JMLR 2019

184 papers

Why do deep convolutional networks generalize so poorly to small image transformations?

Aharon Azulay, Yair Weiss

Log-concave sampling: Metropolis-Hastings algorithms are fast

Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu

Model Selection in Bayesian Neural Networks via Horseshoe Priors

Soumya Ghosh, Jiayu Yao, Finale Doshi-velez

Neural Empirical Bayes

Saeed Saremi, Aapo Hyvärinen

DPPy: DPP Sampling with Python

Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko

Differentiable reservoir computing

Lyudmila Grigoryeva, Juan-Pablo Ortega

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Aaron Fisher, Cynthia Rudin, Francesca Dominici

New Convergence Aspects of Stochastic Gradient Algorithms

Lam M. Nguyen, PHUONG HA NGUYEN, Peter Richtarik, Katya Scheinberg, Martin Takac

DataWig: Missing Value Imputation for Tables

Felix Biessmann, Tammo Rukat, Phillipp Schmidt, Prathik Naidu, Sebastian Schelter

Learning Overcomplete, Low Coherence Dictionaries with Linear Inference

Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer

Fast Automatic Smoothing for Generalized Additive Models

Yousra El-Bachir, Anthony C. Davison

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

Andrew Cotter, Heinrich Jiang, Maya Gupta, Serena Wang, Taman Narayan

Shared Subspace Models for Multi-Group Covariance Estimation

Alexander M. Franks, Peter Hoff

DBSCAN: Optimal Rates For Density-Based Cluster Estimation

Daren Wang, Xinyang Lu, Alessandro Rinaldo

Embarrassingly Parallel Inference for Gaussian Processes

Michael Minyi Zhang, Sinead A. Williamson

Determinantal Point Processes for Coresets

Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard

Stochastic Canonical Correlation Analysis

Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang

Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions

Mehmet Eren Ahsen, Robert M Vogel, Gustavo A Stolovitzky

On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size

Francois Kamper, Sarel J. Steel, Johan A. du Preez

The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks

Arjun Sondhi, Ali Shojaie

Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets

Jie Wang, Zhanqiu Zhang, Jieping Ye

A Kernel Multiple Change-point Algorithm via Model Selection

Sylvain Arlot, Alain Celisse, Zaïd Harchaoui

Sparse Kernel Regression with Coefficient-based ℓ_q-regularization

Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens

Learning by Unsupervised Nonlinear Diffusion

Mauro Maggioni, James M. Murphy

Optimal Convergence Rates for Convex Distributed Optimization in Networks

Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié

GraSPy: Graph Statistics in Python

Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm

Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming

Ali Ahmed, Alireza Aghasi, Paul Hand

SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang

Quantifying Uncertainty in Online Regression Forests

Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström

Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems

Koulik Khamaru, Martin J. Wainwright

Approximation Algorithms for Stochastic Clustering

David G. Harris, Shi Li, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh

High-dimensional Varying Index Coefficient Models via Stein's Identity

Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar

Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams

Vasileios Maroulas, Joshua L Mike, Christopher Oballe

Learning Optimized Risk Scores

Berk Ustun, Cynthia Rudin

On Asymptotic and Finite-Time Optimality of Bayesian Predictors

Daniil Ryabko

Collective Matrix Completion

Mokhtar Z. Alaya, Olga Klopp

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

Characterizing the Sample Complexity of Pure Private Learners

Amos Beimel, Kobbi Nissim, Uri Stemmer

Bayesian Optimization for Policy Search via Online-Offline Experimentation

Benjamin Letham, Eytan Bakshy

Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model

Bin Li, Yik-Chung Wu

Minimal Sample Subspace Learning: Theory and Algorithms

Zhenyue Zhang, Yuqing Xia

Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA

Ji Chen, Xiaodong Li

Provably Accurate Double-Sparse Coding

Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde

Nonparametric Bayesian Aggregation for Massive Data

Zuofeng Shang, Botao Hao, Guang Cheng

Decentralized Dictionary Learning Over Time-Varying Digraphs

Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler

Generalized Maximum Entropy Estimation

Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros

Multiclass Boosting: Margins, Codewords, Losses, and Algorithms

Mohammad Saberian, Nuno Vasconcelos

Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis

Nicolás García Trillos, Daniel Sanz-Alonso, Ruiyi Yang

Gaussian Processes with Linear Operator Inequality Constraints

Christian Agrell

Stochastic Variance-Reduced Cubic Regularization Methods

Dongruo Zhou, Pan Xu, Quanquan Gu

Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes

Luca Venturi, Afonso S. Bandeira, Joan Bruna

More Efficient Estimation for Logistic Regression with Optimal Subsamples

HaiYing Wang

Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model

Sophie Burkhardt, Stefan Kramer

Logical Explanations for Deep Relational Machines Using Relevance Information

Ashwin Srinivasan, Lovekesh Vig, Michael Bain

Time-to-Event Prediction with Neural Networks and Cox Regression

Håvard Kvamme, Ørnulf Borgan, Ida Scheel

Unsupervised Basis Function Adaptation for Reinforcement Learning

Edward Barker, Charl Ras

Causal Learning via Manifold Regularization

Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee

Learning Representations of Persistence Barcodes

Christoph D. Hofer, Roland Kwitt, Marc Niethammer

ORCA: A Matlab/Octave Toolbox for Ordinal Regression

Javier Sánchez-Monedero, Pedro A. Gutiérrez, Maria Perez-Ortiz

Deep Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen

ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM

Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy

Approximate Profile Maximum Likelihood

Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction

Bin Hong, Weizhong Zhang, Wei Liu, Jieping Ye, Deng Cai

Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces

Stephen Page, Steffen Grünewälder

Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models

Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu

Binarsity: a penalization for one-hot encoded features in linear supervised learning

Mokhtar Z. Alaya, Simon Bussy, Stephane Gaiffas, Agathe Guilloux

Generic Inference in Latent Gaussian Process Models

Edwin V. Bonilla, Karl Krauth, Amir Dezfouli

Graph Reduction with Spectral and Cut Guarantees

Andreas Loukas

Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models

Yuqi Gu, Gongjun Xu

Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery

Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei

Distributed Inference for Linear Support Vector Machine

Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu

Measuring the Effects of Data Parallelism on Neural Network Training

Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-dickstein, Roy Frostig

An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search

Abhishek Kaul, Venkata K. Jandhyala, Stergios B. Fotopoulos

A Representer Theorem for Deep Neural Networks

Michael Unser

Learning Unfaithful K-separable Gaussian Graphical Models

De Wen Soh, sekhar tatikonda

Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling

Omer Weissbrod, Shachar Kaufman, David Golan, Saharon Rosset

Scalable Interpretable Multi-Response Regression via SEED

Zemin Zheng, M. Taha Bahadori, Yan Liu, Jinchi Lv

Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method

Ziyan Luo, Defeng Sun, Kim-Chuan Toh, Naihua Xiu

Optimal Transport: Fast Probabilistic Approximation with Exact Solvers

Max Sommerfeld, Jörn Schrieber, Yoav Zemel, Axel Munk

Complete Search for Feature Selection in Decision Trees

Salvatore Ruggieri

Regularization via Mass Transportation

Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani

Non-Convex Matrix Completion and Related Problems via Strong Duality

Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang

Low Permutation-rank Matrices: Structural Properties and Noisy Completion

Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright

Hamiltonian Monte Carlo with Energy Conserving Subsampling

Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson

Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data

Wenjing Liao, Mauro Maggioni

Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

PyOD: A Python Toolbox for Scalable Outlier Detection

Yue Zhao, Zain Nasrullah, Zheng Li

High-Dimensional Poisson Structural Equation Model Learning via ℓ_1-Regularized Regression

Gunwoong Park, Sion Park

Simultaneous Private Learning of Multiple Concepts

Mark Bun, Kobbi Nissim, Uri Stemmer

iNNvestigate Neural Networks!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt

AffectiveTweets: a Weka Package for Analyzing Affect in Tweets

Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad

Best Arm Identification for Contaminated Bandits

Jason Altschuler, Victor-Emmanuel Brunel, Alan Malek

A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization

Muhammad A Masood, Finale Doshi-velez

Dependent relevance determination for smooth and structured sparse regression

Anqi Wu, Oluwasanmi Koyejo, Jonathan Pillow

Model Selection via the VC Dimension

Merlin Mpoudeu, Bertrand Clarke

An asymptotic analysis of distributed nonparametric methods

Botond Szabo, Harry van Zanten

Streaming Principal Component Analysis From Incomplete Data

Armin eftekhari, Gregory Ongie, Laura Balzano, Michael B. Wakin

Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees

Leonardo V. Teixeira, Renato M. Assunção, Rosangela H. Loschi