| (Near) Dimension Independent Risk Bounds for Differentially Private Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Bayesian Framework for Online Classifier Ensemble |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Bayesian Wilcoxon signed-rank test based on the Dirichlet process |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Clockwork RNN |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Compilation Target for Probabilistic Programming Languages |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Consistent Histogram Estimator for Exchangeable Graph Models |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A Convergence Rate Analysis for LogitBoost, MART and Their Variant |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Deep Semi-NMF Model for Learning Hidden Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Deep and Tractable Density Estimator |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Discriminative Latent Variable Model for Online Clustering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Divide-and-Conquer Solver for Kernel Support Vector Machines |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Kernel Independence Test for Random Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A PAC-Bayesian bound for Lifelong Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Physics-Based Model Prior for Object-Oriented MDPs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Statistical Perspective on Algorithmic Leveraging |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Unified Framework for Consistency of Regularized Loss Minimizers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Unifying View of Representer Theorems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A new Q(lambda) with interim forward view and Monte Carlo equivalence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A reversible infinite HMM using normalised random measures |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Active Detection via Adaptive Submodularity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Active Learning of Parameterized Skills |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Active Transfer Learning under Model Shift |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
3 |
| Adaptive Monte Carlo via Bandit Allocation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Admixture of Poisson MRFs: A Topic Model with Word Dependencies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Affinity Weighted Embedding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Agnostic Bayesian Learning of Ensembles |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Alternating Minimization for Mixed Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| An Asynchronous Parallel Stochastic Coordinate Descent Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| An Efficient Approach for Assessing Hyperparameter Importance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| An Information Geometry of Statistical Manifold Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Anomaly Ranking as Supervised Bipartite Ranking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Anti-differentiating approximation algorithms:A case study with min-cuts, spectral, and flow |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Approximate Policy Iteration Schemes: A Comparison |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Asymptotically consistent estimation of the number of change points in highly dependent time series |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asynchronous Distributed ADMM for Consensus Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Automated inference of point of view from user interactions in collective intelligence venues |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bayesian Max-margin Multi-Task Learning with Data Augmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization with Inequality Constraints |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Beta Diffusion Trees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bias in Natural Actor-Critic Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Boosting multi-step autoregressive forecasts |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Boosting with Online Binary Learners for the Multiclass Bandit Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Circulant Binary Embedding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Clustering in the Presence of Background Noise |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Coding for Random Projections |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coherent Matrix Completion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cold-start Active Learning with Robust Ordinal Matrix Factorization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication-Efficient Distributed Optimization using an Approximate Newton-type Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Compact Random Feature Maps |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Composite Quantization for Approximate Nearest Neighbor Search |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Compositional Morphology for Word Representations and Language Modelling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Computing Parametric Ranking Models via Rank-Breaking |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Concentration in unbounded metric spaces and algorithmic stability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Concept Drift Detection Through Resampling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Condensed Filter Tree for Cost-Sensitive Multi-Label Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Consistency of Causal Inference under the Additive Noise Model |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Convergence rates for persistence diagram estimation in Topological Data Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convex Total Least Squares |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coordinate-descent for learning orthogonal matrices through Givens rotations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Covering Number for Efficient Heuristic-based POMDP Planning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep AutoRegressive Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Generative Stochastic Networks Trainable by Backprop |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Demystifying Information-Theoretic Clustering |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deterministic Policy Gradient Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Discovering Latent Network Structure in Point Process Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discrete Chebyshev Classifiers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Discriminative Features via Generalized Eigenvectors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Distributed Representations of Sentences and Documents |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Distributed Stochastic Gradient MCMC |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Doubly Stochastic Variational Bayes for non-Conjugate Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dual Query: Practical Private Query Release for High Dimensional Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamic Programming Boosting for Discriminative Macro-Action Discovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Effective Bayesian Modeling of Groups of Related Count Time Series |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
3 |
| Efficient Algorithms for Robust One-bit Compressive Sensing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Continuous-Time Markov Chain Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Dimensionality Reduction for High-Dimensional Network Estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Label Propagation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Learning of Mahalanobis Metrics for Ranking |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Elementary Estimators for High-Dimensional Linear Regression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Elementary Estimators for Sparse Covariance Matrices and other Structured Moments |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Ensemble Methods for Structured Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating Latent-Variable Graphical Models using Moments and Likelihoods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exchangeable Variable Models |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Exponential Family Matrix Completion under Structural Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Fast Allocation of Gaussian Process Experts |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fast Computation of Wasserstein Barycenters |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Multi-stage Submodular Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Stochastic Alternating Direction Method of Multipliers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Filtering with Abstract Particles |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Finding Dense Subgraphs via Low-Rank Bilinear Optimization |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Finito: A faster, permutable incremental gradient method for big data problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gaussian Approximation of Collective Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gaussian Process Classification and Active Learning with Multiple Annotators |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Gaussian Process Optimization with Mutual Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Exponential Concentration Inequality for Renyi Divergence Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Geodesic Distance Function Learning via Heat Flow on Vector Fields |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Global graph kernels using geometric embeddings |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hamiltonian Monte Carlo Without Detailed Balance |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hard-Margin Active Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Heavy-tailed regression with a generalized median-of-means |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hierarchical Dirichlet Scaling Process |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Quasi-Clustering Methods for Asymmetric Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| High Order Regularization for Semi-Supervised Learning of Structured Output Problems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inferning with High Girth Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Influence Function Learning in Information Diffusion Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Input Warping for Bayesian Optimization of Non-Stationary Functions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Joint Inference of Multiple Label Types in Large Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| K-means recovers ICA filters when independent components are sparse |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel Adaptive Metropolis-Hastings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Kernel Mean Estimation and Stein Effect |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Large-Margin Metric Learning for Constrained Partitioning Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Large-margin Weakly Supervised Dimensionality Reduction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Large-scale Multi-label Learning with Missing Labels |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Latent Bandits. |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latent Confusion Analysis by Normalized Gamma Construction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Latent Semantic Representation Learning for Scene Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learnability of the Superset Label Learning Problem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Character-level Representations for Part-of-Speech Tagging |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Complex Neural Network Policies with Trajectory Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Graphs with a Few Hubs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Latent Variable Gaussian Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Mixtures of Linear Classifiers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Modular Structures from Network Data and Node Variables |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Ordered Representations with Nested Dropout |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Polynomials with Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Sum-Product Networks with Direct and Indirect Variable Interactions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Theory and Algorithms for revenue optimization in second price auctions with reserve |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning by Stretching Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from Contagion (Without Timestamps) |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning the Irreducible Representations of Commutative Lie Groups |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning the Parameters of Determinantal Point Process Kernels |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Disentangle Factors of Variation with Manifold Interaction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Least Squares Revisited: Scalable Approaches for Multi-class Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Programming for Large-Scale Markov Decision Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Linear Time Solver for Primal SVM |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Linear and Parallel Learning of Markov Random Fields |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Local Ordinal Embedding |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Local algorithms for interactive clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-density Parity Constraints for Hashing-Based Discrete Integration |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Making Fisher Discriminant Analysis Scalable |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Marginal Structured SVM with Hidden Variables |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Marginalized Denoising Auto-encoders for Nonlinear Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Margins, Kernels and Non-linear Smoothed Perceptrons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Max-Margin Infinite Hidden Markov Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Maximum Margin Multiclass Nearest Neighbors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory (and Time) Efficient Sequential Monte Carlo |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Memory Efficient Kernel Approximation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Memory and Computation Efficient PCA via Very Sparse Random Projections |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Min-Max Problems on Factor Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model-Based Relational RL When Object Existence is Partially Observable |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modeling Correlated Arrival Events with Latent Semi-Markov Processes |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-label Classification via Feature-aware Implicit Label Space Encoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-period Trading Prediction Markets with Connections to Machine Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multimodal Neural Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiple Testing under Dependence via Semiparametric Graphical Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multiresolution Matrix Factorization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Multivariate Maximal Correlation Analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Narrowing the Gap: Random Forests In Theory and In Practice |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal Joint Object Matching via Convex Relaxation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near-Optimally Teaching the Crowd to Classify |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearest Neighbors Using Compact Sparse Codes |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Variational Inference and Learning in Belief Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Nonlinear Information-Theoretic Compressive Measurement Design |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Nonnegative Sparse PCA with Provable Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonparametric Estimation of Multi-View Latent Variable Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonparametric Estimation of Renyi Divergence and Friends |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Nuclear Norm Minimization via Active Subspace Selection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Measure Concentration of Random Maximum A-Posteriori Perturbations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Modelling Non-linear Topical Dependencies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Robustness and Regularization of Structural Support Vector Machines |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| On learning to localize objects with minimal supervision |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the convergence of no-regret learning in selfish routing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| One Practical Algorithm for Both Stochastic and Adversarial Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Bayesian Passive-Aggressive Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Clustering of Bandits |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Learning in Markov Decision Processes with Changing Cost Sequences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Multi-Task Learning for Policy Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Stochastic Optimization under Correlated Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimal Mean Robust Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimization Equivalence of Divergences Improves Neighbor Embedding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Outlier Path: A Homotopy Algorithm for Robust SVM |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PAC-inspired Option Discovery in Lifelong Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pitfalls in the use of Parallel Inference for the Dirichlet Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Prediction with Limited Advice and Multiarmed Bandits with Paid Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Preserving Modes and Messages via Diverse Particle Selection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Matrix Factorization with Non-random Missing Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Partial Canonical Correlation Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Programming by Feedback |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provable Bounds for Learning Some Deep Representations |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Pursuit-Evasion Without Regret, with an Application to Trading |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Putting MRFs on a Tensor Train |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Randomized Nonlinear Component Analysis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rank-One Matrix Pursuit for Matrix Completion |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rectangular Tiling Process |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recurrent Convolutional Neural Networks for Scene Labeling |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Reducing Dueling Bandits to Cardinal Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian Pursuit for Big Matrix Recovery |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Inverse Covariance Estimation under Noisy Measurements |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Principal Component Analysis with Complex Noise |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust and Efficient Kernel Hyperparameter Paths with Guarantees |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Saddle Points and Accelerated Perceptron Algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Safe Screening with Variational Inequalities and Its Application to Lasso |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Efficient Reinforcement Learning with Gaussian Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample-based approximate regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Semidefinite Relaxation for Maximum A Posterior Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable and Robust Bayesian Inference via the Median Posterior |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scaling SVM and Least Absolute Deviations via Exact Data Reduction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Up Robust MDPs using Function Approximation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Signal recovery from Pooling Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Skip Context Tree Switching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Reinforcement Learning via Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse meta-Gaussian information bottleneck |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spectral Bandits for Smooth Graph Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Regularization for Max-Margin Sequence Tagging |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Spherical Hamiltonian Monte Carlo for Constrained Target Distributions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stable and Efficient Representation Learning with Nonnegativity Constraints |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Statistical analysis of stochastic gradient methods for generalized linear models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Backpropagation and Approximate Inference in Deep Generative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Gradient Hamiltonian Monte Carlo |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Neighbor Compression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Variational Inference for Bayesian Time Series Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Structured Generative Models of Natural Source Code |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured Prediction of Network Response |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Structured Recurrent Temporal Restricted Boltzmann Machines |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Coherent Loss Function for Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| The Falling Factorial Basis and Its Statistical Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Inverse Regression Topic Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Thompson Sampling for Complex Online Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Time-Regularized Interrupting Options (TRIO) |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards End-To-End Speech Recognition with Recurrent Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Minimax Online Learning with Unknown Time Horizon |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Towards an optimal stochastic alternating direction method of multipliers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tracking Adversarial Targets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Transductive Learning with Multi-class Volume Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| True Online TD(lambda) |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Two-Stage Metric Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Matrix Completion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Variational Inference for Sequential Distance Dependent Chinese Restaurant Process |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Von Mises-Fisher Clustering Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Wasserstein Propagation for Semi-Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Weighted Graph Clustering with Non-Uniform Uncertainties |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |