Jikai Jin

26 papers A* 9Journal 17
YearRankTypeTitle / Venue / Authors
2026 J jnl
Adaptive Exploration for Latent-State Bandits.
CoRR
Jikai Jin, Kenneth Hung, Sanath Kumar Krishnamurthy, Baoyi Shi, Congshan Zhang
2026 J jnl
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities.
CoRR
Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade
2025 J jnl
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning.
CoRR
Jikai Jin, Vasilis Syrgkanis, Sham M. Kakade, Hanlin Zhang
2025 J jnl
It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation.
CoRR
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
2025 J jnl
Policy Learning with Abstention.
CoRR
Ayush Sawarni, Jikai Jin, Justin Whitehouse, Vasilis Syrgkanis
2025 J jnl
Sharp Structure-Agnostic Lower Bounds for General Functional Estimation.
CoRR
Jikai Jin, Vasilis Syrgkanis
2025 J jnl
Solving Inequality Proofs with Large Language Models.
CoRR
Jiayi Sheng, Luna Lyu, Jikai Jin, Tony Xia, Alex Gu, James Zou, Pan Lu
2025 A* conf
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (Extended Abstract).
COLT
Jikai Jin, Vasilis Syrgkanis
2024 A* conf
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking.
ICLR
Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon Shaolei Du, Jason D. Lee, Wei Hu
2024 A* conf
Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity.
NeurIPS
Jikai Jin, Vasilis Syrgkanis
2024 J jnl
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation.
CoRR
Jikai Jin, Vasilis Syrgkanis
2023 J jnl
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking.
CoRR
Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu
2023 J jnl
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity.
CoRR
Jikai Jin, Vasilis Syrgkanis
2023 A* conf
Minimax Optimal Kernel Operator Learning via Multilevel Training.
ICLR
Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying
2023 A* conf
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing.
ICML
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee
2023 J jnl
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing.
CoRR
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee
2022 J jnl
Minimax Optimal Kernel Operator Learning via Multilevel Training.
CoRR
Jikai Jin, Yiping Lu, Jose H. Blanchet, Lexing Ying
2022 A* conf
Understanding Riemannian Acceleration via a Proximal Extragradient Framework.
COLT
Jikai Jin, Suvrit Sra
2022 A* conf
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power.
NeurIPS
Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang
2022 J jnl
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power.
CoRR
Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang
2021 J jnl
A Riemannian Accelerated Proximal Extragradient Framework and its Implications.
CoRR
Jikai Jin, Suvrit Sra
2021 A* conf
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis.
NeurIPS
Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang
2021 J jnl
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis.
CoRR
Jikai Jin, Bohang Zhang, Haiyang Wang, Liwei Wang
2020 A* conf
Improved Analysis of Clipping Algorithms for Non-convex Optimization.
NeurIPS
Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang
2020 J jnl
Improved Analysis of Clipping Algorithms for Non-convex Optimization.
CoRR
Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang
2020 J jnl
On The Convergence of First Order Methods for Quasar-Convex Optimization.
CoRR
Jikai Jin