Scholar
Taehyun Cho
Google Scholar ID: kVi85ZgAAAAJ
Seoul National University
Citations & Impact
Citations
51
H-index
5
i10-index
2
Publications
14
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- - Publications:
- - 'Policy-labeled Preference Learning: Is Preference Enough for RLHF?' (ICML 2025)
- - 'Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation' (ICML 2025)
- - 'Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees' (NeurIPS 2024)
- - 'Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion' (NeurIPS 2023)
- - 'SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning' (NeurIPS 2023)
- - 'On the Convergence of Continual Learning with Adaptive Methods' (UAI 2023)
- - 'Adaptive Methods for Nonconvex Continual Learning' (NeurIPS 2022)
- - 'Perturbed Quantile Regression for Distributional Reinforcement Learning' (NeurIPS 2022)
- - 'Chebyshev polynomial codes: Task entanglement-based coding for distributed matrix multiplication' (ICML 2021)
- - 'Optimized shallow neural networks for sum-rate maximization in energy harvesting downlink multiuser NOMA systems' (IEEE Journal on Selected Areas in Communications)
- - 'An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels' (2020 IEEE International Symposium on Information Theory (ISIT))
Research Experience
- - Research Projects:
- - Preprints: 'An Axiomatization of Process Score Model', etc.
- - Work In Progress: 'Off-policy Direct Preference Optimization with Monotonic Improvement Guarantee', etc.
- - Work In Progress: 'Policy Optimization with Process Regret Model', etc.
- - Position: Ph.D. Candidate
Education
- - Degree: Ph.D. Candidate
- - University: Seoul National University
- - Advisor: Jungwoo Lee
- - Year: Final year
- - Department: Electrical and Computer Engineering
- - Bachelor's Degree: Mathematics
- - University: Korea University
Background
- - Research Interests: Sequential decision-making under uncertainty, particularly in the context of human feedback
- - Specialization: Distributional reinforcement learning (distRL), reinforcement learning from human feedback (RLHF), and regret analysis
- - Goal: To develop mathematical models and optimize for human-in-the-loop systems, uncovering both theoretical insights and practical algorithms for robust decision-making
- - Current Interests: Reasoning LLM agents and regret-based decision theory
Miscellany
- - Personal Interests: Actively looking for postdoctoral opportunities in theoretical foundations of reinforcement learning or reasoning LLM research
Co-authors