Journal of Machine Learning Research (JMLR) Volume 18, 2017

  1. Averaged Collapsed Variational Bayes Inference
  2. Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
  3. Local algorithms for interactive clustering
  4. SnapVX: A Network-Based Convex Optimization Solver
  5. Communication-efficient Sparse Regression
  6. Improving Variational Methods via Pairwise Linear Response Identities
  7. Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
  8. Persistence Images: A Stable Vector Representation of Persistent Homology
  9. Spectral Clustering Based on Local PCA
  10. On Perturbed Proximal Gradient Algorithms
  11. Differential Privacy for Bayesian Inference through Posterior Sampling
  12. Refinery: An Open Source Topic Modeling Web Platform
  13. Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns
  14. Automatic Differentiation Variational Inference
  15. Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
  16. A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification
  17. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
  18. Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
  19. Breaking the Curse of Dimensionality with Convex Neural Networks
  20. Memory Efficient Kernel Approximation
  21. On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
  22. Analyzing Tensor Power Method Dynamics in Overcomplete Regime
  23. Identifying a Minimal Class of Models for High–dimensional Data
  24. Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
  25. POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty
  26. Generalized Pólya Urn for Time-Varying Pitman-Yor Processes
  27. Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
  28. Certifiably Optimal Low Rank Factor Analysis
  29. Group Sparse Optimization via lp,q Regularization
  30. Preference-based Teaching
  31. Nonparametric Risk Bounds for Time-Series Forecasting
  32. Online Bayesian Passive-Aggressive Learning
  33. Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning
  34. A Spectral Algorithm for Inference in Hidden semi-Markov Models
  35. Simplifying Probabilistic Expressions in Causal Inference
  36. Nearly optimal classification for semimetrics
  37. Bridging Supervised Learning and Test-Based Co-optimization
  38. GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
  39. GPflow: A Gaussian Process Library using TensorFlow
  40. COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution
  41. Learning Local Dependence In Ordered Data
  42. Bayesian Learning of Dynamic Multilayer Networks
  43. Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality
  44. Asymptotic behavior of Support Vector Machine for spiked population model
  45. Distributed Semi-supervised Learning with Kernel Ridge Regression
  46. On Markov chain Monte Carlo methods for tall data
  47. Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
  48. Clustering from General Pairwise Observations with Applications to Time-varying Graphs
  49. Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
  50. Reconstructing Undirected Graphs from Eigenspaces
  51. An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
  52. Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
  53. Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
  54. On the Consistency of Ordinal Regression Methods
  55. Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
  56. Density Estimation in Infinite Dimensional Exponential Families
  57. Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
  58. Joint Label Inference in Networks
  59. Achieving Optimal Misclassification Proportion in Stochastic Block Models
  60. On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks
  61. Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
  62. Fundamental Conditions for Low-CP-Rank Tensor Completion
  63. Parallel Symmetric Class Expression Learning
  64. Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning
  65. Hierarchically Compositional Kernels for Scalable Nonparametric Learning
  66. Sharp Oracle Inequalities for Square Root Regularization
  67. Soft Margin Support Vector Classification as Buffered Probability Minimization
  68. Variational Particle Approximations
  69. A Bayesian Framework for Learning Rule Sets for Interpretable Classification
  70. A Robust-Equitable Measure for Feature Ranking and Selection
  71. Multiscale Strategies for Computing Optimal Transport
  72. Non-parametric Policy Search with Limited Information Loss
  73. Tests of Mutual or Serial Independence of Random Vectors with Applications
  74. Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements
  75. Quantifying the Informativeness of Similarity Measurements
  76. Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis
  77. Relational Reinforcement Learning for Planning with Exogenous Effects
  78. Bayesian Tensor Regression
  79. Robust Discriminative Clustering with Sparse Regularizers
  80. Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions
  81. Learning Scalable Deep Kernels with Recurrent Structure
  82. Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
  83. Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
  84. Angle-based Multicategory Distance-weighted SVM
  85. Minimax Estimation of Kernel Mean Embeddings
  86. The Impact of Random Models on Clustering Similarity
  87. Hierarchical Clustering via Spreading Metrics
  88. The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems
  89. A survey of Algorithms and Analysis for Adaptive Online Learning
  90. A distributed block coordinate descent method for training l1 regularized linear classifiers
  91. Distributed Learning with Regularized Least Squares
  92. Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
  93. An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
  94. Fisher Consistency for Prior Probability Shift
  95. openXBOW — Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
  96. Optimal Rates for Multi-pass Stochastic Gradient Methods
  97. Rank Determination for Low-Rank Data Completion
  98. Bayesian Network Learning via Topological Order
  99. Stability of Controllers for Gaussian Process Dynamics
  100. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
  101. Confidence Sets with Expected Sizes for Multiclass Classification
  102. Online Learning to Rank with Top-k Feedback
  103. A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
  104. Accelerating Stochastic Composition Optimization
  105. Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
  106. Optimal Dictionary for Least Squares Representation
  107. Computational Limits of A Distributed Algorithm for Smoothing Spline
  108. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
  109. Clustering with Hidden Markov Model on Variable Blocks
  110. Approximation Vector Machines for Large-scale Online Learning
  111. Efficient Sampling from Time-Varying Log-Concave Distributions
  112. Document Neural Autoregressive Distribution Estimation
  113. Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
  114. A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
  115. Second-Order Stochastic Optimization for Machine Learning in Linear Time
  116. Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
  117. Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
  118. Probabilistic Line Searches for Stochastic Optimization
  119. Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
  120. Classification of Time Sequences using Graphs of Temporal Constraints
  121. Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
  122. Kernel Partial Least Squares for Stationary Data
  123. Robust and Scalable Bayes via a Median of Subset Posterior Measures
  124. Statistical and Computational Guarantees for the Baum-Welch Algorithm
  125. Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
  126. Poisson Random Fields for Dynamic Feature Models
  127. Gap Safe Screening Rules for Sparsity Enforcing Penalties
  128. Minimax Filter: Learning to Preserve Privacy from Inference Attacks
  129. Knowledge Graph Completion via Complex Tensor Factorization
  130. Stabilized Sparse Online Learning for Sparse Data
  131. Active-set Methods for Submodular Minimization Problems
  132. A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
  133. Stochastic Gradient Descent as Approximate Bayesian Inference
  134. STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
  135. A Survey of Preference-Based Reinforcement Learning Methods
  136. Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
  137. Dimension Estimation Using Random Connection Models
  138. Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
  139. Adaptive Randomized Dimension Reduction on Massive Data
  140. A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
  141. Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
  142. On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
  143. Generalized Conditional Gradient for Sparse Estimation
  144. Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
  145. Regularization and the small-ball method II: complexity dependent error rates
  146. Matrix Completion with Noisy Entries and Outliers
  147. Faithfulness of Probability Distributions and Graphs
  148. Community Extraction in Multilayer Networks with Heterogeneous Community Structure
  149. On Binary Embedding using Circulant Matrices
  150. Variational Fourier Features for Gaussian Processes
  151. HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
  152. Automatic Differentiation in Machine Learning: a Survey
  153. Normal Bandits of Unknown Means and Variances
  154. Cost-Sensitive Learning with Noisy Labels
  155. Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
  156. A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning
  157. Probabilistic preference learning with the Mallows rank model
  158. Robust Topological Inference: Distance To a Measure and Kernel Distance
  159. Training Gaussian Mixture Models at Scale via Coresets
  160. Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
  161. Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
  162. Deep Learning the Ising Model Near Criticality
  163. pomegranate: Fast and Flexible Probabilistic Modeling in Python
  164. Maximum Principle Based Algorithms for Deep Learning
  165. Gradient Hard Thresholding Pursuit
  166. Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
  167. Local Identifiability of -minimization Dictionary Learning: a Sufficient and Almost Necessary Condition
  168. In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics
  169. On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness
  170. Convergence of Unregularized Online Learning Algorithms
  171. Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
  172. auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
  173. On the Stability of Feature Selection Algorithms
  174. Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
  175. The DFS Fused Lasso: Linear-Time Denoising over General Graphs
  176. Community Detection and Stochastic Block Models: Recent Developments
  177. On -bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data
  178. Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
  179. Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
  180. To Tune or Not to Tune the Number of Trees in Random Forest
  181. Divide-and-Conquer for Debiased -norm Support Vector Machine in Ultra-high Dimensions
  182. Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits
  183. On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
  184. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
  185. Submatrix localization via message passing
  186. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
  187. Significance-based community detection in weighted networks
  188. Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor
  189. Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
  190. KELP: a Kernel-based Learning Platform
  191. Uncovering Causality from Multivariate Hawkes Integrated Cumulants
  192. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
  193. Enhancing Identification of Causal Effects by Pruning
  194. Active Nearest-Neighbor Learning in Metric Spaces
  195. From Predictive Methods to Missing Data Imputation: An Optimization Approach
  196. Saturating Splines and Feature Selection
  197. Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization
  198. Simple, Robust and Optimal Ranking from Pairwise Comparisons
  199. Surprising properties of dropout in deep networks
  200. Exact Learning of Lightweight Description Logic Ontologies
  201. Sparse Concordance-assisted Learning for Optimal Treatment Decision
  202. Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
  203. Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
  204. Steering Social Activity: A Stochastic Optimal Control Point Of View
  205. The Search Problem in Mixture Models
  206. An Eigenvector Perturbation Bound and Its Application
  207. A Tight Bound of Hard Thresholding
  208. Estimation of Graphical Models through Structured Norm Minimization
  209. Sparse Exchangeable Graphs and Their Limits via Graphon Processes
  210. Weighted SGD for Regression with Randomized Preconditioning
  211. Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
  212. Gaussian Lower Bound for the Information Bottleneck Limit
  213. tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
  214. SGDLibrary: A MATLAB library for stochastic optimization algorithms
  215. Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
  216. Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
  217. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
  218. Compact Convex Projections
  219. Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
  220. Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
  221. Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
  222. Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
  223. Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS)
  224. Improved spectral community detection in large heterogeneous networks
  225. Statistical Inference on Random Dot Product Graphs: a Survey
  226. Avanti Athreya, Donniell E. Fishkind, Minh Tang, Carey E. Priebe, Youngser Park, Joshua T.
  227. Rate of Convergence of -Nearest-Neighbor Classification Rule
  228. A Theory of Learning with Corrupted Labels
  229. Interactive Algorithms: Pool, Stream and Precognitive Stream
  230. CoCoA: A General Framework for Communication-Efficient Distributed Optimization
  231. Concentration inequalities for empirical processes of linear time series
  232. A Cluster Elastic Net for Multivariate Regression
  233. Characteristic and Universal Tensor Product Kernels
  234. Learning Certifiably Optimal Rule Lists for Categorical Data
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