JMLR 2018

79 papers

A Constructive Approach to $l_0$ Penalized Regression Jian Huang, Yuling Jiao, Yanyan Liu, Xiliang Lu

A Direct Approach for Sparse Quadratic Discriminant Analysis Binyan Jiang, Xiangyu Wang, Chenlei Leng

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference Ahmed M. Alaa, Mihaela van der Schaar

A New and Flexible Approach to the Analysis of Paired Comparison Data Ivo F. D. Oliveira, Nir Ailon, Ori Davidov

A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz

A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data Xiaoyi Mai

A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization Ruidi Chen, Ioannis Ch. Paschalidis

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations Chen Chen, Min Ren, Min Zhang, Dabao Zhang

Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization Tomoyuki Obuchi, Yoshiyuki Kabashima

An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach Julien Ah-Pine

An Efficient Distributed Learning Algorithm Based on Effective Local Functional Approximations Dhruv Mahajan, Nikunj Agrawal, S. Sathiya Keerthi, Sundararajan Sellamanickam, Leon Bottou

Approximate Submodularity and Its Applications: Subset Selection, Sparse Approximation and Dictionary Selection Abhimanyu Das, David Kempe

Can We Trust the Bootstrap in High-Dimensions? the Case of Linear Models Noureddine El Karoui, Elizabeth Purdom

Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points Leland Bybee, Yves Atchadé

Clustering Is Semidefinitely Not That Hard: Nonnegative SDP for Manifold Disentangling Mariano Tepper, Anirvan M. Sengupta, Dmitri Chklovskii

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf

Covariances, Robustness, and Variational Bayes Ryan Giordano, Tamara Broderick, Michael I. Jordan

DALEX: Explainers for Complex Predictive Models in R Przemyslaw Biecek

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations Maziar Raissi

Design and Analysis of the NIPS 2016 Review Process Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing

Distribution-Specific Hardness of Learning Neural Networks Ohad Shamir

Dual Principal Component Pursuit Manolis C. Tsakiris, René Vidal

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner, Manfred Opper

Emergence of Invariance and Disentanglement in Deep Representations Alessandro Achille, Stefano Soatto

Experience Selection in Deep Reinforcement Learning for Control Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška

Extrapolating Expected Accuracies for Large Multi-Class Problems Charles Zheng, Rakesh Achanta, Yuval Benjamini

Fast MCMC Sampling Algorithms on Polytopes Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu

Generalized Rank-Breaking: Computational and Statistical Tradeoffs Ashish Khetan, Sewoong Oh

Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials Chao Gao

Gradient Descent Learns Linear Dynamical Systems Moritz Hardt, Tengyu Ma, Benjamin Recht

Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery Christian Kümmerle, Juliane Sigl

Hinge-Minimax Learner for the Ensemble of Hyperplanes Dolev Raviv, Tamir Hazan, Margarita Osadchy

How Deep Are Deep Gaussian Processes? Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup

Importance Sampling for Minibatches Dominik Csiba, Peter Richtárik

Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods Remi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

Inference via Low-Dimensional Couplings Alessio Spantini, Daniele Bigoni, Youssef Marzouk

Invariant Models for Causal Transfer Learning Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl

Kernel Density Estimation for Dynamical Systems Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A.K. Suykens

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Learning from Comparisons and Choices Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

Local Rademacher Complexity-Based Learning Guarantees for Multi-Task Learning Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos

Markov Blanket and Markov Boundary of Multiple Variables Xu-Qing Liu, Xin-Sheng Liu

Maximum Selection and Sorting with Adversarial Comparators Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

Model-Free Trajectory-Based Policy Optimization with Monotonic Improvement Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann

Modular Proximal Optimization for Multidimensional Total-Variation Regularization Alvaro Barbero, Suvrit Sra

Multivariate Bayesian Structural Time Series Model Jinwen Qiu, S. Rao Jammalamadaka, Ning Ning

Numerical Analysis near Singularities in RBF Networks Weili Guo, Haikun Wei, Yew-Soon Ong, Jaime Rubio Hervas, Junsheng Zhao, Hai Wang, Kanjian Zhang

On Generalized Bellman Equations and Temporal-Difference Learning Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton

On Semiparametric Exponential Family Graphical Models Zhuoran Yang, Yang Ning, Han Liu

On Tight Bounds for the Lasso Sara van de Geer

Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator Yixin Fang, Jinfeng Xu, Lei Yang

Optimal Bounds for Johnson-Lindenstrauss Transformations Michael Burr, Shuhong Gao, Fiona Knoll

Optimal Quantum Sample Complexity of Learning Algorithms Srinivasan Arunachalam, Ronald de Wolf

Parallelizing Spectrally Regularized Kernel Algorithms Nicole Mücke, Gilles Blanchard

Patchwork Kriging for Large-Scale Gaussian Process Regression Chiwoo Park, Daniel Apley

Profile-Based Bandit with Unknown Profiles Sylvain Lamprier, Thibault Gisselbrecht, Patrick Gallinari

Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem Timothy C. Au

Refining the Confidence Level for Optimistic Bandit Strategies Tor Lattimore

Regularized Optimal Transport and the Rot Mover's Distance Arnaud Dessein, Nicolas Papadakis, Jean-Luc Rouas

Reverse Iterative Volume Sampling for Linear Regression Michał Dereziński, Manfred K. Warmuth

Robust PCA by Manifold Optimization Teng Zhang, Yi Yang

Robust Synthetic Control Muhammad Amjad, Devavrat Shah, Dennis Shen

RSG: Beating Subgradient Method Without Smoothness and Strong Convexity Tianbao Yang, Qihang Lin

Scalable Bayes via Barycenter in Wasserstein Space Sanvesh Srivastava, Cheng Li, David B. Dunson

Scaling up Data Augmentation MCMC via Calibration Leo L. Duan, James E. Johndrow, David B. Dunson

Short-Term Sparse Portfolio Optimization Based on Alternating Direction Method of Multipliers Zhao-Rong Lai, Pei-Yi Yang, Liangda Fang, Xiaotian Wu

Simple Classification Using Binary Data Deanna Needell, Rayan Saab, Tina Woolf

Sparse Estimation in Ising Model via Penalized Monte Carlo Methods Blazej Miasojedow, Wojciech Rejchel

State-by-State Minimax Adaptive Estimation for Nonparametric Hidden Markov Models Luc Lehéricy

Statistical Analysis and Parameter Selection for Mapper Mathieu Carrière, Bertrand Michel, Steve Oudot

Streaming Kernel Regression with Provably Adaptive Mean, Variance, and Regularization Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau

The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro

The Xyz Algorithm for Fast Interaction Search in High-Dimensional Data Gian-Andrea Thanei, Nicolai Meinshausen, Rajen D. Shah

Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier Alain Celisse, Tristan Mary-Huard

Universal Discrete-Time Reservoir Computers with Stochastic Inputs and Linear Readouts Using Non-Homogeneous State-Affine Systems Lyudmila Grigoryeva, Juan-Pablo Ortega

Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations Kai-Yang Chiang, Inderjit S. Dhillon, Cho-Jui Hsieh