Jeongyeol Kwon
20 papers · 2019–2026 · 7 venues · across top CS/AI venues
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Research topics
Machine Learning
(17)
Optimization & Theory
(14)
Mathematics & Optimization
(7)
Reinforcement Learning
(7)
Methods
(6)
Optimization
(6)
Core Methods
(5)
Learning Types
(5)
Artificial Intelligence
(3)
Bayesian & Probabilistic
(2)
Core AI
(2)
Data Science & Analytics
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Applications Β· Data Science & Analytics
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Applications Β· Reinforcement Learning
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Keywords
reinforcement learning
(4)
contextual bandit
(4)
sample complexity
(3)
latent mdp
(3)
bilevel optimization
(3)
expectation-maximization algorithm
(3)
convergence rate
(3)
parameter estimation
(2)
stationary point
(2)
latent context
(2)
multi-task learning
(2)
regret
(2)
gaussian mixture model
(2)
first-order method
(2)
markov decision process
(2)
signal-to-noise ratio
(2)
expectation maximization
(2)
convergence analysis
(2)
policy learning
(2)
reward mixing
(2)
Papers
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
EACL 2026
A Classification View on Meta Learning Bandits
ICML 2025
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing
COLT 2025
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
AISTATS 2025
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
ICLR 2024
On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
ICML 2024
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing
JMLR 2024
Prospective Side Information for Latent MDPs
ICML 2024
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
NeurIPS 2024
A Fully First-Order Method for Stochastic Bilevel Optimization
ICML 2023
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
ICML 2023
Reward-Mixing MDPs with Few Latent Contexts are Learnable
ICML 2023
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
ICML 2022
Tractable Optimality in Episodic Latent MABs
NeurIPS 2022
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
AISTATS 2021
Reinforcement Learning in Reward-Mixing MDPs
NeurIPS 2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound
NeurIPS 2021
EM Converges for a Mixture of Many Linear Regressions
AISTATS 2020
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians
COLT 2020
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
COLT 2019