Today marks the first day of the 2023 Eleventh International Conference on Learning Representation (ICLR) , taking place in Kigali, Rwanda from May 1 - 5.
ICLR is one of the premier conferences on representation learning, a branch of machine learning that focuses on transforming and extracting from data with the aim of identifying useful features or patterns within it. The conference draws in experts from around the world to present cutting-edge work with applications that extend to areas like computer vision, computational biology, gaming, robotics and more.
Amii's Fellows, CIFAR Canada AI Chairs and students are presenting dozens of posters, papers and workshops at this year's conference. Their work covers a wide variety of topics in representation learning and deep learning: everything from new prompting strategies to enable complex reasoning in large language models, to addressing challenges in weakly supervised learning, to making experience replay more sample-efficient.
For this year's conference, we challenged some of our affiliated students to explain their papers in one minute. Check out the videos below, as well as a breakdown of what Amii researchers are contributing to ICLR 2023.
(Entries with a * note someone supervised by an Amii Fellow and/or Canada CIFAR AI Chair)
In-Person Poster Presentations
Latent Variable Representation for Reinforcement Learning
Tongzheng Ren · Chenjun Xiao* · Tianjun Zhang · Na Li · Zhaoran Wang · sujay sanghavi · Dale Schuurmans · Bo Dai
Any-scale Balanced Samplers for Discrete Space
Haoran Sun · Bo Dai · Charles Sutton · Dale Schuurmans · Hanjun Dai
Spectral Decomposition Representation for Reinforcement Learning
Tongzheng Ren · Tianjun Zhang · Lisa Lee · Joseph E Gonzalez · Dale Schuurmans · Bo Dai
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay
Hongming Zhang · Chenjun Xiao*· Han Wang · Jun Jin · bo xu · Martin Müeller
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Denny Zhou · Nathanael Schaerli · Le Hou · Jason Wei · Nathan Scales · Xuezhi Wang · Dale Schuurmans · Claire Cui · Olivier Bousquet · Quoc V Le · Ed H. Chi
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Xuezhi Wang · Jason Wei ·Dale Schuurmans· Quoc V Le · Ed H. Chi · SHARAN NARANG · Aakanksha Chowdhery · Denny Zhou
TEMPERA: Test-Time Prompt Editing via Reinforcement Learning
Tianjun Zhang · Xuezhi Wang · Denny Zhou · Dale Schuurmans · Joseph E Gonzalez
Score-based Continuous-time Discrete Diffusion Models
Haoran Sun · Lijun Yu · Bo Dai · Dale Schuurmans · Hanjun Dai
Mutual Partial Label Learning with Competitive Label Noise
Yan Yan · Yuhong Guo
Sirui Zheng · Lingxiao Wang · Shuang Qiu · Zuyue Fu · Zhuoran Yang · Csaba Szepesvari · Zhaoran Wang
The In-Sample Softmax for Offline Reinforcement Learning
Chenjun Xiao* · Han Wang · Yangchen Pan · Adam White · Martha White
How to prepare your task head for finetuning
Yi Ren · Shangmin Guo · Wonho Bae · Danica Sutherland
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
Yuqiao Wen* · Yongchang Hao · Yanshuai Cao · Lili Mou
Efficient Conditionally Invariant Representation Learning
Roman Pogodin · Namrata Deka · Yazhe Li · Danica Sutherland · Victor Veitch · Arthur Gretton
Frederik Kunstner · Jacques Chen · Jonathan Lavington · Mark Schmidt
Dichotomy of Control: Separating What You Can Control from What You Cannot
Sherry Yang · Dale Schuurmans · Pieter Abbeel · Ofir Nachum
Neural Episodic Control with State Abstraction
Zhuo Li · Derui Zhu · Yujing Hu · Xiaofei Xie · Lei Ma · YAN ZHENG · Yan Song · Yingfeng Chen · Jianjun Zhao
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
Samuel Neumann* · Sungsu Lim · Ajin Joseph · Yangchen Pan · Adam White · Martha White
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
Andrew Patterson* · Adam White · Martha White
What learning algorithm is in-context learning? Investigations with linear models
Ekin Akyürek · Dale Schuurmans · Jacob Andreas · Tengyu Ma · Denny Zhou
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Alan Chan* · Hugo Silva · Sungsu Lim · Tadashi Kozuno* · A. Rupam Mahmood · Martha White
Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment
Yan Yan · Yuhong Guo
Oral Presentations
Dichotomy of Control: Separating What You Can Control from What You Cannot
Sherry Yang · Dale Schuurmans · Pieter Abbeel · Ofir Nachum
Efficient Conditionally Invariant Representation Learning
Roman Pogodin · Namrata Deka · Yazhe Li · Danica Sutherland · Victor Veitch · Arthur Gretton
Neural Episodic Control with State Abstraction
Zhuo Li · Derui Zhu · Yujing Hu · Xiaofei Xie · Lei Ma· Yan Zheng · Yan Song · Yingfeng Chen · Jianjun Zhao
What learning algorithm is in-context learning? Investigations with linear models
Ekin Akyürek · Dale Schuurmans· Jacob Andreas · Tengyu Ma · Denny Zhou