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Author Information
Nicholas Rhinehart (Carnegie Mellon University)
Nick Rhinehart is a Ph.D. student at Carnegie Mellon University, focusing on understanding, forecasting, and controlling the behavior of agents through computer vision and machine learning. He is particularly interested in systems that learn to reason about the future. He has researched with Sergey Levine at UC Berkeley, Paul Vernaza at N.E.C. Labs, and Drew Bagnell at Uber ATG. His First-Person Forecasting work received the Marr Prize (Best Paper) Honorable Mention Award at ICCV 2017. Nick co-organized Tutorial on Inverse RL for Computer Vision at CVPR 2018 and is the primary organizer of ICML 2019 Workshop on Imitation, Intent, and Interaction.
Yunhao Tang (Columbia University)
Vinay Prabhu (UnifyID AI Labs)
Dian Ang Yap (Stanford University)
Alexander Wang (Stanford University)
Marc Finzi (cornell)
Manoj Kumar (Google Brain)
You Lu (Virginia Tech)
Abhishek Kumar (Google)
Qi Lei (University of Texas at Austin)
Michael Przystupa (University of British Columbia)
Nicola De Cao (University of Amsterdam)
Polina Kirichenko (Cornell)
Pavel Izmailov (CORNELL UNIVERSITY)
Andrew Wilson (Cornell University)

Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. His interests include probabilistic modelling, Gaussian processes, Bayesian statistics, physics inspired machine learning, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.
Jakob Kruse (Heidelberg University)
Diego Mesquita (Aalto University)
Mario Lezcano Casado (Univeristy of Oxford)
Thomas Müller (ETH Zürich)
Keir Simmons (Ascent Robotics)
Andrei Atanov (National Research University Higher School of Economics)
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2023 Poster: Scaling Vision Transformers to 22 Billion Parameters »
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2023 Oral: Quantile Credit Assignment »
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2023 Oral: Scaling Vision Transformers to 22 Billion Parameters »
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2023 Poster: Function-Space Regularization in Neural Networks: A Probabilistic Perspective »
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2023 Poster: The Edge of Orthogonality: A Simple View of What Makes BYOL Tick »
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2022 : Spotlights »
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2021 : Invited Talk 5: Applications of normalizing flows: semi-supervised learning, anomaly detection, and continual learning »
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2021 Poster: Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning »
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2021 Oral: A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups »
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2021 Oral: What Are Bayesian Neural Network Posteriors Really Like? »
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2020 Poster: SGD Learns One-Layer Networks in WGANs »
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2020 Poster: Semi-Supervised Learning with Normalizing Flows »
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2020 Poster: Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? »
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2020 Poster: Learning to Score Behaviors for Guided Policy Optimization »
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2020 Poster: Randomly Projected Additive Gaussian Processes for Regression »
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2020 Tutorial: Bayesian Deep Learning and a Probabilistic Perspective of Model Construction »
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2019 : Poster spotlights #3 »
Sumit Kumar · Nicola De Cao · Benson Chen -
2019 Workshop: ICML Workshop on Imitation, Intent, and Interaction (I3) »
Nicholas Rhinehart · Sergey Levine · Chelsea Finn · He He · Ilya Kostrikov · Justin Fu · Siddharth Reddy -
2019 : Poster discussion »
Roman Novak · Maxime Gabella · Frederic Dreyer · Siavash Golkar · Anh Tong · Irina Higgins · Mirco Milletari · Joe Antognini · Sebastian Goldt · Adín Ramírez Rivera · Roberto Bondesan · Ryo Karakida · Remi Tachet des Combes · Michael Mahoney · Nicholas Walker · Stanislav Fort · Samuel Smith · Rohan Ghosh · Aristide Baratin · Diego Granziol · Stephen Roberts · Dmitry Vetrov · Andrew Wilson · César Laurent · Valentin Thomas · Simon Lacoste-Julien · Dar Gilboa · Daniel Soudry · Anupam Gupta · Anirudh Goyal · Yoshua Bengio · Erich Elsen · Soham De · Stanislaw Jastrzebski · Charles H Martin · Samira Shabanian · Aaron Courville · Shorato Akaho · Lenka Zdeborova · Ethan Dyer · Maurice Weiler · Pim de Haan · Taco Cohen · Max Welling · Ping Luo · zhanglin peng · Nasim Rahaman · Loic Matthey · Danilo J. Rezende · Jaesik Choi · Kyle Cranmer · Lechao Xiao · Jaehoon Lee · Yasaman Bahri · Jeffrey Pennington · Greg Yang · Jiri Hron · Jascha Sohl-Dickstein · Guy Gur-Ari -
2019 : Subspace Inference for Bayesian Deep Learning »
Polina Kirichenko · Pavel Izmailov · Andrew Wilson -
2019 : Spotlight »
Tyler Scott · Kiran Thekumparampil · Jonathan Aigrain · Rene Bidart · Priyadarshini Panda · Dian Ang Yap · Yaniv Yacoby · Raphael Gontijo Lopes · Alberto Marchisio · Erik Englesson · Wanqian Yang · Moritz Graule · Yi Sun · Daniel Kang · Mike Dusenberry · Min Du · Hartmut Maennel · Kunal Menda · Vineet Edupuganti · Luke Metz · David Stutz · Vignesh Srinivasan · Timo Sämann · Vineeth N Balasubramanian · Sina Mohseni · Rob Cornish · Judith Butepage · Zhangyang Wang · Bai Li · Bo Han · Honglin Li · Maksym Andriushchenko · Lukas Ruff · Meet P. Vadera · Yaniv Ovadia · Sunil Thulasidasan · Disi Ji · Gang Niu · Saeed Mahloujifar · Aviral Kumar · SANGHYUK CHUN · Dong Yin · Joyce Xu Xu · Hugo Gomes · Raanan Rohekar -
2019 Poster: Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group »
Mario Lezcano Casado · David Martínez-Rubio -
2019 Poster: Simple Black-box Adversarial Attacks »
Chuan Guo · Jacob Gardner · Yurong You · Andrew Wilson · Kilian Weinberger -
2019 Oral: Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group »
Mario Lezcano Casado · David Martínez-Rubio -
2019 Oral: Simple Black-box Adversarial Attacks »
Chuan Guo · Jacob Gardner · Yurong You · Andrew Wilson · Kilian Weinberger -
2019 Poster: SWALP : Stochastic Weight Averaging in Low Precision Training »
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa -
2019 Oral: SWALP : Stochastic Weight Averaging in Low Precision Training »
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa -
2018 Poster: Constant-Time Predictive Distributions for Gaussian Processes »
Geoff Pleiss · Jacob Gardner · Kilian Weinberger · Andrew Wilson -
2018 Oral: Constant-Time Predictive Distributions for Gaussian Processes »
Geoff Pleiss · Jacob Gardner · Kilian Weinberger · Andrew Wilson -
2018 Poster: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2018 Oral: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization »
Jiong Zhang · Qi Lei · Inderjit Dhillon -
2017 Poster: Gradient Coding: Avoiding Stragglers in Distributed Learning »
Rashish Tandon · Qi Lei · Alexandros Dimakis · Nikos Karampatziakis -
2017 Talk: Gradient Coding: Avoiding Stragglers in Distributed Learning »
Rashish Tandon · Qi Lei · Alexandros Dimakis · Nikos Karampatziakis -
2017 Poster: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar -
2017 Talk: Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization »
Qi Lei · En-Hsu Yen · Chao-Yuan Wu · Inderjit Dhillon · Pradeep Ravikumar