Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. - Baekjin Kim; Donghyeon Yu; Joong-Ho Won (Appl. Intell., 2018)

On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. - Baekjin Kim; Ambuj Tewari (NeurIPS, 2019)

Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits. - Baekjin Kim; Ambuj Tewari (CoRR, 2019)

On the Equivalence between Online and Private Learnability beyond Binary Classification. - Young Hun Jung; Baekjin Kim; Ambuj Tewari (NeurIPS, 2020)

Randomized Exploration for Non-Stationary Stochastic Linear Bandits. - Baekjin Kim; Ambuj Tewari (UAI, 2020)

Weighted Gaussian Process Bandits for Non-stationary Environments. - Yuntian Deng; Xingyu Zhou 001; Baekjin Kim; Ambuj Tewari; Abhishek Gupta 002; Ness B. Shroff (AISTATS, 2022)

TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation. - Ahmed El-Kishky; Thomas Markovich; Serim Park; Chetan Verma; Baekjin Kim; Ramy Eskander; Yury Malkov; Frank Portman; Sofía Samaniego; Ying Xiao; Aria Haghighi (KDD, 2022)