3 citations · 6 across the 12 of their papers we have counts for
12 papers
Dominant Arm Identification with Mixing and Recycling Observed Samples
Jonghyun Sim, Wonyoung Kim
We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized reward…
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1
Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad applicability. While existing studi…
DiTTO-LLM: Framework for Discovering Topic-based Technology Opportunities via Large Language Model
Wonyoung Kim, Sujeong Seo, Juhyun Lee
Technology opportunities are critical information that serve as a foundation for advancements in technology, industry, and innovation. This paper proposes a framework based on the…
Adaptive Data Augmentation for Thompson Sampling
Wonyoung Kim
In linear contextual bandits, the objective is to select actions that maximize cumulative rewards, modeled as a linear function with unknown parameters. Although Thompson Sampling…
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar +2
We study the linear bandit problem that accounts for partially observable features. Without proper handling, unobserved features can lead to linear regret in the decision horizon $…
A Doubly Robust Approach to Sparse Reinforcement Learning
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We propose a new regret minimization algorithm for episodic sparse linear Markov decision process (SMDP) where the state-transition distribution is a linear function of observed fe…
Learning the Pareto Front Using Bootstrapped Observation Samples
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vec…
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget…
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits
Wonyoung Kim, Kyungbok Lee, Myunghee Cho Paik
We propose a novel contextual bandit algorithm for generalized linear rewards with an regret over rounds where is the minimum eigenvalue of th…
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh
We propose a linear contextual bandit algorithm with regret bound, where is the dimension of contexts and isthe time horizon. Our proposed algorithm is…
Doubly robust Thompson sampling for linear payoffs
Wonyoung Kim, Gi-soo Kim, Myunghee Cho Paik
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing. The dependence of the arm c…
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won +1
Wasserstein distributionally robust optimization (WDRO) attempts to learn a model that minimizes the local worst-case risk in the vicinity of the empirical data distribution define…