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) - 2014

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	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