JMLR 2018

169 papers

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging

Shusen Wang, Alex Gittens, Michael W. Mahoney

Simultaneous Clustering and Estimation of Heterogeneous Graphical Models

Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng

Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks

Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz, Ness B. Shroff

SGDLibrary: A MATLAB library for stochastic optimization algorithms

Hiroyuki Kasai

tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models

Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stephane Gaiffas, Søren V. Poulsen

Gaussian Lower Bound for the Information Bottleneck Limit

Amichai Painsky, Naftali Tishby

Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice

Hongzhou Lin, Julien Mairal, Zaïd Harchaoui

Weighted SGD for ℓ_p Regression with Randomized Preconditioning

Jiyan Yang, Yin-Lam Chow, Christopher Re, Michael W. Mahoney

Sparse Exchangeable Graphs and Their Limits via Graphon Processes

Christian Borgs, Jennifer T. Chayes, Henry Cohn, Nina Holden

Estimation of Graphical Models through Structured Norm Minimization

Davoud Ataee Tarzanagh, George Michailidis

A Tight Bound of Hard Thresholding

Jie Shen, Ping Li

An ℓ_∞ Eigenvector Perturbation Bound and Its Application

Jianqing Fan, Weichen Wang, Yiqiao Zhong

The Search Problem in Mixture Models

Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai

Steering Social Activity: A Stochastic Optimal Control Point Of View

Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez

Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

Quan Zhang, Mingyuan Zhou

Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models

Junwei Lu, Mladen Kolar, Han Liu

Sparse Concordance-assisted Learning for Optimal Treatment Decision

Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang

Exact Learning of Lightweight Description Logic Ontologies

Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter

Surprising properties of dropout in deep networks

David P. Helmbold, Philip M. Long

Simple, Robust and Optimal Ranking from Pairwise Comparisons

Nihar B. Shah, Martin J. Wainwright

Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization

Andrei Patrascu, Ion Necoara

Saturating Splines and Feature Selection

Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael I. Jordan

From Predictive Methods to Missing Data Imputation: An Optimization Approach

Dimitris Bertsimas, Colin Pawlowski, Ying Daisy Zhuo

Active Nearest-Neighbor Learning in Metric Spaces

Aryeh Kontorovich, Sivan Sabato, Ruth Urner

Enhancing Identification of Causal Effects by Pruning

Santtu Tikka, Juha Karvanen

Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research

Jennifer Wortman Vaughan

Uncovering Causality from Multivariate Hawkes Integrated Cumulants

Massil Achab, Emmanuel Bacry, Stephane Gaiffas, Iacopo Mastromatteo, Jean-François Muzy

KELP: a Kernel-based Learning Platform

Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Alessandro Moschitti, Danilo Croce

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

Benjamin Guedj, Bhargav Srinivasa Desikan

Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor

Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka

Significance-based community detection in weighted networks

John Palowitch, Shankar Bhamidi, Andrew B. Nobel

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

Submatrix localization via message passing

Bruce Hajek, Yihong Wu, Jiaming Xu

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits

Zifan Li, Ambuj Tewari

Divide-and-Conquer for Debiased l_1-norm Support Vector Machine in Ultra-high Dimensions

Heng Lian, Zengyan Fan

To Tune or Not to Tune the Number of Trees in Random Forest

Philipp Probst, Anne-Laure Boulesteix

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios

Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Quanming Yao, James T. Kwok

On b-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data

Rajen D. Shah, Nicolai Meinshausen

Community Detection and Stochastic Block Models: Recent Developments

Emmanuel Abbe

The DFS Fused Lasso: Linear-Time Denoising over General Graphs

OSCAR HERNAN MADRID PADILLA, James Sharpnack, James G. Scott, Ryan J. Tibshirani

Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard

Christopher Tosh, Sanjoy Dasgupta

On the Stability of Feature Selection Algorithms

Sarah Nogueira, Konstantinos Sechidis, Gavin Brown

auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller

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

Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

Convergence of Unregularized Online Learning Algorithms

Yunwen Lei, Lei Shi, Zheng-Chu Guo

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

Fabrizio Angiulli

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

Fred Morstatter, Huan Liu

Local Identifiability of ℓ_1-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition

Siqi Wu, Bin Yu

Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone

Gradient Hard Thresholding Pursuit

Xiao-Tong Yuan, Ping Li, Tong Zhang

Maximum Principle Based Algorithms for Deep Learning

Qianxiao Li, Long Chen, Cheng Tai, Weinan E

pomegranate: Fast and Flexible Probabilistic Modeling in Python

Jacob Schreiber

Deep Learning the Ising Model Near Criticality

Alan Morningstar, Roger G. Melko

Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model

Clint P. George, Hani Doss

Gradient Estimation with Simultaneous Perturbation and Compressive Sensing

Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe

Training Gaussian Mixture Models at Scale via Coresets

Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman

Robust Topological Inference: Distance To a Measure and Kernel Distance

Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo

Probabilistic preference learning with the Mallows rank model

Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas

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

Elif Vural, Christine Guillemot

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

Yining Wang, Aarti Singh

Cost-Sensitive Learning with Noisy Labels

Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari

Normal Bandits of Unknown Means and Variances

Wesley Cowan, Junya Honda, Michael N. Katehakis

Automatic Differentiation in Machine Learning: a Survey

Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind

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

Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning

Variational Fourier Features for Gaussian Processes

James Hensman, Nicolas Durrande, Arno Solin

On Binary Embedding using Circulant Matrices

Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-fu Chang