Yangchen Pan

I am Yangchen Pan (Google Scholar), a PhD candidate in the Department of Computing Science, University of Alberta. My PhD advisors are Dr. Martha White (University of Alberta) and Dr. Amir-massoud Farahmand (University of Toronto & Vector Institute). I am broadly interested in machine learning, deep learning, and reinforcement learning. My most recent work includes model-based reinforcement learning, representation learning, parametric modal regression.

Publications

Refereed Conference Publications

* indicates co-first authorship.

• Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online. [paper]
  Yangchen Pan, Kirby Banman, White Martha.
  International Conference on Learning Representations (ICLR), 2021.

• An implicit function learning approach for parametric modal regression. [paper]
  Yangchen Pan, Ehsan Imani, Martha White, Amir-massoud Farahmand.
  Conference on Neural Information Processing Systems (NeurIPS), 2020.

• Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. [paper]
  Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White.
  International Conference on Learning Representations (ICLR), 2020.

• Frequency-based Search-control in Dyna. [paper]
  Yangchen Pan *, Jincheng Mei *, Amir-massoud Farahmand.
  International Conference on Learning Representations (ICLR), 2020.

• Hill Climbing on Value Estimates for Search-control in Dyna. [paper]
  Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White.
  International Joint Conference on Artificial Intelligence (IJCAI), 2019.

• Reinforcement learning with function-valued action spaces for partial differential equation control. [paper]
  Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski.
  International Conference on Machine Learning (ICML), 2018.

• Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains. [paper]
  Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White.
  International Joint Conference on Artificial Intelligence (IJCAI), 2018.

• Adapting kernel representations online using submodular maximization. [paper]
  Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White.
  International Conference on Machine Learning (ICML), 2017.

• Effective sketching methods for value function approximation. [paper]
  Yangchen Pan, Erfan Sadeqi Azer, Martha White.
  Conference on Uncertainty in Artificial Intelligence (UAI), 2017.

• Accelerated gradient temporal difference learning. [paper]
  Yangchen Pan, Adam White, Martha White.
  AAAI Conference on Artificial Intelligence (AAAI), 2017.

• Incremental truncated LSTD. [paper]
  Clement Gehring, Yangchen Pan, Martha White.
  International Joint Conference on Artificial Intelligence (IJCAI), 2016.

Preprints/Workshop/Work in progress

• Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. [paper]
  Jincheng Mei *, Yangchen Pan *, Amir-massoud Farahmand, Martha White, Hengshuai Yao.
  Preprint. Work in progress. 2021.

• Actor-expert: A framework for using action-value methods in continuous action spaces. [paper]
  Sungsu Lim, Ajin Joseph, Lei Le, Yangchen Pan, Martha White.
  Preprint. Work in progress. 2021.

• Accelerated Gradient Temporal Difference Learning. [paper]
  Yangchen Pan, Adam White, Martha White.
  European workshop on reinforcement learning (EWRL), 2017.

Academic Service

Conference Program Committee Member, Reviewer

NeurIPS 2018, 2020.
ICML 2019, 2021.
ICLR 2020, 2021.
AISTATS 2021.
AAAI 2019-2021.

Conference Sub-reviewer

NeurIPS 2020.
ICML 2018.
ICLR 2017, 2019.
AAAI 2017, 2018.
AAMAS 2018.

Journal Reviewer

Journal of Machine Learning Research (JMLR) 2020, one paper

Teaching Experiences

2019 Fall: CMPUT 466/566, Machine Learning, Teaching Assistant, University of Alberta

2019 Spring: CMPUT 272, Formal Systems and Logic in Computing Science, Teaching Assistant, University of Alberta

2016 Spring: CSCI C343, Data Structure, Associate Instructor, Indiana University at Bloomington

2015 Fall: CSCI B503, Algorithm Design and Analysis, Associate Instructor, Indiana University at Bloomington

2014 Fall: CSCI 1311 Discrete Structure I, Teaching Assistant, George Washington University

Working Experience

Research Intern at Huawei Technologies, Edmonton, AB, Canada, 05/2018-12/2018

Research Intern at Mitsubishi Electric Research Laboratories, Boston, MA, the United States, 05/2017-08/2017