
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++	✅	❌	❌	❌	❌	❌	❌	1
A Credit Assignment Compiler for Joint Prediction	✅	❌	✅	❌	❌	❌	❌	2
A Locally Adaptive Normal Distribution	✅	❌	✅	❌	❌	❌	✅	3
A Minimax Approach to Supervised Learning	❌	❌	✅	❌	❌	❌	✅	2
A Multi-Batch L-BFGS Method for Machine Learning	✅	❌	✅	❌	❌	❌	✅	3
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization	✅	❌	❌	❌	❌	❌	✅	2
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing	✅	✅	❌	❌	❌	❌	❌	2
A Non-generative Framework and Convex Relaxations for Unsupervised Learning	✅	❌	❌	❌	❌	❌	❌	1
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics	✅	❌	❌	❌	❌	❌	✅	2
A Powerful Generative Model Using Random Weights for the Deep Image Representation	❌	✅	✅	❌	❌	❌	✅	3
A Probabilistic Framework for Deep Learning	✅	❌	✅	✅	❌	❌	❌	3
A Probabilistic Model of Social Decision Making based on Reward Maximization	❌	❌	❌	❌	❌	❌	✅	1
A Probabilistic Programming Approach To Probabilistic Data Analysis	✅	❌	✅	❌	❌	❌	✅	3
A Pseudo-Bayesian Algorithm for Robust PCA	❌	❌	✅	❌	❌	❌	❌	1
A Simple Practical Accelerated Method for Finite Sums	✅	✅	✅	❌	❌	❌	✅	4
A Sparse Interactive Model for Matrix Completion with Side Information	✅	❌	✅	✅	❌	❌	✅	4
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
A Unified Approach for Learning the Parameters of Sum-Product Networks	❌	❌	❌	❌	❌	❌	✅	1
A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control	❌	❌	❌	❌	❌	❌	✅	1
A posteriori error bounds for joint matrix decomposition problems	❌	❌	❌	❌	❌	❌	✅	1
A primal-dual method for conic constrained distributed optimization problems	✅	❌	❌	❌	❌	❌	✅	2
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification	✅	❌	✅	✅	❌	❌	✅	4
A scaled Bregman theorem with applications	❌	❌	❌	❌	❌	❌	❌	0
A state-space model of cross-region dynamic connectivity in MEG/EEG	❌	✅	❌	❌	❌	❌	✅	2
Accelerating Stochastic Composition Optimization	✅	❌	✅	❌	❌	❌	✅	3
Achieving budget-optimality with adaptive schemes in crowdsourcing	✅	❌	❌	❌	❌	❌	✅	2
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation	✅	❌	❌	❌	❌	❌	❌	1
Active Learning from Imperfect Labelers	✅	❌	❌	❌	❌	❌	❌	1
Active Learning with Oracle Epiphany	✅	❌	✅	❌	❌	❌	❌	2
Active Nearest-Neighbor Learning in Metric Spaces	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Averaging in Accelerated Descent Dynamics	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Concentration Inequalities for Sequential Decision Problems	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint	✅	❌	✅	❌	❌	❌	✅	3
Adaptive Neural Compilation	✅	✅	❌	❌	❌	❌	✅	3
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy	✅	❌	✅	❌	❌	❌	✅	3
Adaptive Skills Adaptive Partitions (ASAP)	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Smoothed Online Multi-Task Learning	✅	❌	✅	✅	❌	❌	✅	4
Adaptive optimal training of animal behavior	❌	❌	❌	❌	❌	❌	✅	1
Adversarial Multiclass Classification: A Risk Minimization Perspective	✅	❌	✅	✅	❌	❌	✅	4
Agnostic Estimation for Misspecified Phase Retrieval Models	✅	❌	❌	✅	❌	❌	✅	3
Algorithms and matching lower bounds for approximately-convex optimization	✅	❌	❌	❌	❌	❌	❌	1
An Architecture for Deep, Hierarchical Generative Models	❌	✅	✅	❌	❌	❌	❌	2
An Efficient Streaming Algorithm for the Submodular Cover Problem	✅	❌	✅	❌	❌	❌	✅	3
An Online Sequence-to-Sequence Model Using Partial Conditioning	❌	❌	✅	✅	❌	❌	✅	3
An algorithm for L1 nearest neighbor search via monotonic embedding	✅	❌	✅	✅	❌	❌	✅	4
An ensemble diversity approach to supervised binary hashing	❌	❌	✅	❌	❌	❌	✅	2
An equivalence between high dimensional Bayes optimal inference and M-estimation	❌	❌	❌	❌	❌	❌	✅	1
An urn model for majority voting in classification ensembles	❌	✅	✅	✅	❌	❌	✅	4
Ancestral Causal Inference	❌	✅	✅	❌	✅	✅	✅	5
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm	✅	❌	✅	❌	❌	❌	❌	2
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods	❌	❌	❌	❌	❌	❌	❌	0
Architectural Complexity Measures of Recurrent Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Assortment Optimization Under the Mallows model	✅	❌	❌	❌	✅	✅	✅	4
Asynchronous Parallel Greedy Coordinate Descent	✅	❌	✅	✅	✅	❌	✅	5
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models	❌	❌	✅	❌	❌	❌	❌	1
Automated scalable segmentation of neurons from multispectral images	✅	❌	✅	❌	❌	❌	❌	2
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks	❌	✅	❌	✅	✅	❌	✅	4
Average-case hardness of RIP certification	❌	❌	❌	❌	❌	❌	❌	0
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction	✅	❌	❌	❌	❌	❌	❌	1
Backprop KF: Learning Discriminative Deterministic State Estimators	❌	❌	✅	✅	❌	❌	✅	3
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition	❌	❌	❌	❌	❌	❌	❌	0
Barzilai-Borwein Step Size for Stochastic Gradient Descent	✅	❌	✅	❌	❌	❌	✅	3
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes	✅	❌	✅	❌	❌	❌	✅	3
Bayesian Intermittent Demand Forecasting for Large Inventories	❌	❌	✅	✅	✅	❌	✅	4
Bayesian Optimization for Probabilistic Programs	❌	✅	❌	❌	❌	❌	✅	2
Bayesian Optimization with Robust Bayesian Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach	❌	❌	❌	❌	❌	❌	✅	1
Bayesian latent structure discovery from multi-neuron recordings	❌	✅	✅	❌	✅	❌	✅	4
Bayesian optimization for automated model selection	❌	❌	✅	❌	❌	❌	✅	2
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian	✅	✅	❌	❌	❌	✅	✅	4
Beyond Exchangeability: The Chinese Voting Process	❌	❌	✅	❌	❌	❌	❌	1
Bi-Objective Online Matching and Submodular Allocations	✅	❌	❌	❌	❌	❌	❌	1
Binarized Neural Networks	✅	✅	✅	✅	✅	✅	❌	6
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning	✅	❌	❌	❌	❌	❌	❌	1
Blind Attacks on Machine Learners	❌	❌	❌	❌	❌	❌	❌	0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering	❌	❌	✅	❌	❌	❌	✅	2
Boosting with Abstention	✅	❌	✅	✅	❌	❌	✅	4
Bootstrap Model Aggregation for Distributed Statistical Learning	✅	❌	❌	❌	❌	❌	✅	2
Brains on Beats	❌	❌	✅	✅	❌	❌	✅	3
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation	❌	❌	❌	❌	❌	❌	✅	1
Budgeted stream-based active learning via adaptive submodular maximization	✅	❌	✅	❌	❌	❌	✅	3
CMA-ES with Optimal Covariance Update and Storage Complexity	✅	✅	❌	❌	❌	❌	✅	3
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain	❌	❌	✅	✅	✅	❌	✅	4
CRF-CNN: Modeling Structured Information in Human Pose Estimation	✅	❌	✅	❌	❌	❌	✅	3
Can Active Memory Replace Attention?	❌	✅	✅	✅	❌	❌	✅	4
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?	✅	✅	❌	❌	❌	❌	✅	3
Catching heuristics are optimal control policies	❌	❌	❌	❌	❌	❌	✅	1
Causal Bandits: Learning Good Interventions via Causal Inference	✅	✅	❌	❌	❌	❌	✅	3
Causal meets Submodular: Subset Selection with Directed Information	✅	❌	❌	❌	❌	❌	✅	2
CliqueCNN: Deep Unsupervised Exemplar Learning	❌	✅	✅	❌	✅	❌	✅	4
Clustering Signed Networks with the Geometric Mean of Laplacians	✅	❌	✅	❌	❌	❌	✅	3
Clustering with Bregman Divergences: an Asymptotic Analysis	❌	❌	❌	❌	❌	❌	✅	1
Clustering with Same-Cluster Queries	✅	❌	❌	❌	❌	❌	❌	1
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions	❌	❌	✅	✅	❌	❌	✅	3
Coin Betting and Parameter-Free Online Learning	✅	❌	✅	❌	❌	❌	❌	2
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks	❌	❌	✅	✅	❌	❌	✅	3
Combinatorial Energy Learning for Image Segmentation	❌	❌	✅	✅	❌	❌	✅	3
Combinatorial Multi-Armed Bandit with General Reward Functions	✅	❌	❌	❌	❌	❌	❌	1
Combinatorial semi-bandit with known covariance	✅	❌	❌	❌	❌	❌	❌	1
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning	❌	❌	❌	❌	❌	❌	❌	0
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation	❌	❌	✅	✅	✅	✅	✅	5
Communication-Optimal Distributed Clustering	❌	❌	❌	❌	✅	❌	✅	2
Community Detection on Evolving Graphs	❌	❌	❌	❌	❌	❌	✅	1
Completely random measures for modelling block-structured sparse networks	❌	✅	❌	❌	❌	❌	✅	2
Composing graphical models with neural networks for structured representations and fast inference	✅	✅	✅	❌	❌	❌	✅	4
Computational and Statistical Tradeoffs in Learning to Rank	❌	❌	✅	❌	❌	❌	✅	2
Computing and maximizing influence in linear threshold and triggering models	❌	❌	❌	❌	❌	❌	✅	1
Conditional Generative Moment-Matching Networks	✅	❌	✅	✅	❌	❌	✅	4
Conditional Image Generation with PixelCNN Decoders	❌	❌	✅	❌	❌	❌	✅	2
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making	❌	❌	❌	✅	❌	❌	✅	2
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random	❌	❌	✅	❌	❌	❌	❌	1
Consistent Kernel Mean Estimation for Functions of Random Variables	❌	❌	❌	❌	❌	❌	✅	1
Constraints Based Convex Belief Propagation	✅	❌	✅	❌	❌	❌	✅	3
Contextual semibandits via supervised learning oracles	✅	✅	✅	❌	❌	❌	✅	4
Convergence guarantees for kernel-based quadrature rules in misspecified settings	❌	❌	❌	❌	❌	❌	✅	1
Convex Two-Layer Modeling with Latent Structure	❌	❌	✅	✅	❌	❌	✅	3
Convolutional Neural Fabrics	❌	✅	✅	✅	❌	❌	✅	4
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering	❌	✅	✅	✅	❌	❌	✅	4
Cooperative Graphical Models	✅	✅	❌	❌	❌	❌	✅	3
Cooperative Inverse Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Coordinate-wise Power Method	✅	❌	✅	❌	✅	❌	✅	4
Coresets for Scalable Bayesian Logistic Regression	✅	✅	✅	❌	❌	❌	✅	4
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated	✅	❌	✅	❌	❌	❌	✅	3
Coupled Generative Adversarial Networks	❌	✅	✅	❌	❌	❌	✅	3
Crowdsourced Clustering: Querying Edges vs Triangles	❌	❌	✅	❌	❌	❌	✅	2
Cyclades: Conflict-free Asynchronous Machine Learning	✅	✅	✅	❌	✅	❌	✅	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	❌	❌	✅	✅	❌	❌	✅	3
Tree-Structured Reinforcement Learning for Sequential Object Localization	❌	❌	✅	❌	✅	❌	✅	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