Andy Shih

• Imitation Learning by Estimating Expertise of Demonstrators
  Mark Beliaev* and Andy Shih* and Stefano Ermon and Dorsa Sadigh and Ramtin Pedarsani
  39th International Conference on Machine Learning, July 2022. (ICML 2022)
  [bib] [pdf]

• Conditional Imitation Learning for Multi-Agent Games
  Andy Shih and Stefano Ermon and Dorsa Sadigh
  17th ACM/IEEE International Conference on Human-Robot Interaction, March 2022. (HRI 2022)
  [bib] [pdf] [code]

• PantheonRL: A MARL Library for Dynamic Training Interactions
  Bidipta Sarkar* and Aditi Talati* and Andy Shih* and Dorsa Sadigh
  36th AAAI Conference on Artificial Intelligence (Demo Track), February 2022. (AAAI 2022 Demo Track)
  [bib] [pdf] [code] [video]

• HyperSPNs: Compact and Expressive Probabilistic Circuits
  Andy Shih and Dorsa Sadigh and Stefano Ermon
  35th Conference on Neural Information Processing Systems, December 2021. (NeurIPS 2021)
  [bib] [pdf] [slides] [poster] [code]

• Influencing Towards Stable Multi-Agent Interactions
  Woodrow Zhouyuan Wang and Andy Shih and Annie Xie and Dorsa Sadigh
  Proceedings of the 5th Conference on Robot Learning, November 2021. (CoRL 2021)
  [bib] [pdf]

• On the Critical Role of Conventions in Adaptive Human-AI Collaboration
  Andy Shih and Arjun Sawhney and Jovana Kondic and Stefano Ermon and Dorsa Sadigh
  9th International Conference on Learning Representations, May 2021. (ICLR 2021)
  [bib] [pdf] [slides] [poster] [code] [blogpost]

• Probabilistic Circuits for Variational Inference in Discrete Graphical Models
  Andy Shih and Stefano Ermon
  34th Conference on Neural Information Processing Systems, Vancouver, Canada, December 2020. (NeurIPS 2020)
  [bib] [pdf] [slides] [poster] [code] [blogpost]

• On Tractable Representations of Binary Neural Networks
  Weijia Shi and Andy Shih and Adnan Darwiche and Arthur Choi
  17th International Conference on Principles of Knowledge Representation and Reasoning, Rhodes, Greece, September 2020. (KR 2020)
  [bib] [pdf]

• On Symbolically Encoding the Behavior of Random Forests
  Arthur Choi and Andy Shih and Anchal Goyanka and Adnan Darwiche
  3rd Workshop on Formal Methods for ML-Enabled Autonomous Systems, July 2020. (FoMLAS 2020)
  [bib] [pdf]

• Smoothing Structured Decomposable Circuits
  Andy Shih and Guy Van den Broeck and Paul Beame and Antoine Amarilli
  33rd Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019. (NeurIPS 2019) [Spotlight]
  [bib] [pdf] [slides] [poster] [code]

• Explaining Classifiers
  Andy Shih
  Master's Thesis, UCLA Department of Computer Science, 2019.
  [bib] [pdf]

• Verifying Binarized Neural Networks by Angluin-Style Learning
  Andy Shih and Adnan Darwiche and Arthur Choi
  22nd International Conference on Theory and Applications of Satisfiability Testing, Lisbon, Portugal, July 2019. (SAT 2019)
  [bib] [pdf] [code]

• Compiling Neural Networks into Tractable Boolean Circuits
  Arthur Choi and Weijia Shi and Andy Shih and Adnan Darwiche
  AAAI Spring Symposium on Verification of Neural Networks, Stanford University, USA, March 2019. (VNN 2019)
  [bib] [pdf]

• Compiling Bayesian Network Classifiers into Decision Graphs
  Andy Shih and Arthur Choi and Adnan Darwiche
  33rd AAAI Conference on Artificial Intelligence, Honolulu, USA, January 2019. (AAAI 2019)
  [bib] [pdf] [code]

• Formal Verification of Bayesian Network Classifiers
  Andy Shih and Arthur Choi and Adnan Darwiche
  9th International Conference on Probabilistic Graphical Models, Prague, Czech Republic, September 2018. (PGM 2018)
  [bib] [pdf]

• A Symbolic Approach to Explaining Bayesian Network Classifiers
  Andy Shih and Arthur Choi and Adnan Darwiche
  27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, July 2018. (IJCAI 2018)
  [bib] [pdf] [code]

2019 UCLA Computer Science Outstanding Master's Student Award

Reviewer: ICML (2020, 2021), NeurIPS (2020, 2021), ICLR (2021), CoRL (2020, 2021)

Coach: Stanford ACM-ICPC (2020-present) [new webpage!]