| A Junction Tree Framework for Undirected Graphical Model Selection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Novel M-Estimator for Robust PCA |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Reliable Effective Terascale Linear Learning System |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Tensor Approach to Learning Mixed Membership Community Models |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Truncated EM Approach for Spike-and-Slab Sparse Coding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Accelerating t-SNE using Tree-Based Algorithms |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Active Contextual Policy Search |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Active Learning Using Smooth Relative Regret Approximations with Applications |
✅ |
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❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls |
❌ |
❌ |
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❌ |
❌ |
❌ |
❌ |
0 |
| Adaptive Sampling for Large Scale Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Alternating Linearization for Structured Regularization Problems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Asymptotic Accuracy of Distribution-Based Estimation of Latent Variables |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Axioms for Graph Clustering Quality Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Co-Boosting for Multi-modal Gesture Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Entropy Estimation for Countable Discrete Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Boosting Algorithms for Detector Cascade Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bridging Viterbi and Posterior Decoding: A Generalized Risk Approach to Hidden Path Inference Based on Hidden Markov Models |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Causal Discovery with Continuous Additive Noise Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Classifier Cascades and Trees for Minimizing Feature Evaluation Cost |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Clustering Hidden Markov Models with Variational HEM |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Clustering Partially Observed Graphs via Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Confidence Intervals and Hypothesis Testing for High-Dimensional Regression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Contextual Bandits with Similarity Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convex vs Non-Convex Estimators for Regression and Sparse Estimation: the Mean Squared Error Properties of ARD and GLasso |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cover Tree Bayesian Reinforcement Learning |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Detecting Click Fraud in Online Advertising: A Data Mining Approach |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? |
❌ |
✅ |
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❌ |
✅ |
✅ |
5 |
| Dropout: A Simple Way to Prevent Neural Networks from Overfitting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Early Stopping and Non-parametric Regression: An Optimal Data-dependent Stopping Rule |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Effective Sampling and Learning for Mallows Models with Pairwise-Preference Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Effective String Processing and Matching for Author Disambiguation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Efficient Learning and Planning with Compressed Predictive States |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Occlusive Components Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient State-Space Inference of Periodic Latent Force Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Expectation Propagation for Neural Networks with Sparsity-Promoting Priors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast SVM Training Using Approximate Extreme Points |
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❌ |
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5 |
| Follow the Leader If You Can, Hedge If You Must |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Gibbs Max-margin Topic Models with Data Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Estimation From Multi-Attribute Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Ground Metric Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hitting and Commute Times in Large Random Neighborhood Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Markov Network Structure Learning Using Decision Trees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Prediction from Dirichlet Process Mixtures via Enrichment |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inconsistency of Pitman-Yor Process Mixtures for the Number of Components |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information Theoretical Estimators Toolbox |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Iteration Complexity of Feasible Descent Methods for Convex Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| LIBOL: A Library for Online Learning Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Graphical Models With Hubs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Link Prediction in Graphs with Autoregressive Features |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Locally Adaptive Factor Processes for Multivariate Time Series |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Manopt, a Matlab Toolbox for Optimization on Manifolds |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Matrix Completion with the Trace Norm: Learning, Bounding, and Transducing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Multi-Objective Reinforcement Learning using Sets of Pareto Dominating Policies |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multimodal Learning with Deep Boltzmann Machines |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Natural Evolution Strategies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| New Learning Methods for Supervised and Unsupervised Preference Aggregation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| New Results for Random Walk Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Node-Based Learning of Multiple Gaussian Graphical Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Off-policy Learning With Eligibility Traces: A Survey |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Multilabel Classification and Ranking with Bandit Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Bayes-Optimality of F-Measure Maximizers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| One-Shot-Learning Gesture Recognition using HOG-HOF Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimality of Graphlet Screening in High Dimensional Variable Selection |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Order-Independent Constraint-Based Causal Structure Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Parallel MCMC with Generalized Elliptical Slice Sampling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Particle Gibbs with Ancestor Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Policy Evaluation with Temporal Differences: A Survey and Comparison |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Prediction and Clustering in Signed Networks: A Local to Global Perspective |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Ramp Loss Linear Programming Support Vector Machine |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Random Intersection Trees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Recursive Teaching Dimension, VC-Dimension and Sample Compression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Revisiting Bayesian Blind Deconvolution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Revisiting Stein's Paradox: Multi-Task Averaging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Hierarchical Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Online Gesture Recognition with Crowdsourced Annotations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SPMF: A Java Open-Source Pattern Mining Library |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Seeded Graph Matching for Correlated Erdos-Renyi Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Set-Valued Approachability and Online Learning with Partial Monitoring |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sparse Factor Analysis for Learning and Content Analytics |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Statistical Analysis of Metric Graph Reconstruction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured Prediction via Output Space Search |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tensor Decompositions for Learning Latent Variable Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| The Gesture Recognition Toolkit |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| The Student-t Mixture as a Natural Image Patch Prior with Application to Image Compression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Ultrahigh Dimensional Feature Selection for Big Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training Highly Multiclass Classifiers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transfer Learning Decision Forests for Gesture Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unbiased Generative Semi-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| What Regularized Auto-Encoders Learn from the Data-Generating Distribution |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| pystruct - Learning Structured Prediction in Python |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |