JMLR 2018

169 papers

DALEX: Explainers for Complex Predictive Models in R

Przemyslaw Biecek

Seglearn: A Python Package for Learning Sequences and Time Series

David M. Burns, Cari M. Whyne

Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling

Mariano Tepper, Anirvan M. Sengupta, Dmitri Chklovskii

Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods

Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

Robust PCA by Manifold Optimization

Teng Zhang, Yi Yang

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

Xiaoyi MAI

Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator

Yixin Fang, Jinfeng Xu, Lei Yang

A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation

Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz

Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations

Kai-Yang Chiang, Inderjit S. Dhillon, Cho-jui Hsieh

Sparse Estimation in Ising Model via Penalized Monte Carlo Methods

Blazej Miasojedow, Wojciech Rejchel

An efficient distributed learning algorithm based on effective local functional approximations

Dhruv Mahajan, Nikunj Agrawal, S. Sathiya Keerthi, SUNDARARAJAN SELLAMANICKAM, Léon Bottou

Optimal Bounds for Johnson-Lindenstrauss Transformations

Michael Burr, Shuhong Gao, Fiona Knoll

Scikit-Multiflow: A Multi-output Streaming Framework

Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem

Optimal Quantum Sample Complexity of Learning Algorithms

Srinivasan Arunachalam, Ronald de Wolf

The Implicit Bias of Gradient Descent on Separable Data

Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl

Multivariate Bayesian Structural Time Series Model

Jinwen Qiu, S. Rao Jammalamadaka, Ning Ning

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Christian Donner, Manfred Opper

Inference via Low-Dimensional Couplings

Alessio Spantini, Daniele Bigoni, Youssef Marzouk

Extrapolating Expected Accuracies for Large Multi-Class Problems

Charles Zheng, Rakesh Achanta, Yuval Benjamini

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

Hinge-Minimax Learner for the Ensemble of Hyperplanes

Dolev Raviv, Tamir Hazan, Margarita Osadchy

Simple Classification Using Binary Data

Deanna Needell, Rayan Saab, Tina Woolf

A New and Flexible Approach to the Analysis of Paired Comparison Data

Ivo F. D. Oliveira, Nir Ailon, Ori Davidov

Maximum Selection and Sorting with Adversarial Comparators

Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

Theoretical Analysis of Cross-Validation for Estimating the Risk of the k-Nearest Neighbor Classifier

Alain Celisse, Tristan Mary-Huard

On Semiparametric Exponential Family Graphical Models

Zhuoran Yang, Yang Ning, Han Liu

Modular Proximal Optimization for Multidimensional Total-Variation Regularization

Álvaro Barbero, Suvrit Sra

Fast MCMC Sampling Algorithms on Polytopes

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

How Deep Are Deep Gaussian Processes?

Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup

Profile-Based Bandit with Unknown Profiles

Sylvain Lamprier, Thibault Gisselbrecht, Patrick Gallinari

Accelerating Cross-Validation in Multinomial Logistic Regression with ℓ_1-Regularization

Tomoyuki Obuchi, Yoshiyuki Kabashima

Covariances, Robustness, and Variational Bayes

Ryan Giordano, Tamara Broderick, Michael I. Jordan

Emergence of Invariance and Disentanglement in Deep Representations

Alessandro Achille, Stefano Soatto

Design and Analysis of the NIPS 2016 Review Process

Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike Von Luxburg

On Generalized Bellman Equations and Temporal-Difference Learning

Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton

Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery

Christian Kümmerle, Juliane Sigl

On Tight Bounds for the Lasso

Sara van de Geer

Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem

Timothy C. Au

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Markov Blanket and Markov Boundary of Multiple Variables

Xu-Qing Liu, Xin-Sheng Liu

An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach

Julien Ah-Pine

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models

Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf

Learning from Comparisons and Choices

Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu

State-by-state Minimax Adaptive Estimation for Nonparametric Hidden {M}arkov Models

Luc Lehéricy

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning

Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos

The xyz algorithm for fast interaction search in high-dimensional data

Gian-Andrea Thanei, Nicolai Meinshausen, Rajen D. Shah

Invariant Models for Causal Transfer Learning

Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters

Kernel Density Estimation for Dynamical Systems

Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A.K. Suykens

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

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

Chao Gao

Distribution-Specific Hardness of Learning Neural Networks

Ohad Shamir

A Direct Approach for Sparse Quadratic Discriminant Analysis

Binyan Jiang, Xiangyu Wang, Chenlei Leng

Parallelizing Spectrally Regularized Kernel Algorithms

Nicole Mücke, Gilles Blanchard

Gradient Descent Learns Linear Dynamical Systems

Moritz Hardt, Tengyu Ma, Benjamin Recht

Generalized Rank-Breaking: Computational and Statistical Tradeoffs

Ashish Khetan, Sewoong Oh

Importance Sampling for Minibatches

Dominik Csiba, Peter Richtarik

OpenEnsembles: A Python Resource for Ensemble Clustering

Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky

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

Maziar Raissi

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems

Lyudmila Grigoryeva, Juan-Pablo Ortega

Reverse Iterative Volume Sampling for Linear Regression

Michał Dereziński, Manfred K. Warmuth

Robust Synthetic Control

Muhammad Amjad, Devavrat Shah, Dennis Shen

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen

Refining the Confidence Level for Optimistic Bandit Strategies

Tor Lattimore

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters

Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing

Dual Principal Component Pursuit

Manolis C. Tsakiris, René Vidal

Streaming kernel regression with provably adaptive mean, variance, and regularization

Audrey Durand, Odalric-ambrym Maillard, Joelle Pineau

ELFI: Engine for Likelihood-Free Inference

Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää

Regularized Optimal Transport and the Rot Mover's Distance

Arnaud Dessein, Nicolas Papadakis, Jean-Luc Rouas

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement

Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann

A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization

Ruidi Chen, Ioannis Ch. Paschalidis

Statistical Analysis and Parameter Selection for Mapper

Mathieu Carrière, Bertrand Michel, Steve OUDOT

Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points

Leland Bybee, Yves Atchade

A Constructive Approach to L_0 Penalized Regression

Jian Huang, Yuling Jiao, Yanyan Liu, Xiliang Lu

Experience Selection in Deep Reinforcement Learning for Control

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška

Scalable Bayes via Barycenter in Wasserstein Space

Sanvesh Srivastava, Cheng Li, David B. Dunson

Patchwork Kriging for Large-scale Gaussian Process Regression

Chiwoo Park, Daniel Apley

RSG: Beating Subgradient Method without Smoothness and Strong Convexity

Tianbao Yang, Qihang Lin

Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models

Noureddine El Karoui, Elizabeth Purdom

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

Ahmed M. Alaa, Mihaela van der Schaar

Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection

Abhimanyu Das, David Kempe

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations

Chen Chen, Min Ren, Min Zhang, Dabao Zhang

Numerical Analysis near Singularities in RBF Networks

Weili Guo, Haikun Wei, Yew-Soon Ong, Jaime Rubio Hervas, Junsheng Zhao

Learning Certifiably Optimal Rule Lists for Categorical Data

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin

Characteristic and Universal Tensor Product Kernels

Zoltan Szabo, Bharath K. Sriperumbudur

A Cluster Elastic Net for Multivariate Regression

Bradley S. Price, Ben Sherwood

Concentration inequalities for empirical processes of linear time series

Likai Chen, Wei Biao Wu

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I. Jordan

Interactive Algorithms: Pool, Stream and Precognitive Stream

Sivan Sabato, Tom Hess

A Theory of Learning with Corrupted Labels

Brendan van Rooyen, Robert C. Williamson

Rate of Convergence of k-Nearest-Neighbor Classification Rule

Maik Döring, László Györfi, Harro Walk

Statistical Inference on Random Dot Product Graphs: a Survey

Avanti Athreya, Donniell E. Fishkind, Minh Tang, Carey E. Priebe, Youngser Park

Improved spectral community detection in large heterogeneous networks

Hafiz Tiomoko Ali, Romain Couillet

Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS)

Gunwoong Park, Garvesh Raskutti

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization

Alon Gonen, Shai Shalev-shwartz

Katyusha: The First Direct Acceleration of Stochastic Gradient Methods

Zeyuan Allen-Zhu

Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs

Emilija Perkovic, Johannes Textor, Markus Kalisch, Marloes H. Maathuis

Compact Convex Projections

Steffen Grünewälder