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Author Information
Xun Zheng (Carnegie Mellon University)
Tim G. J. Rudner (University of Oxford)
Tim G. J. Rudner is a Computer Science PhD student at the University of Oxford supervised by Yarin Gal and Yee Whye Teh. His research interests span Bayesian deep learning, reinforcement learning, and variational inference. He obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University. Tim is also a Rhodes Scholar and a Fellow of the German National Academic Foundation.
Christopher Tegho (University of Cambridge)
Chris studied Masters in Machine Learning at the University of Cambridge. His research interests are in Bayesian neural networks, deep reinforcement learning, and variational inference, and computer vision. His thesis was on improving uncertainty estimates in deep reinforcement learning, through Bayes By Backprop, for the application of dialogue systems.
Patrick McClure (NIH)
Yunhao Tang (Columbia University)
I am a PhD student at Columbia IEOR. My research interests are reinforcement learning and approximate inference.
ASHWIN D'CRUZ (CAMBRIDGE UNIVERSITY)
Juan Camilo Gamboa Higuera (McGill University)
Chandra Sekhar Seelamantula (Indian Institute of Science, Bangalore)
Chandra Sekhar Seelamantula obtained a Bachelor of Engineering degree in 1999 with a Gold Medal and Best Thesis Award from the University College of Engineering, Osmania University, India, with a specialization in Electronics and Communication Engineering. He obtained a direct Ph.D. degree in 2005 from the Indian Institute of Science (IISc.), Department of Electrical Communication Engineering. During April 2005– March 2006, he worked as a Technology Consultant for M/s. ESQUBE Communication Solutions Private Limited, Bangalore, and developed proprietary audio coding solutions. In April 2006, he joined the Biomedical Imaging Group, ´ Ecole Polytechnique F´ed´erale de Lausanne, Switzerland, as postdoctoral fellow and specialized in the field of Image Processing, Optical-Coherence Tomography, Holography, Splines, Sparse Signal Processing, and Sampling Theories. In 2009, he joined the Department of Electrical Engineering, IISc., Bangalore, where he is currently Associate Professor and directs research activity of the Spectrum Lab. He is also Associate Faculty at the Centre for Neuroscience, IISc. In 2013, he received the Prof. Priti Shankar Teaching Award from IISc. He is currently also the Vice-Chair of the IEEE Signal Processing Society Bangalore Chapter, and a Senior Area Editor of IEEE Signal Processing Letters and an Associate Editor of SPIE Journal of Electronic Imaging.
Jhosimar Arias Figueroa (University of Campinas)
Andrew Berlin (Draper)
Maxime Voisin (Stanford University)
Alexander Amini (MIT)
Thang Long Doan (McGill)
Hengyuan Hu (Carnegie Mellon University)
Aleksandar Botev (University College London)
Niko Suenderhauf (Australian Centre for Robotic Vision, Queensland University of Technology)
CHI ZHANG (Alibaba Group)
John Lambert (Stanford University)
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2021 : Which priors matter? Benchmarking models for learning latent dynamics »
Aleksandar Botev · Andrew Jaegle · Peter Wirnsberger · Daniel Hennes · Irina Higgins -
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2021 : IL-flOw: Imitation Learning from Observation using Normalizing Flows »
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2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
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2021 : Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots »
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2021 : Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots »
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2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
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2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
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2021 : A Fine-Tuning Approach to Belief State Modeling »
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2022 : A Neural Tangent Kernel Perspective on Function-Space Regularization in Neural Networks »
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2022 : Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap »
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2022 : Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks »
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2022 : Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? »
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2022 : Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? »
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2022 : Building a Subspace of Policies for Scalable Continual Learning »
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2022 : Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? »
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2022 : Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks »
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2022 Spotlight: Lightning Talks 4A-4 »
Yunhao Tang · LING LIANG · Thomas Chau · Daeha Kim · Junbiao Cui · Rui Lu · Lei Song · Byung Cheol Song · Andrew Zhao · Remi Munos · Łukasz Dudziak · Jiye Liang · Ke Xue · Kaidi Xu · Mark Rowland · Hongkai Wen · Xing Hu · Xiaobin Huang · Simon Du · Nicholas Lane · Chao Qian · Lei Deng · Bernardo Avila Pires · Gao Huang · Will Dabney · Mohamed Abdelfattah · Yuan Xie · Marc Bellemare -
2022 Spotlight: The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning »
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2022 : Poster Session 2 »
Jinwuk Seok · Bo Liu · Ryotaro Mitsuboshi · David Martinez-Rubio · Weiqiang Zheng · Ilgee Hong · Chen Fan · Kazusato Oko · Bo Tang · Miao Cheng · Aaron Defazio · Tim G. J. Rudner · Gabriele Farina · Vishwak Srinivasan · Ruichen Jiang · Peng Wang · Jane Lee · Nathan Wycoff · Nikhil Ghosh · Yinbin Han · David Mueller · Liu Yang · Amrutha Varshini Ramesh · Siqi Zhang · Kaifeng Lyu · David Yunis · Kumar Kshitij Patel · Fangshuo Liao · Dmitrii Avdiukhin · Xiang Li · Sattar Vakili · Jiaxin Shi -
2022 Poster: BYOL-Explore: Exploration by Bootstrapped Prediction »
Zhaohan Guo · Shantanu Thakoor · Miruna Pislar · Bernardo Avila Pires · Florent Altché · Corentin Tallec · Alaa Saade · Daniele Calandriello · Jean-Bastien Grill · Yunhao Tang · Michal Valko · Remi Munos · Mohammad Gheshlaghi Azar · Bilal Piot -
2022 Poster: Tractable Function-Space Variational Inference in Bayesian Neural Networks »
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2022 Poster: Efficient Dataset Distillation using Random Feature Approximation »
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2022 Poster: The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning »
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2022 Poster: VICE: Variational Interpretable Concept Embeddings »
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2022 Poster: Evolution of Neural Tangent Kernels under Benign and Adversarial Training »
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2021 : Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks »
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2021 Poster: Sparse Flows: Pruning Continuous-depth Models »
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2021 Poster: SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision »
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2021 Poster: Scalable Online Planning via Reinforcement Learning Fine-Tuning »
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2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
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2021 Poster: Causal Navigation by Continuous-time Neural Networks »
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2021 Poster: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations »
Tim G. J. Rudner · Cong Lu · Michael A Osborne · Yarin Gal · Yee Teh -
2021 Poster: K-level Reasoning for Zero-Shot Coordination in Hanabi »
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2021 Poster: Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation »
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2020 Poster: Deep Evidential Regression »
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2020 Poster: Deep Reinforcement and InfoMax Learning »
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2020 Poster: Self-Imitation Learning via Generalized Lower Bound Q-learning »
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2020 Poster: Disentangling by Subspace Diffusion »
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2019 : Coffee Break & Poster Session 2 »
Juho Lee · Yoonho Lee · Yee Whye Teh · Raymond A. Yeh · Yuan-Ting Hu · Alex Schwing · Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Christian Bueno · Aditya Sanghi · Pradeep Kumar Jayaraman · Ignacio Arroyo-Fernández · Andrew Hryniowski · Vinayak Mathur · Sanjay Singh · Shahrzad Haddadan · Vasco Portilheiro · Luna Zhang · Mert Yuksekgonul · Jhosimar Arias Figueroa · Deepak Maurya · Balaraman Ravindran · Frank NIELSEN · Philip Pham · Justin Payan · Andrew McCallum · Jinesh Mehta · Ke SUN -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Poster session »
Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak -
2019 Poster: From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization »
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2019 Poster: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Spotlight: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2018 : Coffee break + posters 2 »
Jan Kremer · Erik McDermott · Brandon Carter · Albert Zeyer · Andreas Krug · Paul Pu Liang · Katherine Lee · Dominika Basaj · Abelino Jimenez · Lisa Fan · Gautam Bhattacharya · Tzeviya S Fuchs · David Gifford · Loren Lugosch · Orhan Firat · Benjamin Baer · JAHANGIR ALAM · Jamin Shin · Mirco Ravanelli · Paul Smolensky · Zining Zhu · Hamid Eghbal-zadeh · Skyler Seto · Imran Sheikh · Joao Felipe Santos · Yonatan Belinkov · Nadir Durrani · Oiwi Parker Jones · Shuai Tang · André Merboldt · Titouan Parcollet · Wei-Ning Hsu · Krishna Pillutla · Ehsan Hosseini-Asl · Monica Dinculescu · Alexander Amini · Ying Zhang · Taoli Cheng · Alain Tapp -
2018 : Coffee break + posters 1 »
Samuel Myer · Wei-Ning Hsu · Jialu Li · Monica Dinculescu · Lea Schönherr · Ehsan Hosseini-Asl · Skyler Seto · Oiwi Parker Jones · Imran Sheikh · Thomas Manzini · Yonatan Belinkov · Nadir Durrani · Alexander Amini · Johanna Hansen · Gabi Shalev · Jamin Shin · Paul Smolensky · Lisa Fan · Zining Zhu · Hamid Eghbal-zadeh · Benjamin Baer · Abelino Jimenez · Joao Felipe Santos · Jan Kremer · Erik McDermott · Andreas Krug · Tzeviya S Fuchs · Shuai Tang · Brandon Carter · David Gifford · Albert Zeyer · André Merboldt · Krishna Pillutla · Katherine Lee · Titouan Parcollet · Orhan Firat · Gautam Bhattacharya · JAHANGIR ALAM · Mirco Ravanelli -
2018 Poster: Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting »
Hippolyt Ritter · Aleksandar Botev · David Barber -
2018 Poster: Distributed Weight Consolidation: A Brain Segmentation Case Study »
Patrick McClure · Charles Zheng · Jakub R Kaczmarzyk · John Rogers-Lee · Satra Ghosh · Dylan Nielson · Peter A Bandettini · Francisco Pereira -
2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2013 Poster: Scalable Inference for Logistic-Normal Topic Models »
Jianfei Chen · Jun Zhu · Zi Wang · Xun Zheng · Bo Zhang