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) - 2015

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	2015	403	0.35	2.94	88.34%	25.0%

Search Papers

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