Publications

Jikai Jin, Vasilis Syrgkanis (2024). Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity. In NeurIPS 2024 (spotlight).

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Jikai Jin, Vasilis Syrgkanis (2024). Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation. In ArXiv preprint 2402.14264.

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Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu (2023). Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. In ICLR 2024.

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Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee (2023). Understanding Incremental Learning of Gradient Descent -- A Fine-grained Analysis of Matrix Sensing. In ICML 2023.

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Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying (2022). Minimax Optimal Kernel Operator Learning via Multilevel Training. In ICLR 2023 (spotlight).

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Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang (2022). Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. In NeurIPS 2022.

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Jikai Jin, Suvrit Sra (2022). Understanding Riemannian Acceleration via a Proximal Extragradient Framework. In COLT 2022.

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Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang (2021). Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis. In NeurIPS 2021.

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Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang (2020). Improved analysis of clipping algorithms for non-convex optimization. In NeurIPS 2020.

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