JMLR Volume 18

• Averaged Collapsed Variational Bayes Inference

  Katsuhiko Ishiguro, Issei Sato, Naonori Ueda (1):1−29, 2017 PDF BibTeX

• Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks

  Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song (2):1−45, 2017 PDF BibTeX

• Local algorithms for interactive clustering

  Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski (3):1−35, 2017 PDF BibTeX

• SnapVX: A Network-Based Convex Optimization Solver

  David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec (4):1−5, 2017 codestanford.eduPDF BibTeX

• Communication-efficient Sparse Regression

  Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor (5):1−30, 2017 PDF BibTeX

• Improving Variational Methods via Pairwise Linear Response Identities

  Jack Raymond, Federico Ricci-Tersenghi (6):1−36, 2017 PDF BibTeX

• Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks

  Adam S. Charles, Dong Yin, Christopher J. Rozell (7):1−37, 2017 PDF BibTeX

• Persistence Images: A Stable Vector Representation of Persistent Homology

  Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, Lori Ziegelmeier (8):1−35, 2017 erratumPDF BibTeX

• Spectral Clustering Based on Local PCA

  Ery Arias-Castro, Gilad Lerman, Teng Zhang (9):1−57, 2017 PDF BibTeX

• On Perturbed Proximal Gradient Algorithms

  Yves F. Atchadé, Gersende Fort, Eric Moulines (10):1−33, 2017 PDF BibTeX

• Differential Privacy for Bayesian Inference through Posterior Sampling

  Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein (11):1−39, 2017 PDF BibTeX

• Refinery: An Open Source Topic Modeling Web Platform

  Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth (12):1−5, 2017 codewebpagePDF BibTeX

• Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns

  Herbert Jaeger (13):1−43, 2017 supplementaryPDF BibTeX

• Automatic Differentiation Variational Inference

  Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei (14):1−45, 2017 PDF BibTeX

• Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters

  Jacques Wainer, Gavin Cawley (15):1−35, 2017 PDF BibTeX

• A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification

  Naoki Ito, Akiko Takeda, Kim-Chuan Toh (16):1−49, 2017 PDF BibTeX

• Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

  Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas (17):1−5, 2017 codewebpagePDF BibTeX

• Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles

  Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen (18):1−65, 2017 PDF BibTeX

• Breaking the Curse of Dimensionality with Convex Neural Networks

  Francis Bach (19):1−53, 2017 PDF BibTeX

• Memory Efficient Kernel Approximation

  Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon (20):1−32, 2017 PDF BibTeX

• On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions

  Francis Bach (21):1−38, 2017 PDF BibTeX

• Analyzing Tensor Power Method Dynamics in Overcomplete Regime

  Animashree An, kumar, Rong Ge, Majid Janzamin (22):1−40, 2017 PDF BibTeX

• JSAT: Java Statistical Analysis Tool, a Library for Machine Learning

  Edward Raff (23):1−5, 2017 codewebpagePDF BibTeX

• Identifying a Minimal Class of Models for High--dimensional Data

  Daniel Nevo, Ya'acov Ritov (24):1−29, 2017 PDF BibTeX

• Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA

  Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown (25):1−5, 2017 codewebpagePDF BibTeX

• POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty

  Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer (26):1−5, 2017 codewebpagePDF BibTeX

• Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes

  François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy (27):1−32, 2017 PDF BibTeX

• Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models

  Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth (28):1−39, 2017 PDF BibTeX

• Certifiably Optimal Low Rank Factor Analysis

  Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder (29):1−53, 2017 PDF BibTeX

• Group Sparse Optimization via lp,q Regularization

  Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang (30):1−52, 2017 PDF BibTeX

• Preference-based Teaching

  Ziyuan Gao, Christoph Ries, Hans U. Simon, S, ra Zilles (31):1−32, 2017 PDF BibTeX

• Nonparametric Risk Bounds for Time-Series Forecasting

  Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish (32):1−40, 2017 PDF BibTeX

• Online Bayesian Passive-Aggressive Learning

  Tianlin Shi, Jun Zhu (33):1−39, 2017 PDF BibTeX

• Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning

  Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy (34):1−41, 2017 PDF BibTeX

• A Spectral Algorithm for Inference in Hidden semi-Markov Models

  Igor Melnyk, Arindam Banerjee (35):1−39, 2017 PDF BibTeX

• Simplifying Probabilistic Expressions in Causal Inference

  Santtu Tikka, Juha Karvanen (36):1−30, 2017 PDF BibTeX

• Nearly optimal classification for semimetrics

  Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch (37):1−22, 2017 PDF BibTeX

• Bridging Supervised Learning and Test-Based Co-optimization

  Elena Popovici (38):1−39, 2017 appendixPDF BibTeX

• GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis

  Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski (39):1−5, 2017 coder-project.orgPDF BibTeX

• GPflow: A Gaussian Process Library using TensorFlow

  Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\'o}n-Villagr{\'a}, Zoubin Ghahramani, James Hensman (40):1−6, 2017 codewebpagePDF BibTeX

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution

  Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, Le Song (41):1−49, 2017 PDF BibTeX

• Learning Local Dependence In Ordered Data

  Guo Yu, Jacob Bien (42):1−60, 2017 PDF BibTeX

• Bayesian Learning of Dynamic Multilayer Networks

  Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts (43):1−29, 2017 PDF BibTeX

• Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality

  Samory Kpotufe, Nakul Verma (44):1−29, 2017 PDF BibTeX

• Asymptotic behavior of Support Vector Machine for spiked population model

  Hanwen Huang (45):1−21, 2017 PDF BibTeX

• Distributed Semi-supervised Learning with Kernel Ridge Regression

  Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou (46):1−22, 2017 PDF BibTeX

• On Markov chain Monte Carlo methods for tall data

  Rémi Bardenet, Arnaud Doucet, Chris Holmes (47):1−43, 2017 PDF BibTeX

• Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

  Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease (48):1−33, 2017 PDF BibTeX

• Clustering from General Pairwise Observations with Applications to Time-varying Graphs

  Shiau Hong Lim, Yudong Chen, Huan Xu (49):1−47, 2017 PDF BibTeX

• Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques

  Debarghya Ghoshdastidar, Ambedkar Dukkipati (50):1−41, 2017 PDF BibTeX

• Reconstructing Undirected Graphs from Eigenspaces

  Yohann De Castro, Thibault Espinasse, Paul Rochet (51):1−24, 2017 PDF BibTeX

• An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback

  Ohad Shamir (52):1−11, 2017 PDF BibTeX

• Perishability of Data: Dynamic Pricing under Varying-Coefficient Models

  Adel Javanmard (53):1−31, 2017 PDF BibTeX

• Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect

  Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar (54):1−24, 2017 PDF BibTeX

• On the Consistency of Ordinal Regression Methods

  Fabian Pedregosa, Francis Bach, Alexandre Gramfort (55):1−35, 2017 PDF BibTeX

• Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences

  Takashi Takenouchi, Takafumi Kanamori (56):1−26, 2017 PDF BibTeX

• Density Estimation in Infinite Dimensional Exponential Families

  Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyv\"{a}rinen, Revant Kumar (57):1−59, 2017 PDF BibTeX

• Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis

  Matthäus Kleindessner, Ulrike von Luxburg (58):1−52, 2017 PDF BibTeX

• Joint Label Inference in Networks

  Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy (59):1−39, 2017 PDF BibTeX

• Achieving Optimal Misclassification Proportion in Stochastic Block Models

  Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou (60):1−45, 2017 PDF BibTeX

• On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks

  Prakash Balach, ran, Eric D. Kolaczyk, Weston D. Viles (61):1−33, 2017 PDF BibTeX

• Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA

  Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas (62):1−58, 2017 PDF BibTeX

• Fundamental Conditions for Low-CP-Rank Tensor Completion

  Morteza Ashraphijuo, Xiaodong Wang (63):1−29, 2017 PDF BibTeX

• Parallel Symmetric Class Expression Learning

  An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsl, (64):1−34, 2017 PDF BibTeX

• Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning

  Jervis Pinto, Alan Fern (65):1−35, 2017 PDF BibTeX

• Hierarchically Compositional Kernels for Scalable Nonparametric Learning

  Jie Chen, Haim Avron, Vikas Sindhwani (66):1−42, 2017 PDF BibTeX

• Sharp Oracle Inequalities for Square Root Regularization

  Benjamin Stucky, Sara van de Geer (67):1−29, 2017 PDF BibTeX

• Soft Margin Support Vector Classification as Buffered Probability Minimization

  Matthew Norton, Alexander Mafusalov, Stan Uryasev (68):1−43, 2017 PDF BibTeX

• Variational Particle Approximations

  Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman (69):1−29, 2017 PDF BibTeX

• A Bayesian Framework for Learning Rule Sets for Interpretable Classification

  Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille (70):1−37, 2017 PDF BibTeX

• A Robust-Equitable Measure for Feature Ranking and Selection

  A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang (71):1−46, 2017 PDF BibTeX

• Multiscale Strategies for Computing Optimal Transport

  Samuel Gerber, Mauro Maggioni (72):1−32, 2017 PDF BibTeX

• Non-parametric Policy Search with Limited Information Loss

  Herke van Hoof, Gerhard Neumann, Jan Peters (73):1−46, 2017 PDF BibTeX

• Tests of Mutual or Serial Independence of Random Vectors with Applications

  Martin Bilodeau, Aurélien Guetsop Nangue (74):1−40, 2017 supplementaryPDF BibTeX

• Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements

  Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail (75):1−34, 2017 PDF BibTeX

• Quantifying the Informativeness of Similarity Measurements

  Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas (76):1−61, 2017 PDF BibTeX

• Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis

  Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon (77):1−36, 2017 PDF BibTeX

• Relational Reinforcement Learning for Planning with Exogenous Effects

  David Mart\'{i}nez, Guillem Aleny\`{a}, Tony Ribeiro, Katsumi Inoue, Carme Torras (78):1−44, 2017 PDF BibTeX

• Bayesian Tensor Regression

  Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson (79):1−31, 2017 PDF BibTeX

• Robust Discriminative Clustering with Sparse Regularizers

  Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach (80):1−50, 2017 PDF BibTeX

• Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions

  Weiwei Liu, Ivor W. Tsang (81):1−36, 2017 PDF BibTeX

• Learning Scalable Deep Kernels with Recurrent Structure

  Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing (82):1−37, 2017 PDF BibTeX

• Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

  Vardan Papyan, Yaniv Romano, Michael Elad (83):1−52, 2017 PDF BibTeX

• Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

  Yuchen Zhang, Lin Xiao (84):1−42, 2017 PDF BibTeX

• Angle-based Multicategory Distance-weighted SVM

  Hui Sun, Bruce A. Craig, Lingsong Zhang (85):1−21, 2017 PDF BibTeX

• Minimax Estimation of Kernel Mean Embeddings

  Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Mu, et (86):1−47, 2017 PDF BibTeX

• The Impact of Random Models on Clustering Similarity

  Alexander J. Gates, Yong-Yeol Ahn (87):1−28, 2017 PDF BibTeX

• Hierarchical Clustering via Spreading Metrics

  Aurko Roy, Sebastian Pokutta (88):1−35, 2017 PDF BibTeX

• The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems

  Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, Jo\~{a}o V. Messias (89):1−5, 2017 codePDF BibTeX

• A survey of Algorithms and Analysis for Adaptive Online Learning

  H. Brendan McMahan (90):1−50, 2017 PDF BibTeX

• A distributed block coordinate descent method for training l1 regularized linear classifiers

  Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan (91):1−35, 2017 PDF BibTeX

• Distributed Learning with Regularized Least Squares

  Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou (92):1−31, 2017 PDF BibTeX

• Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks

  Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman (93):1−67, 2017 PDF BibTeX

• An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels

  Weiwei Liu, Ivor W. Tsang, Klaus-Robert M\"{u}ller (94):1−38, 2017 PDF BibTeX

• Fisher Consistency for Prior Probability Shift

  Dirk Tasche (95):1−32, 2017 PDF BibTeX

• openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit

  Maximilian Schmitt, Björn Schuller (96):1−5, 2017 codePDF BibTeX

• Optimal Rates for Multi-pass Stochastic Gradient Methods

  Junhong Lin, Lorenzo Rosasco (97):1−47, 2017 PDF BibTeX

• Rank Determination for Low-Rank Data Completion

  Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal (98):1−29, 2017 PDF BibTeX

• Bayesian Network Learning via Topological Order

  Young Woong Park, Diego Klabjan (99):1−32, 2017 PDF BibTeX

• Stability of Controllers for Gaussian Process Dynamics

  Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters (100):1−37, 2017 PDF BibTeX

• Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

  Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach (101):1−51, 2017 PDF BibTeX

• Confidence Sets with Expected Sizes for Multiclass Classification

  Christophe Denis, Mohamed Hebiri (102):1−28, 2017 PDF BibTeX

• Online Learning to Rank with Top-k Feedback

  Sougata Chaudhuri, Ambuj Tewari (103):1−50, 2017 PDF BibTeX

• A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

  Thang D. Bui, Josiah Yan, Richard E. Turner (104):1−72, 2017 PDF BibTeX

• Accelerating Stochastic Composition Optimization

  Mengdi Wang, Ji Liu, Ethan X. Fang (105):1−23, 2017 PDF BibTeX

• Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server

  Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh (106):1−37, 2017 PDF BibTeX

• Optimal Dictionary for Least Squares Representation

  Mohammed Rayyan Sheriff, Debasish Chatterjee (107):1−28, 2017 PDF BibTeX

• Computational Limits of A Distributed Algorithm for Smoothing Spline

  Zuofeng Shang, Guang Cheng (108):1−37, 2017 PDF BibTeX

• Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

  Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor (109):1−67, 2017 PDF BibTeX

• Clustering with Hidden Markov Model on Variable Blocks

  Lin Lin, Jia Li (110):1−49, 2017 PDF BibTeX

• Approximation Vector Machines for Large-scale Online Learning

  Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung (111):1−55, 2017 PDF BibTeX

• Efficient Sampling from Time-Varying Log-Concave Distributions

  Hariharan Narayanan, Alexer Rakhlin (112):1−29, 2017 PDF BibTeX

• Document Neural Autoregressive Distribution Estimation

  Stanislas Lauly, Yin Zheng, Alex, re Allauzen, Hugo Larochelle (113):1−24, 2017 PDF BibTeX

• Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks

  Shannon Fenn, Pablo Moscato (114):1−26, 2017 PDF BibTeX

• A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization

  Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang (115):1−52, 2017 PDF BibTeX

• Second-Order Stochastic Optimization for Machine Learning in Linear Time

  Naman Agarwal, Brian Bullins, Elad Hazan (116):1−40, 2017 erratumPDF BibTeX

• Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models

  Jiahe Lin, George Michailidis (117):1−49, 2017 PDF BibTeX

• Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network

  Zheng-Chu Guo, Lei Shi, Qiang Wu (118):1−25, 2017 PDF BibTeX

• Probabilistic Line Searches for Stochastic Optimization

  Maren Mahsereci, Philipp Hennig (119):1−59, 2017 PDF BibTeX

• Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

  Ricardo Silva, Shohei Shimizu (120):1−49, 2017 PDF BibTeX

• Classification of Time Sequences using Graphs of Temporal Constraints

  Mathieu Guillame-Bert, Artur Dubrawski (121):1−34, 2017 PDF BibTeX

• Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement

  Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang (122):1−43, 2017 PDF BibTeX

• Kernel Partial Least Squares for Stationary Data

  Marco Singer, Tatyana Krivobokova, Axel Munk (123):1−41, 2017 PDF BibTeX

• Robust and Scalable Bayes via a Median of Subset Posterior Measures

  Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson (124):1−40, 2017 PDF BibTeX

• Statistical and Computational Guarantees for the Baum-Welch Algorithm

  Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright (125):1−53, 2017 PDF BibTeX

• Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

  Christophe Dupuy, Francis Bach (126):1−45, 2017 PDF BibTeX

• Poisson Random Fields for Dynamic Feature Models

  Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh (127):1−45, 2017 PDF BibTeX

• Gap Safe Screening Rules for Sparsity Enforcing Penalties

  Eugene Ndiaye, Olivier Fercoq, Alex, re Gramfort, Joseph Salmon (128):1−33, 2017 PDF BibTeX

• Minimax Filter: Learning to Preserve Privacy from Inference Attacks

  Jihun Hamm (129):1−31, 2017 PDF BibTeX

• Knowledge Graph Completion via Complex Tensor Factorization

  Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard (130):1−38, 2017 PDF BibTeX

• Stabilized Sparse Online Learning for Sparse Data

  Yuting Ma, Tian Zheng (131):1−36, 2017 PDF BibTeX

• Active-set Methods for Submodular Minimization Problems

  K. S. Sesh Kumar, Francis Bach (132):1−31, 2017 PDF BibTeX

• A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations

  Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman (133):1−33, 2017 PDF BibTeX

• Stochastic Gradient Descent as Approximate Bayesian Inference

  Stephan M, t, Matthew D. Hoffman, David M. Blei (134):1−35, 2017 PDF BibTeX

• STORE: Sparse Tensor Response Regression and Neuroimaging Analysis

  Will Wei Sun, Lexin Li (135):1−37, 2017 PDF BibTeX

• A Survey of Preference-Based Reinforcement Learning Methods

  Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz (136):1−46, 2017 PDF BibTeX

• Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising

  Jérémie Bigot, Charles Deledalle, Delphine Féral (137):1−50, 2017 PDF BibTeX

• Dimension Estimation Using Random Connection Models

  Paulo Serra, Michel M, jes (138):1−35, 2017 PDF BibTeX

• Bayesian Inference for Spatio-temporal Spike-and-Slab Priors

  Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen (139):1−58, 2017 PDF BibTeX

• Adaptive Randomized Dimension Reduction on Massive Data

  Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt (140):1−30, 2017 PDF BibTeX

• A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms

  Huishuai Zhang, Yingbin Liang, Yuejie Chi (141):1−35, 2017 PDF BibTeX

• Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering

  Pietro Coretto, Christian Hennig (142):1−39, 2017 PDF BibTeX

• On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models

  Yining Wang, Adams Wei Yu, Aarti Singh (143):1−41, 2017 PDF BibTeX

• Generalized Conditional Gradient for Sparse Estimation

  Yaoliang Yu, Xinhua Zhang, Dale Schuurmans (144):1−46, 2017 PDF BibTeX

• Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities

  Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári (145):1−31, 2017 PDF BibTeX

• Regularization and the small-ball method II: complexity dependent error rates

  Guillaume Lecué, Shahar Mendelson (146):1−48, 2017 PDF BibTeX

• Matrix Completion with Noisy Entries and Outliers

  Raymond K. W. Wong, Thomas C. M. Lee (147):1−25, 2017 PDF BibTeX

• Faithfulness of Probability Distributions and Graphs

  Kayvan Sadeghi (148):1−29, 2017 PDF BibTeX

• Community Extraction in Multilayer Networks with Heterogeneous Community Structure

  James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel (149):1−49, 2017 PDF BibTeX

• On Binary Embedding using Circulant Matrices

  Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang (150):1−30, 2018 PDF BibTeX

• Variational Fourier Features for Gaussian Processes

  James Hensman, Nicolas Durrande, Arno Solin (151):1−52, 2018 PDF BibTeX

• HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data

  Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning (152):1−6, 2018 codewebpagePDF BibTeX

• Automatic Differentiation in Machine Learning: a Survey

  Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind (153):1−43, 2018 PDF BibTeX

• Normal Bandits of Unknown Means and Variances

  Wesley Cowan, Junya Honda, Michael N. Katehakis (154):1−28, 2018 PDF BibTeX

• Cost-Sensitive Learning with Noisy Labels

  Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari (155):1−33, 2018 PDF BibTeX

• Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data

  Yining Wang, Aarti Singh (156):1−42, 2018 PDF BibTeX

• A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning

  Elif Vural, Christine Guillemot (157):1−55, 2018 PDF BibTeX

• Probabilistic preference learning with the Mallows rank model

  Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas (158):1−49, 2018 PDF BibTeX

• Robust Topological Inference: Distance To a Measure and Kernel Distance

  Fr{\'e}d{\'e}ric Chazal, Brittany Fasy, Fabrizio Lecci, Bertr, Michel, Aless, ro Rinaldo, Larry Wasserman (159):1−40, 2018 PDF BibTeX

• Training Gaussian Mixture Models at Scale via Coresets

  Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman (160):1−25, 2018 PDF BibTeX

• Gradient Estimation with Simultaneous Perturbation and Compressive Sensing

  Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe (161):1−27, 2018 PDF BibTeX

• Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model

  Clint P. George, Hani Doss (162):1−38, 2018 PDF BibTeX

• Deep Learning the Ising Model Near Criticality

  Alan Morningstar, Roger G. Melko (163):1−17, 2018 PDF BibTeX

• pomegranate: Fast and Flexible Probabilistic Modeling in Python

  Jacob Schreiber (164):1−6, 2018 PDF BibTeX

• Maximum Principle Based Algorithms for Deep Learning

  Qianxiao Li, Long Chen, Cheng Tai, Weinan E (165):1−29, 2018 PDF BibTeX

• Gradient Hard Thresholding Pursuit

  Xiao-Tong Yuan, Ping Li, Tong Zhang (166):1−43, 2018 PDF BibTeX

• Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

  Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone (167):1−51, 2018 PDF BibTeX

• Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition

  Siqi Wu, Bin Yu (168):1−56, 2018 PDF BibTeX

• In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics

  Fred Morstatter, Huan Liu (169):1−32, 2018 PDF BibTeX

• On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness

  Fabrizio Angiulli (170):1−60, 2018 PDF BibTeX

• Convergence of Unregularized Online Learning Algorithms

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• Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation

  Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura (172):1−38, 2018 PDF BibTeX

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  Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Bj\"{o}rn Schuller (173):1−5, 2018 PDF BibTeX

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