Volume 21, 2020
Dynamic Assortment Optimization with Changing Contextual Information.

Xi Chen Yining Wang Yuan Zhou

Mining Topological Structure in Graphs through Forest Representations.

Robin Vandaele Yvan Saeys Tijl De Bie

Provable Convex Co-clustering of Tensors.

Eric C. Chi Brian J. Gaines Will Wei Sun Hua Zhou Jian Yang

Multiclass Anomaly Detector: the CS++ Support Vector Machine.

Alistair Shilton Sutharshan Rajasegarar Marimuthu Palaniswami

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn.

Sebastian Pölsterl

Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images.

Avrim Blum Travis Dick Naren Manoj Hongyang Zhang

ProtoAttend: Attention-Based Prototypical Learning.

Sercan Ömer Arik Tomas Pfister

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation.

Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem

Learning Data-adaptive Non-parametric Kernels.

Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li

Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging.

Baihua He Yanyan Liu Yuanshan Wu Guosheng Yin Xingqiu Zhao

On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond.

Xiao-Tong Yuan Ping Li

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms.

Simon Fischer Ingo Steinwart

Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data.

Martin Slawski Emanuel Ben-David Ping Li

Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance.

Dalit Engelhardt

A Numerical Measure of the Instability of Mapper-Type Algorithms.

Francisco Belchí Guillamón Jacek Brodzki Matthew Burfitt Mahesan Niranjan

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models.

Reza Mohammadi Matthew T. Pratola Maurits Kaptein

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy.

Di Wang Marco Gaboardi Adam Smith Jinhui Xu

Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms.

Yazhen Wang Shang Wu

Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach.

Haoran Wang Thaleia Zariphopoulou Xun Yu Zhou

A determinantal point process for column subset selection.

Ayoub Belhadji Rémi Bardenet Pierre Chainais

Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning.

Lucas Lehnert Michael L. Littman

Conic Optimization for Quadratic Regression Under Sparse Noise.

Igor Molybog Ramtin Madani Javad Lavaei

Contextual Explanation Networks.

Maruan Al-Shedivat Avinava Dubey Eric P. Xing

Learning and Interpreting Multi-Multi-Instance Learning Networks.

Alessandro Tibo Manfred Jaeger Paolo Frasconi

Semi-parametric Learning of Structured Temporal Point Processes.

Ganggang Xu Ming Wang Jiangze Bian Hui Huang Timothy R. Burch Sandro C. Andrade Jingfei Zhang Yongtao Guan

Adaptive Smoothing for Path Integral Control.

Dominik Thalmeier Hilbert J. Kappen Simone Totaro Vicenç Gómez

A Unified q-Memorization Framework for Asynchronous Stochastic Optimization.

Bin Gu Wenhan Xian Zhouyuan Huo Cheng Deng Heng Huang

Beyond Trees: Classification with Sparse Pairwise Dependencies.

Yaniv Tenzer Amit Moscovich Mary Frances Dorn Boaz Nadler Clifford H. Spiegelman

Efficient Adjustment Sets for Population Average Causal Treatment Effect Estimation in Graphical Models.

Andrea Rotnitzky Ezequiel Smucler

Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs.

Rui Tuo Wenjia Wang

Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods.

Franca Hoffmann Bamdad Hosseini Zhi Ren Andrew M. Stuart

Scikit-network: Graph Analysis in Python.

Thomas Bonald Nathan de Lara Quentin Lutz Bertrand Charpentier

Topology of Deep Neural Networks.

Gregory Naitzat Andrey Zhitnikov Lek-Heng Lim

Near-optimal Individualized Treatment Recommendations.

Haomiao Meng Ying-Qi Zhao Haoda Fu Xingye Qiao

Distributed High-dimensional Regression Under a Quantile Loss Function.

Xi Chen Weidong Liu Xiaojun Mao Zhuoyi Yang

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.

Sanmit Narvekar Bei Peng Matteo Leonetti Jivko Sinapov Matthew E. Taylor Peter Stone

Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction.

Boyue Li Shicong Cen Yuxin Chen Yuejie Chi

Variational Inference for Computational Imaging Inverse Problems.

Francesco Tonolini Jack Radford Alex Turpin Daniele Faccio Roderick Murray-Smith

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Tabish Rashid Mikayel Samvelyan Christian Schröder de Witt Gregory Farquhar Jakob N. Foerster Shimon Whiteson

Optimal Estimation of Sparse Topic Models.

Xin Bing Florentina Bunea Marten H. Wegkamp

Breaking the Curse of Nonregularity with Subagging - Inference of the Mean Outcome under Optimal Treatment Regimes.

Chengchun Shi Wenbin Lu Rui Song

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes.

Devanshu Agrawal Theodore Papamarkou Jacob D. Hinkle

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality.

Ryumei Nakada Masaaki Imaizumi

Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes.

Emily C. Hector Peter X.-K. Song

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise.

Yuka Hashimoto Isao Ishikawa Masahiro Ikeda Yoichi Matsuo Yoshinobu Kawahara

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success.

Lucas Mentch Siyu Zhou

Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior.

William Hoiles Vikram Krishnamurthy Kunal Pattanayak

The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization.

Dmitry Kobak Jonathan Lomond Benoit Sanchez

Convex and Non-Convex Approaches for Statistical Inference with Class-Conditional Noisy Labels.

Hyebin Song Ran Dai Garvesh Raskutti Rina Foygel Barber

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.

Nathan Kallus Masatoshi Uehara

High Dimensional Forecasting via Interpretable Vector Autoregression.

William B. Nicholson Ines Wilms Jacob Bien David S. Matteson

Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group.

Yuexiang Zhai Zitong Yang Zhenyu Liao John Wright Yi Ma

Cramer-Wold Auto-Encoder.

Szymon Knop Przemyslaw Spurek Jacek Tabor Igor T. Podolak Marcin Mazur Stanislaw Jastrzebski

Trust-Region Variational Inference with Gaussian Mixture Models.

Oleg Arenz Mingjun Zhong Gerhard Neumann

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information.

Yichong Xu Sivaraman Balakrishnan Aarti Singh Artur Dubrawski

apricot: Submodular selection for data summarization in Python.

Jacob M. Schreiber Jeffrey A. Bilmes William Stafford Noble

Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective.

Chao Gao Yuan Yao Weizhi Zhu

Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study.

Yan Ru Pei Haik Manukian Massimiliano Di Ventra

Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion.

Jiaying Gu Qing Zhou

Streamlined Variational Inference with Higher Level Random Effects.

Tui H. Nolan Marianne Menictas Matt P. Wand

Asymptotic Consistency of α-Rényi-Approximate Posteriors.

Prateek Jaiswal Vinayak A. Rao Harsha Honnappa

Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise.

Andrei Kulunchakov Julien Mairal

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality.

Miaoyan Wang Lexin Li

Spectral Algorithms for Community Detection in Directed Networks.

Zhe Wang Yingbin Liang Pengsheng Ji

Dual Iterative Hard Thresholding.

Xiao-Tong Yuan Bo Liu Lezi Wang Qingshan Liu Dimitris N. Metaxas

Robust Reinforcement Learning with Bayesian Optimisation and Quadrature.

Supratik Paul Konstantinos I. Chatzilygeroudis Kamil Ciosek Jean-Baptiste Mouret Michael A. Osborne Shimon Whiteson

The Kalai-Smorodinsky solution for many-objective Bayesian optimization.

Mickaël Binois Victor Picheny Patrick Taillandier Abderrahmane Habbal

Distributionally Ambiguous Optimization for Batch Bayesian Optimization.

Nikitas Rontsis Michael A. Osborne Paul J. Goulart

Local Causal Network Learning for Finding Pairs of Total and Direct Effects.

Yue Liu Zhuangyan Fang Yangbo He Zhi Geng Chunchen Liu

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms.

Junhong Lin Volkan Cevher

New Insights and Perspectives on the Natural Gradient Method.

James Martens

Orlicz Random Fourier Features.

Linda Chamakh Emmanuel Gobet Zoltán Szabó

Empirical Priors for Prediction in Sparse High-dimensional Linear Regression.

Ryan Martin Yiqi Tang

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.

Qihang Lin Selvaprabu Nadarajah Negar Soheili Tianbao Yang

Nesterov's Acceleration for Approximate Newton.

Haishan Ye Luo Luo Zhihua Zhang

Importance Sampling Techniques for Policy Optimization.

Alberto Maria Metelli Matteo Papini Nico Montali Marcello Restelli

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Colin Raffel Noam Shazeer Adam Roberts Katherine Lee Sharan Narang Michael Matena Yanqi Zhou Wei Li Peter J. Liu

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks.

Amir R. Asadi Emmanuel Abbe

metric-learn: Metric Learning Algorithms in Python.

William de Vazelhes C. J. Carey Yuan Tang Nathalie Vauquier Aurélien Bellet

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting.

Akshay Krishnamurthy John Langford Aleksandrs Slivkins Chicheng Zhang

Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions.

Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks.

Owen Marschall Kyunghyun Cho Cristina Savin

Probabilistic Learning on Graphs via Contextual Architectures.

Davide Bacciu Federico Errica Alessio Micheli

Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers.

Yao Ma Alex Olshevsky Csaba Szepesvári Venkatesh Saligrama

Monte Carlo Gradient Estimation in Machine Learning.

Shakir Mohamed Mihaela Rosca Michael Figurnov Andriy Mnih

Convergence of Sparse Variational Inference in Gaussian Processes Regression.

David R. Burt Carl Edward Rasmussen Mark van der Wilk

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.

Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel C. Hoffman Stephanie Houde Q. Vera Liao Ronny Luss Aleksandra Mojsilovic Sami Mourad Pablo Pedemonte Ramya Raghavendra John T. Richards Prasanna Sattigeri Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis Wei Yunfeng Zhang

A General System of Differential Equations to Model First-Order Adaptive Algorithms.

André Belotto da Silva Maxime Gazeau

A Regularization-Based Adaptive Test for High-Dimensional GLMs.

Chong Wu Gongjun Xu Xiaotong Shen Wei Pan

Apache Mahout: Machine Learning on Distributed Dataflow Systems.

Robin Anil Gökhan Çapan Isabel Drost-Fromm Ted Dunning Ellen Friedman Trevor Grant Shannon Quinn Paritosh Ranjan Sebastian Schelter Özgür Yilmazel

Distributed Minimum Error Entropy Algorithms.

Xin Guo Ting Hu Qiang Wu

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization.

Rad Niazadeh Tim Roughgarden Joshua R. Wang

Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination.

Hang Yu Songwei Wu Luyin Xin Justin Dauwels

Tensor Regression Networks.

Jean Kossaifi Zachary C. Lipton Arinbjörn Kolbeinsson Aran Khanna Tommaso Furlanello Anima Anandkumar

Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering.

Israel Almodóvar-Rivera Ranjan Maitra

Agnostic Estimation for Phase Retrieval.

Matey Neykov Zhaoran Wang Han Liu

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning.

Aryan Mokhtari Alec Koppel Martin Takác Alejandro Ribeiro

Bayesian Closed Surface Fitting Through Tensor Products.

Olivier Binette Debdeep Pati David B. Dunson

Tslearn, A Machine Learning Toolkit for Time Series Data.

Romain Tavenard Johann Faouzi Gilles Vandewiele Felix Divo Guillaume Androz Chester Holtz Marie Payne Roman Yurchak Marc Rußwurm Kushal Kolar Eli Woods

Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models.

Jiahe Lin George Michailidis

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

Alexander Alexandrov Konstantinos Benidis Michael Bohlke-Schneider Valentin Flunkert Jan Gasthaus Tim Januschowski Danielle C. Maddix Syama Sundar Rangapuram David Salinas Jasper Schulz Lorenzo Stella Ali Caner Türkmen Yuyang Wang

Identifiability and Consistent Estimation of Nonparametric Translation Hidden Markov Models with General State Space.

Elisabeth Gassiat Sylvain Le Corff Luc Lehéricy

NEVAE: A Deep Generative Model for Molecular Graphs.

Bidisha Samanta Abir De Gourhari Jana Vicenç Gómez Pratim Kumar Chattaraj Niloy Ganguly Manuel Gomez-Rodriguez

Prediction regions through Inverse Regression.

Emilie Devijver Émeline Perthame

High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model.

Houssem Sifaou Abla Kammoun Mohamed-Slim Alouini

MFE: Towards reproducible meta-feature extraction.

Edesio Alcobaça Felipe Siqueira Adriano Rivolli Luís Paulo F. Garcia Jefferson Tales Oliva André C. P. L. F. de Carvalho

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization.

Nhan H. Pham Lam M. Nguyen Dzung T. Phan Quoc Tran-Dinh

Bayesian Model Selection with Graph Structured Sparsity.

Youngseok Kim Chao Gao

ThunderGBM: Fast GBDTs and Random Forests on GPUs.

Zeyi Wen Hanfeng Liu Jiashuai Shi Qinbin Li Bingsheng He Jian Chen

Change Point Estimation in a Dynamic Stochastic Block Model.

Monika Bhattacharjee Moulinath Banerjee George Michailidis

Quadratic Decomposable Submodular Function Minimization: Theory and Practice.

Pan Li Niao He Olgica Milenkovic

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization.

Aryan Mokhtari Hamed Hassani Amin Karbasi

Sparse Projection Oblique Randomer Forests.

Tyler M. Tomita James Browne Cencheng Shen Jaewon Chung Jesse Patsolic Benjamin Falk Carey E. Priebe Jason Yim Randal C. Burns Mauro Maggioni Joshua T. Vogelstein

Stochastic Nested Variance Reduction for Nonconvex Optimization.

Dongruo Zhou Pan Xu Quanquan Gu

AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings).

Eugenio Bargiacchi Diederik M. Roijers Ann Nowé

Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size.

Francois Kamper Sarel Steel Johan A. du Preez

General Latent Feature Models for Heterogeneous Datasets.

Isabel Valera Melanie F. Pradier Maria Lomeli Zoubin Ghahramani

Joint Causal Inference from Multiple Contexts.

Joris M. Mooij Sara Magliacane Tom Claassen

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings.

Carlo Ciliberto Lorenzo Rosasco Alessandro Rudi

Loss Control with Rank-one Covariance Estimate for Short-term Portfolio Optimization.

Zhao-Rong Lai Liming Tan Xiaotian Wu Liangda Fang

pyDML: A Python Library for Distance Metric Learning.

Juan-Luis Suárez Salvador García Francisco Herrera

Cornac: A Comparative Framework for Multimodal Recommender Systems.

Aghiles Salah Quoc-Tuan Truong Hady W. Lauw

Minimax Nonparametric Parallelism Test.

Xin Xing Meimei Liu Ping Ma Wenxuan Zhong

Distributed Kernel Ridge Regression with Communications.

Shao-Bo Lin Di Wang Ding-Xuan Zhou

Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients.

Yuansi Chen Raaz Dwivedi Martin J. Wainwright Bin Yu

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching.

Ming Yu Varun Gupta Mladen Kolar

Probabilistic Symmetries and Invariant Neural Networks.

Benjamin Bloem-Reddy Yee Whye Teh

Causal Discovery from Heterogeneous/Nonstationary Data.

Biwei Huang Kun Zhang Jiji Zhang Joseph D. Ramsey Ruben Sanchez-Romero Clark Glymour Bernhard Schölkopf

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators.

Tom Rainforth Adam Golinski Frank Wood Sheheryar Zaidi

Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data.

Toby Dylan Hocking Guillem Rigaill Paul Fearnhead Guillaume Bourque

Convergence Rate of Optimal Quantization and Application to the Clustering Performance of the Empirical Measure.

Yating Liu Gilles Pagès

Effective Ways to Build and Evaluate Individual Survival Distributions.

Humza Haider Bret Hoehn Sarah Davis Russell Greiner

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions.

Christiane Görgen Manuele Leonelli

Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis.

Xiaoming Yuan Shangzhi Zeng Jin Zhang

Sequential change-point detection in high-dimensional Gaussian graphical models.

Hossein Keshavarz George Michailidis Yves Atchadé

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly.

Kirthevasan Kandasamy Karun Raju Vysyaraju Willie Neiswanger Biswajit Paria Christopher R. Collins Jeff Schneider Barnabás Póczos Eric P. Xing

Memoryless Sequences for General Losses.

Rafael M. Frongillo Andrew B. Nobel

Quantile Graphical Models: a Bayesian Approach.

Nilabja Guha Veera Baladandayuthapani Bani K. Mallick

Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms.

Malte Probst Franz Rothlauf

Multi-Player Bandits: The Adversarial Case.

Pragnya Alatur Kfir Y. Levy Andreas Krause

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning.

Anis Elgabli Jihong Park Amrit S. Bedi Mehdi Bennis Vaneet Aggarwal

Identifiability of Additive Noise Models Using Conditional Variances.

Gunwoong Park

High-dimensional Gaussian graphical models on network-linked data.

Tianxi Li Cheng Qian Elizaveta Levina Ji Zhu

Scalable Approximate MCMC Algorithms for the Horseshoe Prior.

James E. Johndrow Paulo Orenstein Anirban Bhattacharya

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets.

Maxim Borisyak Artem Ryzhikov Andrey Ustyuzhanin Denis Derkach Fedor Ratnikov Olga Mineeva

Estimation of a Low-rank Topic-Based Model for Information Cascades.

Ming Yu Varun Gupta Mladen Kolar

Representation Learning for Dynamic Graphs: A Survey.

Seyed Mehran Kazemi Rishab Goel Kshitij Jain Ivan Kobyzev Akshay Sethi Peter Forsyth Pascal Poupart

Union of Low-Rank Tensor Spaces: Clustering and Completion.

Morteza Ashraphijuo Xiaodong Wang

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics.

Xi Chen Simon S. Du Xin T. Tong

The weight function in the subtree kernel is decisive.

Romain Azaïs Florian Ingels

WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions.

Edgar Dobriban Yue Sheng

Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients.

Yu Liu Kris De Brabanter

Community-Based Group Graphical Lasso.

Eugen Pircalabelu Gerda Claeskens

Unique Sharp Local Minimum in L1-minimization Complete Dictionary Learning.

Yu Wang Siqi Wu Bin Yu

Generalized Optimal Matching Methods for Causal Inference.

Nathan Kallus

Multiparameter Persistence Landscapes.

Oliver Vipond

Kymatio: Scattering Transforms in Python.

Mathieu Andreux Tomás Angles Georgios Exarchakis Roberto Leonarduzzi Gaspar Rochette Louis Thiry John Zarka Stéphane Mallat Joakim Andén Eugene Belilovsky Joan Bruna Vincent Lostanlen Muawiz Chaudhary Matthew J. Hirn Edouard Oyallon Sixin Zhang Carmine-Emanuele Cella Michael Eickenberg

Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis.

Salar Fattahi Somayeh Sojoudi

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions.

Artin Spiridonoff Alex Olshevsky Ioannis Ch. Paschalidis

Self-paced Multi-view Co-training.

Fan Ma Deyu Meng Xuanyi Dong Yi Yang

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes.

Peter D. Grünwald Nishant A. Mehta

Conjugate Gradients for Kernel Machines.

Simon Bartels Philipp Hennig

GraKeL: A Graph Kernel Library in Python.

Giannis Siglidis Giannis Nikolentzos Stratis Limnios Christos Giatsidis Konstantinos Skianis Michalis Vazirgiannis

High-Dimensional Inference for Cluster-Based Graphical Models.

Carson Eisenach Florentina Bunea Yang Ning Claudiu Dinicu

Expected Policy Gradients for Reinforcement Learning.

Kamil Ciosek Shimon Whiteson

Learning Causal Networks via Additive Faithfulness.

Kuang-Yao Lee Tianqi Liu Bing Li Hongyu Zhao

Sparse and low-rank multivariate Hawkes processes.

Emmanuel Bacry Martin Bompaire Stéphane Gaïffas Jean-François Muzy

Ensemble Learning for Relational Data.

Hoda Eldardiry Jennifer Neville Ryan A. Rossi

Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory.

Thomas Ricatte Rémi Gilleron Marc Tommasi

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement.

Wouter Kool Herke van Hoof Max Welling

pyts: A Python Package for Time Series Classification.

Johann Faouzi Hicham Janati

A Convex Parametrization of a New Class of Universal Kernel Functions.

Brendon K. Colbert Matthew M. Peet

Dynamical Systems as Temporal Feature Spaces.

Peter Tiño

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data.

Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-Ling Wang Michael I. Jordan

Branch and Bound for Piecewise Linear Neural Network Verification.

Rudy Bunel Jingyue Lu Ilker Turkaslan Philip H. S. Torr Pushmeet Kohli M. Pawan Kumar

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables.

Rune Christiansen Jonas Peters

Optimal Bipartite Network Clustering.

Zhixin Zhou Arash A. Amini

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.

Saber Salehkaleybar AmirEmad Ghassami Negar Kiyavash Kun Zhang

Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification.

Leo L. Duan

Causal Discovery Toolbox: Uncovering causal relationships in Python.

Diviyan Kalainathan Olivier Goudet Ritik Dutta

Noise Accumulation in High Dimensional Classification and Total Signal Index.

Miriam R. Elman Jessica Minnier Xiaohui Chang Dongseok Choi

Learning with Fenchel-Young losses.

Mathieu Blondel André F. T. Martins Vlad Niculae

Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent.

Dominic Richards Patrick Rebeschini

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent.

Huan Li Zhouchen Lin

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping.

Mihai Cucuringu Hemant Tyagi

Generalized Nonbacktracking Bounds on the Influence.

Emmanuel Abbe Sanjeev R. Kulkarni Eun Jee Lee

Tensor Train Decomposition on TensorFlow (T3F).

Alexander Novikov Pavel Izmailov Valentin Khrulkov Michael Figurnov Ivan V. Oseledets

The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response.

Xin Zhang Qing Mai Hui Zou

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms.

Nicolás García Trillos Zachary Kaplan Thabo Samakhoana Daniel Sanz-Alonso

A New Class of Time Dependent Latent Factor Models with Applications.

Sinead A. Williamson Michael Minyi Zhang Paul Damien

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes.

Anders E. Bilgrau Carel F. W. Peeters Poul Svante Eriksen Martin Bøgsted Wessel N. van Wieringen

Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models.

Ivona Bezáková Antonio Blanca Zongchen Chen Daniel Stefankovic Eric Vigoda

Distributed Feature Screening via Componentwise Debiasing.

Xingxiang Li Runze Li Zhiming Xia Chen Xu

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

Jian Guo He He Tong He Leonard Lausen Mu Li Haibin Lin Xingjian Shi Chenguang Wang Junyuan Xie Sheng Zha Aston Zhang Hang Zhang Zhi Zhang Zhongyue Zhang Shuai Zheng Yi Zhu

A Unified Framework for Structured Graph Learning via Spectral Constraints.

Sandeep Kumar Jiaxi Ying José Vinícius de Miranda Cardoso Daniel P. Palomar

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems.

Dhruv Malik Ashwin Pananjady Kush Bhatia Koulik Khamaru Peter L. Bartlett Martin J. Wainwright

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections.

Junhong Lin Volkan Cevher

High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix.

Cheng Yong Tang Ethan X. Fang Yuexiao Dong

Connecting Spectral Clustering to Maximum Margins and Level Sets.

David P. Hofmeyr

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data.

Aki Vehtari Andrew Gelman Tuomas Sivula Pasi Jylänki Dustin Tran Swupnil Sahai Paul Blomstedt John P. Cunningham David Schiminovich Christian P. Robert

Practical Locally Private Heavy Hitters.

Raef Bassily Kobbi Nissim Uri Stemmer Abhradeep Thakurta

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning.

Ery Arias-Castro Adel Javanmard Bruno Pelletier

On lp-Support Vector Machines and Multidimensional Kernels.

Víctor Blanco Justo Puerto Antonio M. Rodríguez-Chía

Generalized probabilistic principal component analysis of correlated data.

Mengyang Gu Weining Shen

Neyman-Pearson classification: parametrics and sample size requirement.

Xin Tong Lucy Xia Jiacheng Wang Yang Feng

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information.

T. Tony Cai Tengyuan Liang Alexander Rakhlin

Online Sufficient Dimension Reduction Through Sliced Inverse Regression.

Zhanrui Cai Runze Li Liping Zhu

On Mahalanobis Distance in Functional Settings.

José R. Berrendero Beatriz Bueno-Larraz Antonio Cuevas

DESlib: A Dynamic ensemble selection library in Python.

Rafael M. O. Cruz Luiz G. Hafemann Robert Sabourin George D. C. Cavalcanti

Target Propagation in Recurrent Neural Networks.

Nikolay Manchev Michael W. Spratling

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms.

Anna V. Little Mauro Maggioni James M. Murphy

Lower Bounds for Parallel and Randomized Convex Optimization.

Jelena Diakonikolas Cristóbal Guzmán

Universal Latent Space Model Fitting for Large Networks with Edge Covariates.

Zhuang Ma Zongming Ma Hongsong Yuan

A Model of Fake Data in Data-driven Analysis.

Xiaofan Li Andrew B. Whinston

A Statistical Learning Approach to Modal Regression.

Yunlong Feng Jun Fan Johan A. K. Suykens

A Low Complexity Algorithm with O(√T) Regret and O(1) Constraint Violations for Online Convex Optimization with Long Term Constraints.

Hao Yu Michael J. Neely