Jeongyeol Kwon
20 papers · 2019–2026 · 7 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (6)
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Hot Topic Early Bird
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Renaissance Researcher
(5)
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Conference Polyglot
(6)
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Dynamic Duo
(12)
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Triple Crown
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Unstoppable
(7)
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Century Club
(19)
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Prolific Year
(5)
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Keyword Collector
(71)
Conferences
ICML (7)
NIPS (4)
AISTATS (3)
COLT (3)
EACL (1)
ICLR (1)
JMLR (1)
Top co-authors
Keywords
reinforcement learning
(4)
convergence rate
(3)
markov decision process
(2)
reward mixing
(2)
contextual bandit
(2)
sample complexity
(2)
expectation maximization
(2)
signal-to-noise ratio
(2)
latent context
(2)
parameter estimation
(2)
latent mdp
(2)
multi-task learning
(2)
convergence analysis
(1)
global convergence
(1)
adversarial robustness
(1)
margin-based learning
(1)
em algorithm
(1)
mixed linear regression
(1)
out-of-distribution generalization
(1)
domain generalization
(1)
Papers
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
EACL 2026
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
A Classification View on Meta Learning Bandits
ICML 2025
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
ICLR 2024
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
NIPS 2024
Prospective Side Information for Latent MDPs
ICML 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
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
Tractable Optimality in Episodic Latent MABs
NIPS 2022
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
ICML 2022
RL for Latent MDPs: Regret Guarantees and a Lower Bound
NIPS 2021
Reinforcement Learning in Reward-Mixing MDPs
NIPS 2021
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
AISTATS 2021
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians
COLT 2020
EM Converges for a Mixture of Many Linear Regressions
AISTATS 2020
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
COLT 2019