Chun Kai Ling

Chun Kai Ling

Postdoctoral Research Scientist

Columbia University

Biography

I am a Postdoctoral Research Scientist at Columbia University working with Professors Christian Kroer and Garud Iyengar. I completed my PhD at Carnegie Mellon University under the supervision of Zico Kolter and Fei Fang. I am broadly interested in artificial intelligence. My current projects involve designing machine learning techniques to learn and solve large games.

I will be joining the Department of Computer Science in National University of Singapore (NUS) as an Assistant Professor in Fall 2024 and will be interested in taking students. If you are a prospective student interested in working in AI, Computational Game Theory, or Machine Learning, feel free to send an email reaching out for a discussion!

Download my resumé.

Interests
  • Artificial Intelligence
  • Machine Learning
  • Game Theory
Education
  • PhD in Computer Science, 2017-2023

    Carnegie Mellon University

  • BEng in Computer Engineering, 2015

    National University of Singapore

Recent Publications

(2024). Online bipartite matching with imperfect advice. Accepted to ICML 2024 main track.

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(2024). Layered Graph Security Games. Accepted to IJCAI 2024 main track.

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(2024). Quantifying Interactions in Semi-supervised Multimodal Learning: Guarantees and Applications. Accepted to ICLR 2024 Main Track.

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(2023). Deep Copula-based Survival Analysis for Dependent Censoring with Identifiability Guarantees. Accepted to AAAI 2024 Main Track.

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(2023). Learning Coalition Structures with Games. Accepted to AAAI 2024 Main Track.

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(2023). Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework. In Neurips 2023 Main Track.

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(2023). Multi-defender Security Games with Schedules. In GameSec 2023. Best Paper Award.

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(2023). Abstracting Imperfect Information Away from Two-Player Zero-Sum Games. In ICML 2023 Main Track.

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(2022). Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games. In AAAI 2023 main track (oral).

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(2022). Safe Subgame Resolving for Extensive Form Correlated Equilibrium. In AAAI 2022 main track (oral).

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(2020). Deep Archimedean Copulas. Accepted to Neurips 2020 main track.

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(2020). Safe Search for Stackelberg Equilibria in Extensive-Form Games. Accepted to AAAI 2021 main track.

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(2020). Nonmyopic Gaussian Process Optimization with Macro-Actions. In AISTATS.

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(2019). Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks. In Neurips main track.

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(2019). Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium. In Neurips main track (Oral).

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(2019). Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games. In AAAI 2019 main track.

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(2018). What game are we playing? End-to-end learning in normal and extensive form games. In IJCAI 2018 main track. Distinguished Paper Award..

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(2017). What game are we playing? Differentiably learning games from incomplete observations. In NIPS 2017 Deep RL Symposium.

Preprint PDF Poster

(2016). Gaussian Process Planning with Lipschitz Continuous Reward Functions. In AAAI.

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