JMLR 2017

149 papers

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

Shiau Hong Lim, Yudong Chen, Huan Xu

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease

On Markov chain Monte Carlo methods for tall data

Rémi Bardenet, Arnaud Doucet, Chris Holmes

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou

Asymptotic behavior of Support Vector Machine for spiked population model

Hanwen Huang

Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality

Samory Kpotufe, Nakul Verma

Bayesian Learning of Dynamic Multilayer Networks

Daniele Durante, Nabanita Mukherjee, Rebecca C. Steorts

Learning Local Dependence In Ordered Data

Guo Yu, Jacob Bien

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

Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha

GPflow: A Gaussian Process Library using TensorFlow

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas

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

Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski

Bridging Supervised Learning and Test-Based Co-optimization

Elena Popovici

Nearly optimal classification for semimetrics

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Simplifying Probabilistic Expressions in Causal Inference

Santtu Tikka, Juha Karvanen

A Spectral Algorithm for Inference in Hidden semi-Markov Models

Igor Melnyk, Arindam Banerjee

Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning

Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy

Online Bayesian Passive-Aggressive Learning

Tianlin Shi, Jun Zhu

Nonparametric Risk Bounds for Time-Series Forecasting

Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish

Preference-based Teaching

Ziyuan Gao, Christoph Ries, Hans U. Simon, Sandra Zilles

Group Sparse Optimization via lp,q Regularization

Yaohua Hu, Chong Li, Kaiwen Meng, Jing Qin, Xiaoqi Yang

Certifiably Optimal Low Rank Factor Analysis

Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder

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

Alexandre Bouchard-côté, Arnaud Doucet, Andrew Roth

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes

Francois Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy

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

Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta

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

Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-brown

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

Daniel Nevo, Ya'acov Ritov

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

Edward Raff

Analyzing Tensor Power Method Dynamics in Overcomplete Regime

Animashree Anandkumar, Rong Ge, Majid Janzamin

On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions

Francis Bach

Memory Efficient Kernel Approximation

Si Si, Cho-jui Hsieh, Inderjit S. Dhillon

Breaking the Curse of Dimensionality with Convex Neural Networks

Francis Bach

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

Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen

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

Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification

Naoki Ito, Akiko Takeda, Kim-Chuan Toh

Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters

Jacques Wainer, Gavin Cawley

Automatic Differentiation Variational Inference

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

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

Herbert Jaeger

Refinery: An Open Source Topic Modeling Web Platform

Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth

Differential Privacy for Bayesian Inference through Posterior Sampling

Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein

On Perturbed Proximal Gradient Algorithms

Yves F. Atchadé, Gersende Fort, Eric Moulines

Spectral Clustering Based on Local PCA

Ery Arias-Castro, Gilad Lerman, Teng Zhang

Persistence Images: A Stable Vector Representation of Persistent Homology

Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson

Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks

Adam S. Charles, Dong Yin, Christopher J. Rozell

Improving Variational Methods via Pairwise Linear Response Identities

Jack Raymond, Federico Ricci-Tersenghi

Communication-efficient Sparse Regression

Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor

SnapVX: A Network-Based Convex Optimization Solver

David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic

Local algorithms for interactive clustering

Pranjal Awasthi, Maria Florina Balcan, Konstantin Voevodski

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

Nan Du, Yingyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha

Averaged Collapsed Variational Bayes Inference

Katsuhiko Ishiguro, Issei Sato, Naonori Ueda