Reproducibility Index

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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

Conference on Neural Information Processing Systems (NeurIPS) - 2014

Documentation Rate of Empirical Papers by Reproducibility Variable

Distribution of Empirical Papers by Number of Documented Variables

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025). See Methods for details.	Documentation Score Documentation Score is the average score over the seven reproducibility variables for empirical research papers. See Methods for details.	% 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	2014	411	0.3	2.78	89.54%	22.83%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
(Almost) No Label No Cry	✅	❌	✅	✅	✅	❌	✅	5
A Bayesian model for identifying hierarchically organised states in neural population activity	❌	✅	✅	❌	❌	❌	✅	3
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation	✅	❌	✅	❌	✅	❌	✅	4
A Boosting Framework on Grounds of Online Learning	✅	❌	❌	❌	❌	❌	❌	1
A Complete Variational Tracker	❌	❌	✅	❌	❌	❌	✅	2
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights	✅	❌	❌	❌	❌	❌	✅	2
A Drifting-Games Analysis for Online Learning and Applications to Boosting	✅	❌	❌	❌	❌	❌	❌	1
A Dual Algorithm for Olfactory Computation in the Locust Brain	❌	❌	❌	❌	❌	❌	✅	1
A Filtering Approach to Stochastic Variational Inference	✅	❌	✅	❌	❌	❌	✅	3
A Framework for Testing Identifiability of Bayesian Models of Perception	❌	❌	❌	❌	❌	❌	✅	1
A Latent Source Model for Online Collaborative Filtering	✅	❌	❌	❌	❌	❌	❌	1
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input	❌	✅	✅	✅	❌	❌	❌	3
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations	❌	❌	✅	✅	❌	❌	✅	3
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process	❌	✅	✅	❌	❌	❌	✅	3
A Representation Theory for Ranking Functions	❌	❌	✅	✅	❌	✅	✅	4
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs	✅	❌	❌	✅	❌	❌	✅	3
A Safe Screening Rule for Sparse Logistic Regression	✅	❌	✅	❌	✅	❌	✅	4
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment	❌	❌	❌	✅	✅	❌	✅	3
A Statistical Decision-Theoretic Framework for Social Choice	❌	❌	❌	❌	❌	❌	✅	1
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System	❌	❌	❌	❌	❌	❌	✅	1
A Unified Semantic Embedding: Relating Taxonomies and Attributes	❌	❌	✅	✅	❌	❌	✅	3
A Wild Bootstrap for Degenerate Kernel Tests	❌	✅	❌	❌	❌	❌	✅	2
A framework for studying synaptic plasticity with neural spike train data	❌	❌	❌	❌	❌	❌	✅	1
A provable SVD-based algorithm for learning topics in dominant admixture corpus	✅	✅	✅	❌	❌	❌	✅	4
A statistical model for tensor PCA	✅	❌	❌	❌	❌	❌	✅	2
A* Sampling	✅	❌	❌	❌	❌	❌	❌	1
Accelerated Mini-batch Randomized Block Coordinate Descent Method	✅	❌	✅	❌	❌	❌	✅	3
Active Learning and Best-Response Dynamics	❌	❌	❌	❌	❌	❌	✅	1
Active Regression by Stratification	✅	❌	❌	❌	❌	❌	❌	1
Advances in Learning Bayesian Networks of Bounded Treewidth	✅	❌	✅	❌	❌	✅	✅	4
Algorithm selection by rational metareasoning as a model of human strategy selection	❌	❌	✅	❌	❌	❌	✅	2
Algorithms for CVaR Optimization in MDPs	✅	❌	❌	❌	❌	❌	❌	1
Altitude Training: Strong Bounds for Single-Layer Dropout	❌	❌	✅	❌	❌	❌	❌	1
An Accelerated Proximal Coordinate Gradient Method	✅	❌	✅	❌	❌	❌	✅	3
An Autoencoder Approach to Learning Bilingual Word Representations	❌	✅	✅	✅	❌	❌	✅	4
An Integer Polynomial Programming Based Framework for Lifted MAP Inference	✅	❌	✅	❌	✅	❌	❌	3
Analog Memories in a Balanced Rate-Based Network of E-I Neurons	❌	❌	❌	❌	❌	❌	✅	1
Analysis of Brain States from Multi-Region LFP Time-Series	❌	❌	❌	✅	✅	❌	✅	3
Analysis of Learning from Positive and Unlabeled Data	❌	❌	✅	❌	❌	❌	❌	1
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP	❌	❌	✅	❌	❌	❌	❌	1
Approximating Hierarchical MV-sets for Hierarchical Clustering	❌	❌	✅	✅	❌	❌	✅	3
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations	❌	❌	✅	✅	✅	❌	✅	4
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)	❌	❌	✅	❌	❌	❌	✅	2
Asynchronous Anytime Sequential Monte Carlo	❌	❌	❌	❌	✅	❌	✅	2
Attentional Neural Network: Feature Selection Using Cognitive Feedback	❌	✅	✅	✅	❌	❌	✅	4
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning	❌	❌	✅	❌	❌	❌	✅	2
Augur: Data-Parallel Probabilistic Modeling	✅	❌	✅	❌	✅	❌	✅	4
Automated Variational Inference for Gaussian Process Models	❌	❌	✅	❌	✅	✅	✅	4
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning	❌	❌	✅	✅	❌	❌	✅	3
Bandit Convex Optimization: Towards Tight Bounds	✅	❌	❌	❌	❌	❌	❌	1
Bayes-Adaptive Simulation-based Search with Value Function Approximation	✅	❌	❌	❌	❌	❌	✅	2
Bayesian Inference for Structured Spike and Slab Priors	✅	❌	❌	❌	❌	❌	✅	2
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling	❌	❌	✅	✅	✅	❌	✅	4
Bayesian Sampling Using Stochastic Gradient Thermostats	✅	❌	✅	✅	❌	❌	✅	4
Best-Arm Identification in Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling	❌	✅	✅	❌	❌	❌	✅	3
Beyond Disagreement-Based Agnostic Active Learning	✅	❌	❌	❌	❌	❌	❌	1
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems	❌	❌	❌	❌	✅	✅	✅	3
Biclustering Using Message Passing	✅	❌	❌	❌	❌	❌	✅	2
Blossom Tree Graphical Models	❌	❌	✅	✅	❌	❌	❌	2
Bounded Regret for Finite-Armed Structured Bandits	✅	✅	❌	❌	❌	❌	✅	3
Bregman Alternating Direction Method of Multipliers	❌	✅	❌	❌	✅	❌	✅	3
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs	✅	✅	✅	✅	✅	❌	✅	6
Causal Inference through a Witness Protection Program	✅	✅	❌	❌	❌	❌	✅	3
Causal Strategic Inference in Networked Microfinance Economies	✅	❌	❌	❌	❌	❌	❌	1
Clamping Variables and Approximate Inference	❌	❌	❌	❌	❌	❌	✅	1
Clustered factor analysis of multineuronal spike data	❌	❌	❌	❌	❌	❌	✅	1
Clustering from Labels and Time-Varying Graphs	❌	❌	✅	❌	❌	❌	❌	1
Combinatorial Pure Exploration of Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	❌	1
Communication Efficient Distributed Machine Learning with the Parameter Server	✅	✅	❌	❌	✅	❌	✅	4
Communication-Efficient Distributed Dual Coordinate Ascent	✅	❌	✅	❌	✅	❌	✅	4
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices	❌	❌	✅	❌	❌	❌	✅	2
Computing Nash Equilibria in Generalized Interdependent Security Games	✅	❌	✅	❌	❌	❌	✅	3
Concavity of reweighted Kikuchi approximation	❌	❌	❌	❌	❌	❌	✅	1
Conditional Random Field Autoencoders for Unsupervised Structured Prediction	❌	❌	✅	❌	❌	❌	✅	2
Conditional Swap Regret and Conditional Correlated Equilibrium	❌	❌	❌	❌	❌	❌	❌	0
Cone-Constrained Principal Component Analysis	❌	❌	❌	❌	❌	❌	✅	1
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model	✅	❌	✅	❌	❌	❌	✅	3
Consistency of weighted majority votes	❌	❌	❌	❌	❌	❌	❌	0
Consistent Binary Classification with Generalized Performance Metrics	✅	❌	✅	✅	❌	❌	❌	3
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings	❌	❌	✅	❌	❌	❌	❌	1
Constrained convex minimization via model-based excessive gap	✅	❌	✅	❌	❌	❌	✅	3
Content-based recommendations with Poisson factorization	✅	✅	✅	✅	❌	❌	✅	5
Controlling privacy in recommender systems	❌	❌	✅	❌	❌	❌	✅	2
Convex Deep Learning via Normalized Kernels	✅	❌	✅	✅	❌	❌	✅	4
Convex Optimization Procedure for Clustering: Theoretical Revisit	❌	❌	❌	❌	❌	❌	✅	1
Convolutional Kernel Networks	✅	✅	✅	✅	❌	❌	✅	5
Convolutional Neural Network Architectures for Matching Natural Language Sentences	❌	❌	✅	❌	❌	❌	✅	2
Coresets for k-Segmentation of Streaming Data	✅	❌	✅	❌	✅	❌	✅	4
Covariance shrinkage for autocorrelated data	❌	❌	✅	❌	❌	❌	✅	2
DFacTo: Distributed Factorization of Tensors	✅	✅	✅	❌	✅	✅	❌	5
Decomposing Parameter Estimation Problems	❌	❌	✅	❌	❌	❌	✅	2
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain	✅	✅	❌	✅	❌	❌	✅	4
Decoupled Variational Gaussian Inference	✅	❌	✅	❌	❌	❌	✅	3
Deep Convolutional Neural Network for Image Deconvolution	❌	✅	✅	❌	❌	❌	✅	3
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping	❌	✅	✅	✅	✅	❌	✅	5
Deep Joint Task Learning for Generic Object Extraction	❌	❌	✅	❌	✅	❌	✅	3
Deep Learning Face Representation by Joint Identification-Verification	✅	❌	✅	✅	✅	❌	✅	5
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning	❌	❌	✅	❌	❌	❌	✅	2
Deep Networks with Internal Selective Attention through Feedback Connections	✅	❌	✅	❌	✅	❌	✅	4
Deep Recursive Neural Networks for Compositionality in Language	❌	❌	✅	✅	❌	❌	✅	3
Deep Symmetry Networks	❌	❌	✅	✅	❌	❌	✅	3
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning	✅	❌	✅	✅	❌	❌	✅	4
Dependent nonparametric trees for dynamic hierarchical clustering	❌	❌	❌	❌	❌	❌	✅	1
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network	❌	❌	✅	❌	✅	❌	✅	3
Design Principles of the Hippocampal Cognitive Map	❌	❌	❌	❌	❌	❌	✅	1
Deterministic Symmetric Positive Semidefinite Matrix Completion	✅	❌	❌	❌	❌	❌	✅	2
Difference of Convex Functions Programming for Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Dimensionality Reduction with Subspace Structure Preservation	✅	❌	✅	✅	❌	❌	✅	4
Discovering Structure in High-Dimensional Data Through Correlation Explanation	✅	✅	✅	❌	❌	❌	❌	3
Discovering, Learning and Exploiting Relevance	✅	❌	❌	❌	❌	❌	❌	1
Discrete Graph Hashing	✅	❌	✅	❌	❌	❌	✅	3
Discriminative Metric Learning by Neighborhood Gerrymandering	✅	❌	✅	✅	❌	❌	✅	4
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Distance-Based Network Recovery under Feature Correlation	✅	❌	✅	❌	✅	❌	✅	4
Distributed Balanced Clustering via Mapping Coresets	❌	❌	❌	✅	❌	❌	✅	2
Distributed Bayesian Posterior Sampling via Moment Sharing	❌	❌	✅	❌	✅	❌	✅	3
Distributed Estimation, Information Loss and Exponential Families	❌	❌	✅	✅	❌	❌	✅	3
Distributed Parameter Estimation in Probabilistic Graphical Models	❌	❌	❌	❌	❌	❌	❌	0
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis	✅	❌	✅	❌	✅	❌	✅	4
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models	❌	✅	✅	✅	❌	❌	✅	4
Diverse Randomized Agents Vote to Win	❌	❌	❌	❌	✅	❌	✅	2
Diverse Sequential Subset Selection for Supervised Video Summarization	❌	❌	✅	✅	❌	❌	✅	3
Divide-and-Conquer Learning by Anchoring a Conical Hull	✅	❌	✅	✅	❌	❌	✅	4
Do Convnets Learn Correspondence?	❌	❌	✅	✅	❌	❌	✅	3
Do Deep Nets Really Need to be Deep?	❌	❌	✅	✅	✅	❌	❌	3
Dynamic Rank Factor Model for Text Streams	❌	❌	❌	❌	❌	❌	✅	1
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials	✅	❌	❌	❌	❌	✅	✅	3
Efficient Minimax Signal Detection on Graphs	❌	❌	✅	❌	❌	❌	✅	2
Efficient Minimax Strategies for Square Loss Games	❌	❌	❌	❌	❌	❌	❌	0
Efficient Optimization for Average Precision SVM	✅	❌	✅	✅	❌	❌	❌	3
Efficient Partial Monitoring with Prior Information	✅	❌	✅	❌	❌	❌	✅	3
Efficient Sampling for Learning Sparse Additive Models in High Dimensions	✅	❌	❌	❌	❌	❌	✅	2
Efficient Structured Matrix Rank Minimization	✅	❌	❌	❌	❌	❌	✅	2
Efficient learning by implicit exploration in bandit problems with side observations	✅	❌	❌	❌	❌	❌	❌	1
Elementary Estimators for Graphical Models	❌	❌	❌	❌	❌	❌	✅	1
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors	❌	❌	✅	❌	❌	❌	✅	2
Estimation with Norm Regularization	❌	❌	❌	❌	❌	❌	❌	0
Exact Post Model Selection Inference for Marginal Screening	✅	❌	✅	❌	❌	❌	✅	3
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm	❌	❌	✅	✅	✅	❌	✅	4
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights	✅	✅	✅	✅	❌	❌	✅	5
Expectation-Maximization for Learning Determinantal Point Processes	✅	✅	❌	❌	❌	❌	✅	3
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation	❌	❌	✅	✅	✅	❌	✅	4
Exploiting easy data in online optimization	✅	❌	❌	❌	❌	❌	✅	2
Exponential Concentration of a Density Functional Estimator	❌	❌	❌	❌	❌	❌	❌	0
Extended and Unscented Gaussian Processes	❌	❌	✅	✅	❌	❌	✅	3
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities	✅	❌	✅	❌	❌	❌	✅	3
Extracting Latent Structure From Multiple Interacting Neural Populations	❌	❌	❌	✅	❌	❌	✅	2
Extremal Mechanisms for Local Differential Privacy	❌	❌	❌	❌	❌	❌	❌	0
Extreme bandits	✅	❌	✅	❌	❌	❌	✅	3
Factoring Variations in Natural Images with Deep Gaussian Mixture Models	❌	❌	✅	✅	❌	❌	✅	3
Fairness in Multi-Agent Sequential Decision-Making	✅	❌	❌	❌	✅	✅	✅	4
Fast Kernel Learning for Multidimensional Pattern Extrapolation	❌	❌	❌	✅	✅	❌	❌	2
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning	✅	❌	✅	✅	✅	✅	✅	6
Fast Prediction for Large-Scale Kernel Machines	✅	❌	✅	✅	✅	❌	✅	5
Fast Sampling-Based Inference in Balanced Neuronal Networks	❌	✅	❌	❌	❌	❌	✅	2
Fast Training of Pose Detectors in the Fourier Domain	✅	✅	✅	✅	❌	❌	❌	4
Fast and Robust Least Squares Estimation in Corrupted Linear Models	✅	✅	✅	✅	❌	❌	✅	5
Feature Cross-Substitution in Adversarial Classification	✅	❌	✅	✅	❌	❌	❌	3
Feedback Detection for Live Predictors	❌	❌	❌	✅	❌	❌	✅	2
Feedforward Learning of Mixture Models	❌	❌	✅	❌	❌	❌	✅	2
Finding a sparse vector in a subspace: Linear sparsity using alternating directions	✅	❌	✅	❌	❌	❌	✅	3
Flexible Transfer Learning under Support and Model Shift	❌	❌	✅	✅	✅	❌	❌	3
From MAP to Marginals: Variational Inference in Bayesian Submodular Models	✅	✅	✅	❌	❌	❌	✅	4
From Stochastic Mixability to Fast Rates	❌	❌	❌	❌	❌	❌	❌	0
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation	❌	❌	❌	❌	❌	❌	❌	0
Gaussian Process Volatility Model	✅	✅	❌	✅	❌	❌	✅	4
General Stochastic Networks for Classification	❌	❌	✅	✅	❌	❌	✅	3
General Table Completion using a Bayesian Nonparametric Model	✅	✅	✅	❌	❌	❌	✅	4
Generalized Dantzig Selector: Application to the k-support norm	✅	❌	❌	❌	❌	❌	✅	2
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion	✅	❌	✅	❌	❌	❌	✅	3
Generalized Unsupervised Manifold Alignment	✅	❌	✅	❌	❌	❌	✅	3
Generative Adversarial Nets	✅	✅	✅	❌	❌	❌	✅	4
Global Belief Recursive Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Global Sensitivity Analysis for MAP Inference in Graphical Models	❌	❌	✅	❌	❌	❌	✅	2
Graph Clustering With Missing Data: Convex Algorithms and Analysis	❌	❌	✅	❌	❌	❌	✅	2
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data	❌	❌	❌	❌	❌	❌	❌	0
Greedy Subspace Clustering	✅	❌	✅	❌	❌	❌	✅	3
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction	❌	✅	✅	❌	❌	❌	✅	3
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models	❌	❌	✅	✅	❌	❌	✅	3
Hardness of parameter estimation in graphical models	❌	❌	❌	❌	❌	❌	❌	0
How hard is my MDP?" The distribution-norm to the rescue"	❌	❌	✅	❌	❌	❌	✅	2
How transferable are features in deep neural networks?	❌	✅	✅	❌	❌	❌	❌	2
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization	✅	❌	✅	❌	❌	❌	✅	3
Improved Distributed Principal Component Analysis	✅	❌	✅	❌	❌	❌	✅	3
Improved Multimodal Deep Learning with Variation of Information	❌	❌	✅	✅	❌	❌	✅	3
Incremental Clustering: The Case for Extra Clusters	✅	❌	❌	❌	❌	❌	❌	1
Incremental Local Gaussian Regression	✅	❌	❌	✅	✅	❌	✅	4
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers	✅	✅	✅	❌	❌	❌	✅	4
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit	❌	❌	❌	❌	❌	❌	✅	1
Inferring synaptic conductances from spike trains with a biophysically inspired point process model	❌	❌	❌	❌	❌	❌	✅	1
Information-based learning by agents in unbounded state spaces	❌	❌	✅	❌	❌	❌	✅	2
Iterative Neural Autoregressive Distribution Estimator NADE-k	❌	✅	✅	✅	❌	❌	✅	4
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation	❌	❌	✅	✅	✅	❌	❌	3
Just-In-Time Learning for Fast and Flexible Inference	❌	❌	✅	✅	❌	✅	✅	4
Kernel Mean Estimation via Spectral Filtering	✅	❌	✅	✅	❌	❌	✅	4
LSDA: Large Scale Detection through Adaptation	❌	✅	✅	✅	❌	❌	❌	3
Large-Margin Convex Polytope Machine	✅	❌	✅	✅	✅	❌	✅	5
Large-scale L-BFGS using MapReduce	✅	❌	❌	❌	❌	❌	✅	2
Latent Support Measure Machines for Bag-of-Words Data Classification	❌	❌	✅	❌	❌	❌	✅	2
Learning Chordal Markov Networks by Dynamic Programming	❌	✅	✅	❌	❌	❌	✅	3
Learning Deep Features for Scene Recognition using Places Database	❌	✅	✅	✅	✅	❌	✅	5
Learning Distributed Representations for Structured Output Prediction	✅	❌	✅	✅	❌	❌	✅	4
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm	✅	❌	✅	✅	❌	❌	✅	4
Learning Generative Models with Visual Attention	❌	❌	✅	✅	❌	❌	❌	2
Learning Mixed Multinomial Logit Model from Ordinal Data	✅	❌	❌	❌	❌	❌	❌	1
Learning Mixtures of Ranking Models	✅	❌	❌	❌	✅	❌	✅	3
Learning Mixtures of Submodular Functions for Image Collection Summarization	✅	❌	❌	✅	❌	❌	✅	3
Learning Multiple Tasks in Parallel with a Shared Annotator	✅	❌	✅	❌	❌	❌	✅	3
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics	✅	❌	❌	❌	❌	❌	✅	2
Learning Optimal Commitment to Overcome Insecurity	✅	❌	❌	❌	❌	❌	❌	1
Learning Shuffle Ideals Under Restricted Distributions	❌	❌	✅	❌	❌	❌	❌	1
Learning Time-Varying Coverage Functions	✅	❌	✅	✅	❌	❌	✅	4
Learning a Concept Hierarchy from Multi-labeled Documents	✅	❌	✅	✅	❌	❌	✅	4
Learning convolution filters for inverse covariance estimation of neural network connectivity	✅	❌	✅	✅	✅	❌	✅	5
Learning on graphs using Orthonormal Representation is Statistically Consistent	✅	❌	✅	❌	❌	❌	❌	2
Learning the Learning Rate for Prediction with Expert Advice	✅	❌	❌	❌	❌	❌	❌	1
Learning to Discover Efficient Mathematical Identities	❌	✅	✅	❌	✅	❌	✅	4
Learning to Optimize via Information-Directed Sampling	✅	❌	❌	❌	❌	❌	✅	2
Learning to Search in Branch and Bound Algorithms	✅	❌	✅	✅	❌	✅	✅	5
Learning with Fredholm Kernels	❌	❌	✅	✅	❌	❌	❌	2
Learning with Pseudo-Ensembles	❌	✅	✅	❌	❌	❌	✅	3
Local Decorrelation For Improved Pedestrian Detection	❌	❌	✅	✅	❌	❌	✅	3
Local Linear Convergence of Forward--Backward under Partial Smoothness	❌	❌	❌	❌	❌	❌	✅	1
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology	❌	✅	✅	❌	❌	✅	✅	4
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces	❌	❌	✅	✅	❌	❌	✅	3
Low Rank Approximation Lower Bounds in Row-Update Streams	❌	❌	❌	❌	❌	❌	❌	0
Low-Rank Time-Frequency Synthesis	✅	❌	✅	❌	❌	❌	✅	3
Low-dimensional models of neural population activity in sensory cortical circuits	❌	❌	✅	✅	✅	❌	✅	4
Magnitude-sensitive preference formation`	❌	❌	❌	❌	❌	❌	✅	1
Making Pairwise Binary Graphical Models Attractive	❌	❌	❌	❌	❌	❌	✅	1
Median Selection Subset Aggregation for Parallel Inference	✅	❌	✅	❌	❌	❌	✅	3
Message Passing Inference for Large Scale Graphical Models with High Order Potentials	✅	❌	✅	❌	❌	❌	✅	3
Metric Learning for Temporal Sequence Alignment	✅	❌	✅	❌	❌	❌	❌	2
Mind the Nuisance: Gaussian Process Classification using Privileged Noise	❌	✅	✅	✅	❌	❌	✅	4
Minimax-optimal Inference from Partial Rankings	❌	❌	❌	❌	❌	❌	✅	1
Mode Estimation for High Dimensional Discrete Tree Graphical Models	✅	❌	✅	❌	❌	❌	✅	3
Model-based Reinforcement Learning and the Eluder Dimension	✅	❌	❌	❌	❌	❌	❌	1
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""	❌	❌	✅	✅	❌	❌	✅	3
Mondrian Forests: Efficient Online Random Forests	✅	✅	✅	✅	❌	❌	✅	5
Multi-Class Deep Boosting	✅	❌	✅	✅	❌	❌	✅	4
Multi-Resolution Cascades for Multiclass Object Detection	❌	❌	✅	❌	❌	❌	✅	2
Multi-Scale Spectral Decomposition of Massive Graphs	✅	❌	✅	✅	❌	❌	❌	3
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition	✅	❌	❌	❌	✅	❌	✅	3
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations	❌	✅	✅	❌	❌	❌	✅	3
Multi-scale Graphical Models for Spatio-Temporal Processes	❌	❌	❌	✅	❌	❌	✅	2
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks	❌	❌	✅	✅	❌	❌	✅	3
Multiscale Fields of Patterns	❌	❌	✅	❌	❌	❌	✅	2
Multitask learning meets tensor factorization: task imputation via convex optimization	❌	❌	✅	✅	❌	❌	✅	3
Multivariate Regression with Calibration	❌	❌	✅	✅	✅	❌	✅	4
Multivariate f-divergence Estimation With Confidence	✅	❌	✅	✅	❌	❌	✅	4
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms	✅	❌	❌	❌	❌	❌	❌	1
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures	✅	❌	❌	❌	❌	❌	❌	1
Near-optimal Reinforcement Learning in Factored MDPs	✅	❌	❌	❌	❌	❌	❌	1
Near-optimal sample compression for nearest neighbors	✅	❌	✅	❌	❌	❌	❌	2
Neural Word Embedding as Implicit Matrix Factorization	❌	✅	✅	❌	❌	❌	✅	3
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks	❌	❌	❌	❌	❌	❌	✅	1
New Rules for Domain Independent Lifted MAP Inference	✅	❌	✅	❌	✅	✅	✅	5
Non-convex Robust PCA	✅	❌	✅	❌	❌	❌	✅	3
Nonparametric Bayesian inference on multivariate exponential families	❌	❌	✅	❌	❌	❌	✅	2
Object Localization based on Structural SVM using Privileged Information	✅	❌	✅	✅	❌	❌	✅	4
On Communication Cost of Distributed Statistical Estimation and Dimensionality	✅	❌	❌	❌	❌	❌	❌	1
On Integrated Clustering and Outlier Detection	✅	❌	✅	❌	❌	❌	✅	3
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation	✅	❌	❌	❌	❌	❌	✅	2
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning	✅	❌	✅	❌	✅	✅	✅	5
On Multiplicative Multitask Feature Learning	✅	❌	✅	✅	❌	❌	❌	3
On Prior Distributions and Approximate Inference for Structured Variables	❌	❌	✅	✅	❌	❌	✅	3
On Sparse Gaussian Chain Graph Models	❌	❌	✅	✅	❌	❌	✅	3
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification	❌	❌	✅	❌	❌	❌	✅	2
On the Computational Efficiency of Training Neural Networks	✅	❌	❌	✅	❌	❌	❌	2
On the Convergence Rate of Decomposable Submodular Function Minimization	❌	❌	❌	❌	❌	❌	❌	0
On the Information Theoretic Limits of Learning Ising Models	❌	❌	❌	❌	❌	❌	❌	0
On the Number of Linear Regions of Deep Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures	✅	❌	✅	❌	❌	❌	❌	2
On the relations of LFPs & Neural Spike Trains	❌	❌	❌	✅	❌	❌	✅	2
Online Decision-Making in General Combinatorial Spaces	✅	❌	❌	❌	❌	❌	❌	1
Online Optimization for Max-Norm Regularization	✅	❌	❌	❌	❌	❌	✅	2
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions	✅	❌	✅	✅	❌	❌	✅	4
Online combinatorial optimization with stochastic decision sets and adversarial losses	❌	❌	❌	❌	❌	❌	✅	1
Optimal Neural Codes for Control and Estimation	❌	❌	❌	❌	❌	❌	✅	1
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers	✅	❌	❌	❌	❌	❌	✅	2
Optimal Teaching for Limited-Capacity Human Learners	❌	❌	✅	❌	❌	❌	✅	2
Optimal decision-making with time-varying evidence reliability	❌	❌	❌	❌	❌	❌	✅	1
Optimal prior-dependent neural population codes under shared input noise	❌	❌	❌	❌	❌	❌	❌	0
Optimal rates for k-NN density and mode estimation	✅	❌	❌	❌	❌	❌	❌	1
Optimistic Planning in Markov Decision Processes Using a Generative Model	✅	❌	❌	❌	❌	❌	❌	1
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection	✅	❌	✅	❌	❌	✅	✅	4
Optimizing Energy Production Using Policy Search and Predictive State Representations	✅	❌	❌	❌	❌	❌	✅	2
Optimizing F-Measures by Cost-Sensitive Classification	❌	❌	✅	✅	❌	❌	✅	3
Orbit Regularization	✅	❌	❌	❌	❌	❌	✅	2
PAC-Bayesian AUC classification and scoring	✅	❌	✅	✅	❌	❌	✅	4
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising	❌	❌	✅	❌	✅	❌	✅	3
Parallel Direction Method of Multipliers	❌	❌	✅	❌	❌	❌	✅	2
Parallel Double Greedy Submodular Maximization	✅	❌	✅	❌	✅	❌	✅	4
Parallel Feature Selection Inspired by Group Testing	❌	❌	✅	✅	✅	❌	✅	4
Parallel Sampling of HDPs using Sub-Cluster Splits	✅	✅	✅	❌	❌	❌	❌	3
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Partition-wise Linear Models	❌	❌	✅	✅	❌	❌	✅	3
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision	❌	❌	❌	❌	❌	❌	❌	0
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data	❌	❌	✅	❌	✅	❌	✅	3
Positive Curvature and Hamiltonian Monte Carlo	❌	❌	❌	❌	❌	❌	✅	1
Pre-training of Recurrent Neural Networks via Linear Autoencoders	✅	❌	✅	✅	✅	❌	✅	5
Predicting Useful Neighborhoods for Lazy Local Learning	❌	❌	✅	❌	❌	❌	✅	2
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	✅	✅	✅	❌	❌	❌	✅	4
Probabilistic Differential Dynamic Programming	✅	❌	❌	❌	❌	❌	✅	2
Probabilistic ODE Solvers with Runge-Kutta Means	❌	❌	❌	❌	❌	❌	✅	1
Probabilistic low-rank matrix completion on finite alphabets	✅	❌	✅	✅	✅	❌	✅	5
Projecting Markov Random Field Parameters for Fast Mixing	✅	❌	❌	❌	✅	❌	✅	3
Projective dictionary pair learning for pattern classification	✅	❌	✅	✅	✅	❌	✅	5
Provable Submodular Minimization using Wolfe's Algorithm	✅	❌	✅	❌	❌	❌	✅	3
Provable Tensor Factorization with Missing Data	✅	✅	❌	❌	❌	❌	❌	2
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators	✅	❌	✅	✅	✅	❌	✅	5
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models	✅	❌	✅	❌	❌	❌	✅	3
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls	✅	❌	❌	❌	❌	❌	✅	2
Quantized Kernel Learning for Feature Matching	❌	✅	✅	❌	❌	❌	❌	2
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning	✅	❌	✅	❌	❌	✅	✅	4
Randomized Experimental Design for Causal Graph Discovery	❌	❌	❌	❌	❌	❌	✅	1
Ranking via Robust Binary Classification	✅	❌	✅	✅	❌	❌	❌	3
Rates of Convergence for Nearest Neighbor Classification	❌	❌	❌	❌	❌	❌	❌	0
Real-Time Decoding of an Integrate and Fire Encoder	✅	❌	❌	❌	❌	✅	✅	3
Recovery of Coherent Data via Low-Rank Dictionary Pursuit	✅	❌	✅	❌	❌	❌	✅	3
Recurrent Models of Visual Attention	❌	❌	✅	❌	❌	❌	✅	2
Recursive Context Propagation Network for Semantic Scene Labeling	❌	❌	✅	✅	✅	❌	✅	4
Recursive Inversion Models for Permutations	✅	❌	✅	❌	❌	❌	✅	3
Reducing the Rank in Relational Factorization Models by Including Observable Patterns	❌	❌	✅	✅	✅	❌	✅	4
Repeated Contextual Auctions with Strategic Buyers	✅	❌	❌	❌	❌	❌	❌	1
Reputation-based Worker Filtering in Crowdsourcing	✅	❌	✅	❌	❌	❌	✅	3
Restricted Boltzmann machines modeling human choice	❌	❌	✅	❌	✅	❌	✅	3
Robust Bayesian Max-Margin Clustering	❌	❌	✅	✅	❌	❌	✅	3
Robust Classification Under Sample Selection Bias	✅	❌	✅	❌	❌	✅	❌	3
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space	❌	❌	❌	✅	❌	❌	✅	2
Robust Logistic Regression and Classification	✅	❌	❌	❌	❌	❌	✅	2
Robust Tensor Decomposition with Gross Corruption	❌	❌	❌	❌	❌	❌	❌	0
Rounding-based Moves for Metric Labeling	✅	❌	❌	❌	❌	❌	❌	1
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives	✅	✅	✅	❌	❌	❌	✅	4
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature	❌	❌	✅	❌	❌	❌	✅	2
Scalable Inference for Neuronal Connectivity from Calcium Imaging	❌	❌	❌	❌	❌	❌	✅	1
Scalable Kernel Methods via Doubly Stochastic Gradients	✅	❌	✅	❌	✅	❌	✅	4
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices	❌	✅	✅	❌	✅	❌	❌	3
Scalable Non-linear Learning with Adaptive Polynomial Expansions	✅	✅	✅	❌	❌	❌	✅	4
Scale Adaptive Blind Deblurring	❌	❌	✅	❌	❌	❌	✅	2
Scaling-up Importance Sampling for Markov Logic Networks	✅	❌	✅	❌	✅	❌	✅	4
Searching for Higgs Boson Decay Modes with Deep Learning	❌	❌	❌	✅	✅	❌	✅	3
Self-Adaptable Templates for Feature Coding	✅	❌	✅	✅	❌	❌	✅	4
Self-Paced Learning with Diversity	✅	✅	✅	✅	❌	❌	✅	5
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models	✅	❌	✅	❌	❌	❌	✅	3
Semi-supervised Learning with Deep Generative Models	✅	✅	✅	❌	❌	❌	✅	4
Sensory Integration and Density Estimation	❌	❌	❌	❌	❌	❌	❌	0
Sequence to Sequence Learning with Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
Sequential Monte Carlo for Graphical Models	✅	✅	✅	❌	❌	❌	✅	4
SerialRank: Spectral Ranking using Seriation	✅	❌	❌	❌	❌	❌	❌	1
Shape and Illumination from Shading using the Generic Viewpoint Assumption	❌	✅	✅	❌	❌	❌	✅	3
Shaping Social Activity by Incentivizing Users	✅	❌	✅	❌	❌	❌	✅	3
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation	❌	❌	✅	❌	❌	✅	✅	3
Simple MAP Inference via Low-Rank Relaxations	✅	❌	✅	❌	❌	❌	✅	3
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning	✅	❌	✅	✅	❌	❌	✅	4
Smoothed Gradients for Stochastic Variational Inference	✅	❌	✅	❌	❌	❌	✅	3
Sparse Bayesian structure learning with “dependent relevance determination” priors	❌	❌	✅	✅	❌	❌	❌	2
Sparse Multi-Task Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Sparse PCA via Covariance Thresholding	✅	❌	❌	✅	❌	❌	✅	3
Sparse PCA with Oracle Property	✅	❌	❌	✅	❌	❌	✅	3
Sparse Polynomial Learning and Graph Sketching	✅	❌	✅	❌	✅	❌	✅	4
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space	✅	❌	✅	❌	❌	❌	✅	3
Sparse Space-Time Deconvolution for Calcium Image Analysis	❌	❌	❌	❌	❌	❌	✅	1
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Spectral Clustering of graphs with the Bethe Hessian	❌	✅	✅	❌	❌	❌	❌	2
Spectral Learning of Mixture of Hidden Markov Models	✅	❌	✅	❌	❌	❌	✅	3
Spectral Methods for Indian Buffet Process Inference	✅	❌	✅	❌	❌	❌	✅	3
Spectral Methods for Supervised Topic Models	✅	❌	✅	✅	❌	❌	✅	4
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing	✅	❌	✅	❌	❌	❌	✅	3
Spectral k-Support Norm Regularization	✅	❌	✅	✅	❌	❌	✅	4
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm	❌	❌	❌	❌	❌	❌	❌	0
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards	✅	❌	❌	❌	❌	❌	❌	1
Stochastic Network Design in Bidirected Trees	❌	❌	✅	❌	❌	❌	✅	2
Stochastic Proximal Gradient Descent with Acceleration Techniques	✅	❌	✅	❌	❌	❌	✅	3
Stochastic variational inference for hidden Markov models	✅	❌	✅	✅	❌	❌	✅	4
Streaming, Memory Limited Algorithms for Community Detection	✅	❌	❌	❌	❌	❌	❌	1
Structure Regularization for Structured Prediction	✅	✅	✅	✅	❌	❌	✅	5
Structure learning of antiferromagnetic Ising models	✅	❌	❌	❌	❌	❌	❌	1
Submodular Attribute Selection for Action Recognition in Video	✅	❌	✅	✅	❌	❌	✅	4
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets	❌	❌	✅	✅	❌	❌	✅	3
Subspace Embeddings for the Polynomial Kernel	✅	❌	❌	❌	❌	❌	✅	2
Testing Unfaithful Gaussian Graphical Models	✅	❌	❌	❌	❌	❌	❌	1
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification	❌	❌	✅	❌	❌	❌	✅	2
The Blinded Bandit: Learning with Adaptive Feedback	✅	❌	❌	❌	❌	❌	❌	1
The Infinite Mixture of Infinite Gaussian Mixtures	❌	✅	✅	❌	✅	❌	✅	4
The Large Margin Mechanism for Differentially Private Maximization	✅	❌	❌	❌	❌	❌	❌	1
The Noisy Power Method: A Meta Algorithm with Applications	✅	❌	❌	❌	❌	❌	❌	1
The limits of squared Euclidean distance regularization	❌	❌	❌	❌	❌	❌	❌	0
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology	❌	❌	❌	❌	❌	❌	✅	1
Tight Continuous Relaxation of the Balanced k-Cut Problem	❌	❌	✅	❌	❌	❌	✅	2
Tight convex relaxations for sparse matrix factorization	❌	❌	❌	✅	❌	❌	❌	1
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time	✅	❌	❌	❌	❌	❌	✅	2
Time--Data Tradeoffs by Aggressive Smoothing	✅	❌	❌	❌	❌	❌	✅	2
Top Rank Optimization in Linear Time	✅	❌	✅	✅	✅	✅	✅	6
Transportability from Multiple Environments with Limited Experiments: Completeness Results	✅	❌	❌	❌	❌	❌	❌	1
Tree-structured Gaussian Process Approximations	❌	✅	✅	❌	❌	❌	❌	2
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets	❌	❌	✅	❌	❌	❌	✅	2
Two-Stream Convolutional Networks for Action Recognition in Videos	❌	❌	✅	❌	✅	❌	✅	3
Universal Option Models	❌	❌	❌	❌	❌	❌	✅	1
Unsupervised Deep Haar Scattering on Graphs	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Transcription of Piano Music	❌	❌	✅	✅	❌	❌	✅	3
Unsupervised learning of an efficient short-term memory network	❌	❌	❌	❌	❌	❌	✅	1
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings	❌	❌	✅	✅	✅	❌	✅	4
Variational Gaussian Process State-Space Models	✅	❌	✅	❌	❌	❌	✅	3
Weakly-supervised Discovery of Visual Pattern Configurations	❌	❌	✅	❌	❌	❌	✅	2
Weighted importance sampling for off-policy learning with linear function approximation	❌	❌	❌	❌	❌	❌	✅	1
Zero-shot recognition with unreliable attributes	✅	❌	✅	✅	❌	❌	✅	4
Zeta Hull Pursuits: Learning Nonconvex Data Hulls	✅	❌	✅	❌	❌	❌	✅	3
large scale canonical correlation analysis with iterative least squares	✅	❌	✅	❌	❌	❌	✅	3