JMLR 2020
232 papers
A Numerical Measure of the Instability of Mapper-Type Algorithms
Francisco Belchi, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan A Sober Look at the Unsupervised Learning of Disentangled Representations and Their Evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem A Unified Framework for Structured Graph Learning via Spectral Constraints
Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar Adaptive Smoothing for Path Integral Control
Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro, Vicenç Gómez Beyond Trees: Classification with Sparse Pairwise Dependencies
Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H.S. Torr, Pushmeet Kohli, M. Pawan Kumar Causal Discovery from Heterogeneous/Nonstationary Data
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf Change Point Estimation in a Dynamic Stochastic Block Model
Monika Bhattacharjee, Moulinath Banerjee, George Michailidis Contextual Explanation Networks
Maruan Al-Shedivat, Avinava Dubey, Eric Xing Cramer-Wold Auto-Encoder
Szymon Knop, Przemysław Spurek, Jacek Tabor, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone 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 DESlib: A Dynamic Ensemble Selection Library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti Dual Extrapolation for Sparse GLMs
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon Dual Iterative Hard Thresholding
Xiao-Tong Yuan, Bo Liu, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas Ensemble Learning for Relational Data
Hoda Eldardiry, Jennifer Neville, Ryan A. Rossi 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 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 General Latent Feature Models for Heterogeneous Datasets
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani 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 High-Dimensional Inference for Cluster-Based Graphical Models
Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu Importance Sampling Techniques for Policy Optimization
Alberto Maria Metelli, Matteo Papini, Nico Montali, Marcello Restelli Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, Tom Claassen Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara Learning Data-Adaptive Non-Parametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li Learning Mixed Latent Tree Models
Can Zhou, Xiaofei Wang, Jianhua Guo Learning with Fenchel-Young Losses
Mathieu Blondel, André F.T. Martins, Vlad Niculae Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen, César L. C. Mattos, Amauri H. Souza Júnior, João P. P. Gomes Minimax Nonparametric Parallelism Test
Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy, Andreas Krause Multiclass Anomaly Detector: The CS++ Support Vector Machine
Alistair Shilton, Sutharshan Rajasegarar, Marimuthu Palaniswami NEVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez On Efficient Adjustment in Causal Graphs
Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez On Mahalanobis Distance in Functional Settings
José R. Berrendero, Beatriz Bueno-Larraz, Antonio Cuevas Optimal Estimation of Sparse Topic Models
Xin Bing, Florentina Bunea, Marten Wegkamp Orlicz Random Fourier Features
Linda Chamakh, Emmanuel Gobet, Zoltán Szabó Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta Provable Convex Co-Clustering of Tensors
Eric C. Chi, Brian J. Gaines, Will Wei Sun, Hua Zhou, Jian Yang Quantile Graphical Models: A Bayesian Approach
Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart Risk Bounds for Reservoir Computing
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega Robust Reinforcement Learning with Bayesian Optimisation and Quadrature
Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson Self-Paced Multi-View Co-Training
Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang 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 Sparse and Low-Rank Multivariate Hawkes Processes
Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-Francois Muzy Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein Spectral Bandits
Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko Tensor Regression Networks
Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar The Kalai-Smorodinsky Solution for Many-Objective Bayesian Optimization
Mickael Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal Topology of Deep Neural Networks
Gregory Naitzat, Andrey Zhitnikov, Lek-Heng Lim 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, Barnabas Poczos, Eric P. Xing Variational Inference for Computational Imaging Inverse Problems
Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith