How, Jonathan P.

27 publications

TMLR 2023 Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning Macheng Shen, Jonathan P How

AAAI 2022 Context-Specific Representation Abstraction for Deep Option Learning Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

NeurIPS 2022 Influencing Long-Term Behavior in Multiagent Reinforcement Learning Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P How

ICLRW 2022 Influencing Long-Term Behavior in Multiagent Reinforcement Learning Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob Nicolaus Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P How

CVPRW 2020 SILA: An Incremental Learning Approach for Pedestrian Trajectory Prediction Golnaz Habibi, Nikita Jaipuria, Jonathan P. How

CoRL 2019 Certified Adversarial Robustness for Deep Reinforcement Learning Björn Lütjens, Michael Everett, Jonathan P. How

AAAI 2019 Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How

AAAI 2019 Learning to Teach in Cooperative Multiagent Reinforcement Learning Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

JAIR 2019 Modeling and Planning with Macro-Actions in Decentralized POMDPs Christopher Amato, George Dimitri Konidaris, Leslie Pack Kaelbling, Jonathan P. How

ICMLW 2019 R-MADDPG for Partially Observable Environments and Limited Communication Rose E. Wang, Michael Everett, Jonathan P. How

ICML 2017 Deep Decentralized Multi-Task Multi-Agent Reinforcement Learning Under Partial Observability Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian

CVPR 2017 Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher Iii

NeurIPS 2016 Improving PAC Exploration Using the Median of Means Jason Pazis, Ronald E Parr, Jonathan P How

AAAI 2016 Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments Miao Liu, Christopher Amato, Emily P. Anesta, John Daniel Griffith, Jonathan P. How

MLOSS 2015 RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How

CVPR 2015 Small-Variance Nonparametric Clustering on the Hypersphere Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher Iii

IJCAI 2015 Stick-Breaking Policy Learning in Dec-POMDPs Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How

NeurIPS 2015 Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher Iii, Jonathan P How

UAI 2014 Approximate Decentralized Bayesian Inference Trevor Campbell, Jonathan P. How

UAI 2014 Quantifying Nonlocal Informativeness in High-Dimensional, Loopy Gaussian Graphical Models Daniel S. Levine, Jonathan P. How

FnTML 2013 A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning Alborz Geramifard, Thomas J. Walsh, Stefanie Tellex, Girish Chowdhary, Nicholas Roy, Jonathan P. How

UAI 2013 Batch-iFDD for Representation Expansion in Large MDPs Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan P. How

NeurIPS 2013 Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P How, Lawrence Carin

NeurIPS 2013 Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S Levine, Jonathan P How

ECML-PKDD 2012 Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery N. Kemal Ure, Alborz Geramifard, Girish Chowdhary, Jonathan P. How

ECML-PKDD 2012 Bayesian Nonparametric Inverse Reinforcement Learning Bernard Michini, Jonathan P. How

ICML 2011 Online Discovery of Feature Dependencies Alborz Geramifard, Finale Doshi, Josh Redding, Nicholas Roy, Jonathan P. How