The work of Amii researchers is being featured at the 38th annual International Conference on Machine Learning (ICML), running online this year from July 18 to 24.
ICML is the premier gathering of professionals dedicated to the advancement of the branch of AI known as machine learning (ML). Globally renowned for presenting and publishing cutting-edge research on all aspects of ML and application areas, the conference is within the top ten highest-ranked ML & AI conferences in the world based on h-index and Impact Score values (see: Google Scholar and Guide2Research).
Amii Fellows and Canada CIFAR AI Chairs – professors at the University of Alberta, University of British Columbia and Carleton University – as well as other Amii researchers have 23 papers included in the proceedings:
Accepted Papers
* indicates Amii researcher or alum
A Distribution-dependent Analysis of Meta Learning
Mikhail Konobeev*, Ilja Kuzborskij, Csaba Szepesvári*
Average-Reward Off-Policy Policy Evaluation with Function Approximation
Shangtong Zhang, Yi Wan*, Richard Sutton*, Shimon Whiteson
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado*, Nicolas Le Roux
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári*, Mengdi Wang
Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen*, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans*
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Dustin Morrill*, Ryan D'Orazio*, Marc Lanctot*, James Wright*, Michael Bowling*, Amy Greenwald
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White*, Matteo Hessel, Charles Blundell, Hado van Hasselt
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch*, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot*, Karl Tuyls
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári*
Learning and Planning in Average-Reward Markov Decision Processes
Yi Wan*, Abhishek Naik*, Richard Sutton*
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans*, Jure Leskovec, Denny Zhou
Leveraging Non-uniformity in First-order Non-convex Optimization
Jincheng Mei*, Yue Gao*, Bo Dai, Csaba Szepesvári*, Dale Schuurmans*
Branislav Kveton, Mikhail Konobeev*, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári*
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao*, Yifan Wu*, Jincheng Mei*, Bo Dai, Tor Lattimore*, Lihong Li*, Csaba Szepesvári*, Dale Schuurmans*
Parameterless Transductive Feature Re-representation for Few-Shot Learning
Wentao Cui, Yuhong Guo*
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno*, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Gu
Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub*, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang
Revisiting Peng's Q(λ) for Modern Reinforcement Learning
Tadashi Kozuno*, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
RNNRepair: Automatic RNN Repair via Model-based Analysis
Xiaofei Xie, Wenbo Guo, Lei Ma*, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing
Robust Asymmetric Learning in POMDPs
Andrew Warrington, Jonathan Lavington*, Adam Ścibior, Mark Schmidt*, Frank Wood
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore*, Csaba Szepesvári*, Mengdi Wang
Tractable structured natural-gradient descent using local parameterizations
Wu Lin*, Frank Nielsen, Khan Emtiyaz, Mark Schmidt*
Valid Causal Inference with (Some) Invalid Instruments
Jason Hartford*, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown*
Workshops
Reinforcement Learning for Real Life
Co-organized by Yuxi Li, Minmin Chen, Omer Gottesman, Lihong Li, Zongqing Lu, Rupam Mahmood*, Niranjani Prasad, Zhiwei (Tony) Qin, Csaba Szepesvári*, Matthew Taylor*
Workshop on Reinforcement Learning Theory
Co-organized by Shipra Agrawal, Simon Du, Niao He, Csaba Szepesvári*, Lin Yang
Other Activities
Trivia Night hosted by Amii
Date: Friday, July 23 (3 - 4:30 pm MST)
Description: Join us for a virtual trivia night and an opportunity to learn more about the work we are doing at Amii! Teams will be assembled during the event and the winning team will walk away with a $25 gift card towards a celebratory meal from UberEATS.
Mentoring Program
Date: July 19-23
Description: Researchers from around the world are welcome to join these special sessions with leading AI & ML researchers. Mentors will share their research, career and life advice, and answer questions. Check out the list of amazing mentors here, including our own Marlos C. Machado, Nidhi Hegde, Matthew E. Taylor, Michael Bowling, Adam White and Robert Holte!
Service
In addition to his work being featured at the ICML Conference, Adam White also served as Social Chair.