Jikai Jin

Jikai Jin

Ph.D. student

Stanford university

Biography

Welcome to my personal website! I am a first-year Ph.D. student in The Institute for Computational and Mathematical Engineering (ICME), Stanford university. Prior to joining Stanford, I obtained my bachelor degree in computational mathematics in the School of Mathematical Sciences, Peking University, fortunately having Prof. Liwei Wang as my research advisor. My research is highly interdisciplinary across machine learning, statistics and operations research. While currently focusing on theoretical aspects, the ultimate goal of my research is to develop state-of-the-art solutions for important real-world problems. If you share similar interest, feel free to contact me via email or Wechat. Download my resumé.

Interests
  • Statistics (non-parametric estimation and causal inference)
  • Operations research (experiment design, uncertainty in decision-making)
  • Machine Learning (theory, algorithms, social aspects and causal ML)
Education

  • Ph.D. in Computational and Mathematical Engineering, 2023 - 2028 (expected)

    Stanford University

  • BSc in Computational Mathematics, 2019 - 2023

    Peking University

  • High School Diploma, 2017 - 2019

    No.2 High School of East China Normal University

Recent News

All news»

Feb. 2024 New paper Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation with Vasilis Syrgkanis posted on ArXiv! Fantastic results showing that first-order debiasing is optimal for black-box ML-based nuisance estimators.

Jan. 2024 Our paper Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking is accepted by ICLR 2024!

Nov. 2023 New paper Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking with Keifeng Lyu, Zhiyuan Li, Simon S. Du, Jason D. Lee and Wei Hu posted on ArXiv! Welcome any feedback or comments.

Nov. 2023 New paper Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity with Vasilis Syrgkanis posted on ArXiv! Welcome any feedback or comments.

Oct. 2023 Give a talk at the 2023 INFORMS Annual Meeting based on our incremental learning paper. Had a wonderful time in Phoenix!

Recent Publications

Quickly discover relevant content by filtering publications.
Jikai Jin, Vasilis Syrgkanis (2024). Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation. In ArXiv preprint 2402.14264.

PDF ArXiv

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.

Cite ArXiv

Jikai Jin, Vasilis Syrgkanis (2023). Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity. In ArXiv preprint 2311.12267.

Cite ArXiv

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.

PDF Cite ArXiv Poster

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

PDF Cite ArXiv Slides Poster

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.

PDF Cite ArXiv

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

PDF Cite ArXiv

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

PDF Cite ArXiv

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

PDF Cite ArXiv

Experience

 
 
 
 
 
advised by Prof. Liwei Wang (Peking University)
Unergrduate Research Intern
advised by Prof. Liwei Wang (Peking University)
Feb 2020 – Present Beijing, China
Work on machine learning theory.

Awards and Honors

All awards and honors»

Jun. 2023 Huaixin Scholar, BICMR

Jun. 2023 Peking University Excellent Graduate.

Jan. 2023 Sensetime Scholarship (awarded to 30 Chinese undergraduate students in the field of AI).

Oct. 2022 Qin Wanshun-Jin Yunhui Scholarship, Peking University.

Oct. 2022 Merit Student, Peking University.

Oct. 2021 Exceptional award for academic innovation, Peking University.

Jun. 2021 Elite undergraduate training program of Applied Mathematics and Statistics.

May 2021 Bronze Medal, S.T. Yau College Student Mathematics Competition, probability and statistics individual.

Dec. 2020 Qin Wanshun-Jin Yunhui Scholarship, Peking University.

Oct. 2020 Yizheng Scholarship, Peking University.

Feb. 2019 Silver Medal, 11th Romania Masters of Mathematics.

Oct. 2018 First Prize (ranked No.6), Chinese Mathematical Olympiad (CMO).

Oct. 2017 First Prize (ranked No.13), Chinese Mathematical Olympiad (CMO).

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