JMLR Volume 18

Averaged Collapsed Variational Bayes Inference
Katsuhiko Ishiguro, Issei Sato, Naonori Ueda; 18(1):1−29, 2017.
[abs][pdf][bib]

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; 18(2):1−45, 2017.
[abs][pdf][bib]

Local algorithms for interactive clustering
Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski; 18(3):1−35, 2017.
[abs][pdf][bib]

SnapVX: A Network-Based Convex Optimization Solver
David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; 18(4):1−5, 2017.
[abs][pdf][bib]    [code][stanford.edu]

Communication-efficient Sparse Regression
Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor; 18(5):1−30, 2017.
[abs][pdf][bib]

Improving Variational Methods via Pairwise Linear Response Identities
Jack Raymond, Federico Ricci-Tersenghi; 18(6):1−36, 2017.
[abs][pdf][bib]

Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Adam S. Charles, Dong Yin, Christopher J. Rozell; 18(7):1−37, 2017.
[abs][pdf][bib]

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; 18(8):1−35, 2017.
[abs][pdf][bib]

Spectral Clustering Based on Local PCA
Ery Arias-Castro, Gilad Lerman, Teng Zhang; 18(9):1−57, 2017.
[abs][pdf][bib]

On Perturbed Proximal Gradient Algorithms
Yves F. Atchadé, Gersende Fort, Eric Moulines; 18(10):1−33, 2017.
[abs][pdf][bib]

Differential Privacy for Bayesian Inference through Posterior Sampling
Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein; 18(11):1−39, 2017.
[abs][pdf][bib]

Refinery: An Open Source Topic Modeling Web Platform
Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; 18(12):1−5, 2017.
[abs][pdf][bib]    [code][github]

Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns
Herbert Jaeger; 18(13):1−43, 2017.
[abs][pdf][bib]    [supplementary]

Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei; 18(14):1−45, 2017.
[abs][pdf][bib]

Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters
Jacques Wainer, Gavin Cawley; 18(15):1−35, 2017.
[abs][pdf][bib]

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification
Naoki Ito, Akiko Takeda, Kim-Chuan Toh; 18(16):1−49, 2017.
[abs][pdf][bib]

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; 18(17):1−5, 2017.
[abs][pdf][bib]    [code][contrib.scikit-learn.org]

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen; 18(18):1−65, 2017.
[abs][pdf][bib]

Breaking the Curse of Dimensionality with Convex Neural Networks
Francis Bach; 18(19):1−53, 2017.
[abs][pdf][bib]

Memory Efficient Kernel Approximation
Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon; 18(20):1−32, 2017.
[abs][pdf][bib]

On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
Francis Bach; 18(21):1−38, 2017.
[abs][pdf][bib]

Analyzing Tensor Power Method Dynamics in Overcomplete Regime
Animashree Anandkumar, Rong Ge, Majid Janzamin; 18(22):1−40, 2017.
[abs][pdf][bib]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
Edward Raff; 18(23):1−5, 2017.
[abs][pdf][bib]    [code][github]

Identifying a Minimal Class of Models for High--dimensional Data
Daniel Nevo, Ya'acov Ritov; 18(24):1−29, 2017.
[abs][pdf][bib]

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; 18(25):1−5, 2017.
[abs][pdf][bib]    [code][ubc.ca]

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; 18(26):1−5, 2017.
[abs][pdf][bib]    [code][github]

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes
François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy; 18(27):1−32, 2017.
[abs][pdf][bib]

Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth; 18(28):1−39, 2017.
[abs][pdf][bib]

Certifiably Optimal Low Rank Factor Analysis
Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder; 18(29):1−53, 2017.
[abs][pdf][bib]

Group Sparse Optimization via lp,q Regularization
Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang; 18(30):1−52, 2017.
[abs][pdf][bib]

Preference-based Teaching
Ziyuan Gao, Christoph Ries, Hans U. Simon, Sandra Zilles; 18(31):1−32, 2017.
[abs][pdf][bib]

Nonparametric Risk Bounds for Time-Series Forecasting
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish; 18(32):1−40, 2017.
[abs][pdf][bib]

Online Bayesian Passive-Aggressive Learning
Tianlin Shi, Jun Zhu; 18(33):1−39, 2017.
[abs][pdf][bib]

Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning
Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy; 18(34):1−41, 2017.
[abs][pdf][bib]

A Spectral Algorithm for Inference in Hidden semi-Markov Models
Igor Melnyk, Arindam Banerjee; 18(35):1−39, 2017.
[abs][pdf][bib]

Simplifying Probabilistic Expressions in Causal Inference
Santtu Tikka, Juha Karvanen; 18(36):1−30, 2017.
[abs][pdf][bib]

Nearly optimal classification for semimetrics
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch; 18(37):1−22, 2017.
[abs][pdf][bib]

Bridging Supervised Learning and Test-Based Co-optimization
Elena Popovici; 18(38):1−39, 2017.
[abs][pdf][bib]    [appendix]

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; 18(39):1−5, 2017.
[abs][pdf][bib]    [code][r-project.org]

GPflow: A Gaussian Process Library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman; 18(40):1−6, 2017.
[abs][pdf][bib]    [code][github]

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; 18(41):1−49, 2017.
[abs][pdf][bib]

Learning Local Dependence In Ordered Data
Guo Yu, Jacob Bien; 18(42):1−60, 2017.
[abs][pdf][bib]

Bayesian Learning of Dynamic Multilayer Networks
Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts; 18(43):1−29, 2017.
[abs][pdf][bib]

Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality
Samory Kpotufe, Nakul Verma; 18(44):1−29, 2017.
[abs][pdf][bib]

Asymptotic behavior of Support Vector Machine for spiked population model
Hanwen Huang; 18(45):1−21, 2017.
[abs][pdf][bib]

Distributed Semi-supervised Learning with Kernel Ridge Regression
Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou; 18(46):1−22, 2017.
[abs][pdf][bib]

On Markov chain Monte Carlo methods for tall data
Rémi Bardenet, Arnaud Doucet, Chris Holmes; 18(47):1−43, 2017.
[abs][pdf][bib]

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease; 18(48):1−33, 2017.
[abs][pdf][bib]

Clustering from General Pairwise Observations with Applications to Time-varying Graphs
Shiau Hong Lim, Yudong Chen, Huan Xu; 18(49):1−47, 2017.
[abs][pdf][bib]

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
Debarghya Ghoshdastidar, Ambedkar Dukkipati; 18(50):1−41, 2017.
[abs][pdf][bib]

Reconstructing Undirected Graphs from Eigenspaces
Yohann De Castro, Thibault Espinasse, Paul Rochet; 18(51):1−24, 2017.
[abs][pdf][bib]

An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
Ohad Shamir; 18(52):1−11, 2017.
[abs][pdf][bib]

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
Adel Javanmard; 18(53):1−31, 2017.
[abs][pdf][bib]

Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar; 18(54):1−24, 2017.
[abs][pdf][bib]

On the Consistency of Ordinal Regression Methods
Fabian Pedregosa, Francis Bach, Alexandre Gramfort; 18(55):1−35, 2017.
[abs][pdf][bib]

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences
Takashi Takenouchi, Takafumi Kanamori; 18(56):1−26, 2017.
[abs][pdf][bib]

Density Estimation in Infinite Dimensional Exponential Families
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar; 18(57):1−59, 2017.
[abs][pdf][bib]

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
Matthäus Kleindessner, Ulrike von Luxburg; 18(58):1−52, 2017.
[abs][pdf][bib]

Joint Label Inference in Networks
Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy; 18(59):1−39, 2017.
[abs][pdf][bib]

Achieving Optimal Misclassification Proportion in Stochastic Block Models
Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou; 18(60):1−45, 2017.
[abs][pdf][bib]

On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks
Prakash Balachandran, Eric D. Kolaczyk, Weston D. Viles; 18(61):1−33, 2017.
[abs][pdf][bib]

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas; 18(62):1−58, 2017.
[abs][pdf][bib]

Fundamental Conditions for Low-CP-Rank Tensor Completion
Morteza Ashraphijuo, Xiaodong Wang; 18(63):1−29, 2017.
[abs][pdf][bib]

Parallel Symmetric Class Expression Learning
An C. Tran, Jens Dietrich, Hans W. Guesgen, Stephen Marsland; 18(64):1−34, 2017.
[abs][pdf][bib]

Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning
Jervis Pinto, Alan Fern; 18(65):1−35, 2017.
[abs][pdf][bib]

Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, Vikas Sindhwani; 18(66):1−42, 2017.
[abs][pdf][bib]

Sharp Oracle Inequalities for Square Root Regularization
Benjamin Stucky, Sara van de Geer; 18(67):1−29, 2017.
[abs][pdf][bib]

Soft Margin Support Vector Classification as Buffered Probability Minimization
Matthew Norton, Alexander Mafusalov, Stan Uryasev; 18(68):1−43, 2017.
[abs][pdf][bib]

Variational Particle Approximations
Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman; 18(69):1−29, 2017.
[abs][pdf][bib]

A Bayesian Framework for Learning Rule Sets for Interpretable Classification
Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille; 18(70):1−37, 2017.
[abs][pdf][bib]

A Robust-Equitable Measure for Feature Ranking and Selection
A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang; 18(71):1−46, 2017.
[abs][pdf][bib]

Multiscale Strategies for Computing Optimal Transport
Samuel Gerber, Mauro Maggioni; 18(72):1−32, 2017.
[abs][pdf][bib]

Non-parametric Policy Search with Limited Information Loss
Herke van Hoof, Gerhard Neumann, Jan Peters; 18(73):1−46, 2017.
[abs][pdf][bib]

Tests of Mutual or Serial Independence of Random Vectors with Applications
Martin Bilodeau, Aurélien Guetsop Nangue; 18(74):1−40, 2017.
[abs][pdf][bib]    [supplementary]

Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail; 18(75):1−34, 2017.
[abs][pdf][bib]

Quantifying the Informativeness of Similarity Measurements
Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas; 18(76):1−61, 2017.
[abs][pdf][bib]

Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis
Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon; 18(77):1−36, 2017.
[abs][pdf][bib]

Relational Reinforcement Learning for Planning with Exogenous Effects
David Martínez, Guillem Alenyà, Tony Ribeiro, Katsumi Inoue, Carme Torras; 18(78):1−44, 2017.
[abs][pdf][bib]

Bayesian Tensor Regression
Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson; 18(79):1−31, 2017.
[abs][pdf][bib]

Robust Discriminative Clustering with Sparse Regularizers
Nicolas Flammarion, Balamurugan Palaniappan, Francis Bach; 18(80):1−50, 2017.
[abs][pdf][bib]

Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions
Weiwei Liu, Ivor W. Tsang; 18(81):1−36, 2017.
[abs][pdf][bib]

Learning Scalable Deep Kernels with Recurrent Structure
Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing; 18(82):1−37, 2017.
[abs][pdf][bib]

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Vardan Papyan, Yaniv Romano, Michael Elad; 18(83):1−52, 2017.
[abs][pdf][bib]

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Lin Xiao; 18(84):1−42, 2017.
[abs][pdf][bib]

Angle-based Multicategory Distance-weighted SVM
Hui Sun, Bruce A. Craig, Lingsong Zhang; 18(85):1−21, 2017.
[abs][pdf][bib]

Minimax Estimation of Kernel Mean Embeddings
Ilya Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet; 18(86):1−47, 2017.
[abs][pdf][bib]

The Impact of Random Models on Clustering Similarity
Alexander J. Gates, Yong-Yeol Ahn; 18(87):1−28, 2017.
[abs][pdf][bib]

Hierarchical Clustering via Spreading Metrics
Aurko Roy, Sebastian Pokutta; 18(88):1−35, 2017.
[abs][pdf][bib]

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, João V. Messias; 18(89):1−5, 2017.
[abs][pdf][bib]    [code][github]

A survey of Algorithms and Analysis for Adaptive Online Learning
H. Brendan McMahan; 18(90):1−50, 2017.
[abs][pdf][bib]

A distributed block coordinate descent method for training l1 regularized linear classifiers
Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan; 18(91):1−35, 2017.
[abs][pdf][bib]

Distributed Learning with Regularized Least Squares
Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou; 18(92):1−31, 2017.
[abs][pdf][bib]

Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman; 18(93):1−67, 2017.
[abs][pdf][bib]

An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller; 18(94):1−38, 2017.
[abs][pdf][bib]

Fisher Consistency for Prior Probability Shift
Dirk Tasche; 18(95):1−32, 2017.
[abs][pdf][bib]

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Maximilian Schmitt, Björn Schuller; 18(96):1−5, 2017.
[abs][pdf][bib]    [code][github]

Optimal Rates for Multi-pass Stochastic Gradient Methods
Junhong Lin, Lorenzo Rosasco; 18(97):1−47, 2017.
[abs][pdf][bib]

Rank Determination for Low-Rank Data Completion
Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal; 18(98):1−29, 2017.
[abs][pdf][bib]

Bayesian Network Learning via Topological Order
Young Woong Park, Diego Klabjan; 18(99):1−32, 2017.
[abs][pdf][bib]

Stability of Controllers for Gaussian Process Dynamics
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters; 18(100):1−37, 2017.
[abs][pdf][bib]

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach; 18(101):1−51, 2017.
[abs][pdf][bib]

Confidence Sets with Expected Sizes for Multiclass Classification
Christophe Denis, Mohamed Hebiri; 18(102):1−28, 2017.
[abs][pdf][bib]

Online Learning to Rank with Top-k Feedback
Sougata Chaudhuri, Ambuj Tewari; 18(103):1−50, 2017.
[abs][pdf][bib]

A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Thang D. Bui, Josiah Yan, Richard E. Turner; 18(104):1−72, 2017.
[abs][pdf][bib]

Accelerating Stochastic Composition Optimization
Mengdi Wang, Ji Liu, Ethan X. Fang; 18(105):1−23, 2017.
[abs][pdf][bib]

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; 18(106):1−37, 2017.
[abs][pdf][bib]

Optimal Dictionary for Least Squares Representation
Mohammed Rayyan Sheriff, Debasish Chatterjee; 18(107):1−28, 2017.
[abs][pdf][bib]

Computational Limits of A Distributed Algorithm for Smoothing Spline
Zuofeng Shang, Guang Cheng; 18(108):1−37, 2017.
[abs][pdf][bib]

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor; 18(109):1−67, 2017.
[abs][pdf][bib]

Clustering with Hidden Markov Model on Variable Blocks
Lin Lin, Jia Li; 18(110):1−49, 2017.
[abs][pdf][bib]

Approximation Vector Machines for Large-scale Online Learning
Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung; 18(111):1−55, 2017.
[abs][pdf][bib]

Efficient Sampling from Time-Varying Log-Concave Distributions
Hariharan Narayanan, Alexander Rakhlin; 18(112):1−29, 2017.
[abs][pdf][bib]

Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alexandre Allauzen, Hugo Larochelle; 18(113):1−24, 2017.
[abs][pdf][bib]

Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks
Shannon Fenn, Pablo Moscato; 18(114):1−26, 2017.
[abs][pdf][bib]

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang; 18(115):1−52, 2017.
[abs][pdf][bib]

Second-Order Stochastic Optimization for Machine Learning in Linear Time
Naman Agarwal, Brian Bullins, Elad Hazan; 18(116):1−40, 2017.
[abs][pdf][bib]

Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
Jiahe Lin, George Michailidis; 18(117):1−49, 2017.
[abs][pdf][bib]

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zheng-Chu Guo, Lei Shi, Qiang Wu; 18(118):1−25, 2017.
[abs][pdf][bib]

Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig; 18(119):1−59, 2017.
[abs][pdf][bib]

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
Ricardo Silva, Shohei Shimizu; 18(120):1−49, 2017.
[abs][pdf][bib]

Classification of Time Sequences using Graphs of Temporal Constraints
Mathieu Guillame-Bert, Artur Dubrawski; 18(121):1−34, 2017.
[abs][pdf][bib]

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang; 18(122):1−43, 2017.
[abs][pdf][bib]

Kernel Partial Least Squares for Stationary Data
Marco Singer, Tatyana Krivobokova, Axel Munk; 18(123):1−41, 2017.
[abs][pdf][bib]

Robust and Scalable Bayes via a Median of Subset Posterior Measures
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson; 18(124):1−40, 2017.
[abs][pdf][bib]

Statistical and Computational Guarantees for the Baum-Welch Algorithm
Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright; 18(125):1−53, 2017.
[abs][pdf][bib]

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
Christophe Dupuy, Francis Bach; 18(126):1−45, 2017.
[abs][pdf][bib]

Poisson Random Fields for Dynamic Feature Models
Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh; 18(127):1−45, 2017.
[abs][pdf][bib]

Gap Safe Screening Rules for Sparsity Enforcing Penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon; 18(128):1−33, 2017.
[abs][pdf][bib]

Minimax Filter: Learning to Preserve Privacy from Inference Attacks
Jihun Hamm; 18(129):1−31, 2017.
[abs][pdf][bib]

Knowledge Graph Completion via Complex Tensor Factorization
Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard; 18(130):1−38, 2017.
[abs][pdf][bib]

Stabilized Sparse Online Learning for Sparse Data
Yuting Ma, Tian Zheng; 18(131):1−36, 2017.
[abs][pdf][bib]

Active-set Methods for Submodular Minimization Problems
K. S. Sesh Kumar, Francis Bach; 18(132):1−31, 2017.
[abs][pdf][bib]

A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations
Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, Stanley Durrleman; 18(133):1−33, 2017.
[abs][pdf][bib]

Stochastic Gradient Descent as Approximate Bayesian Inference
Stephan Mandt, Matthew D. Hoffman, David M. Blei; 18(134):1−35, 2017.
[abs][pdf][bib]

STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Will Wei Sun, Lexin Li; 18(135):1−37, 2017.
[abs][pdf][bib]

A Survey of Preference-Based Reinforcement Learning Methods
Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz; 18(136):1−46, 2017.
[abs][pdf][bib]

Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising
Jérémie Bigot, Charles Deledalle, Delphine Féral; 18(137):1−50, 2017.
[abs][pdf][bib]

Dimension Estimation Using Random Connection Models
Paulo Serra, Michel Mandjes; 18(138):1−35, 2017.
[abs][pdf][bib]

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen; 18(139):1−58, 2017.
[abs][pdf][bib]

Adaptive Randomized Dimension Reduction on Massive Data
Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E Engelhardt; 18(140):1−30, 2017.
[abs][pdf][bib]

A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
Huishuai Zhang, Yingbin Liang, Yuejie Chi; 18(141):1−35, 2017.
[abs][pdf][bib]

Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
Pietro Coretto, Christian Hennig; 18(142):1−39, 2017.
[abs][pdf][bib]

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Yining Wang, Adams Wei Yu, Aarti Singh; 18(143):1−41, 2017.
[abs][pdf][bib]

Generalized Conditional Gradient for Sparse Estimation
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans; 18(144):1−46, 2017.
[abs][pdf][bib]

Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári; 18(145):1−31, 2017.
[abs][pdf][bib]

Regularization and the small-ball method II: complexity dependent error rates
Guillaume Lecué, Shahar Mendelson; 18(146):1−48, 2017.
[abs][pdf][bib]

Matrix Completion with Noisy Entries and Outliers
Raymond K. W. Wong, Thomas C. M. Lee; 18(147):1−25, 2017.
[abs][pdf][bib]

Faithfulness of Probability Distributions and Graphs
Kayvan Sadeghi; 18(148):1−29, 2017.
[abs][pdf][bib]

Community Extraction in Multilayer Networks with Heterogeneous Community Structure
James D. Wilson, John Palowitch, Shankar Bhamidi, Andrew B. Nobel; 18(149):1−49, 2017.
[abs][pdf][bib]




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