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]




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