I am a fourth-year PhD student in the Computer Science department at Stanford University, co-advised by Stefano Ermon and Dorsa Sadigh. I am a part of the Stanford Artificial Intelligence Laboratory (SAIL) and Stanford Machine Learning Group.
I got my BS and MS in Computer Science from UCLA, where I worked in the Automated Reasoning Group with Arthur Choi and Adnan Darwiche.
Here is my CV.
Training and Inference on Any-Order Autoregressive Models the Right Way
Andy Shih and Dorsa Sadigh and Stefano Ermon
36th Conference on Neural Information Processing Systems, December 2022. (NeurIPS 2022) [Oral]
[bib]
[arxiv]
[pdf]
[slides]
[poster]
[code]
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]
[arxiv]
[pdf]
[code]
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]
[arxiv]
[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]
[arxiv]
[pdf]
[code]
[video]
On the Opportunities and Risks of Foundation Models
Co-author: Section 2.4 Reasoning and Search
Stanford Center for Research on Foundation Models (CRFM)
[report]
[arxiv]
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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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]
[arxiv]
[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!]
andyshih at stanford dot edu