Baekjin Kim

Publication Activity (10 Years)

Years Active: 2018-2022
Publications (10 Years): 11

Top Topics

Bandit Problems

Top Venues

Publications

Ahmed El-Kishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation. CoRR (2022)
Ahmed El-Kishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation. KDD (2022)
Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff
Weighted Gaussian Process Bandits for Non-stationary Environments. AISTATS (2022)
Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff
Weighted Gaussian Process Bandits for Non-stationary Environments. CoRR (2021)
Young Hun Jung, Baekjin Kim, Ambuj Tewari
On the Equivalence between Online and Private Learnability beyond Binary Classification. NeurIPS (2020)
Young Hun Jung, Baekjin Kim, Ambuj Tewari
On the Equivalence between Online and Private Learnability beyond Binary Classification. CoRR (2020)
Baekjin Kim, Ambuj Tewari
Randomized Exploration for Non-Stationary Stochastic Linear Bandits. UAI (2020)
Baekjin Kim, Ambuj Tewari
Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits. CoRR (2019)
Baekjin Kim, Ambuj Tewari
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. CoRR (2019)
Baekjin Kim, Ambuj Tewari
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. NeurIPS (2019)
Baekjin Kim, Donghyeon Yu, Joong-Ho Won
Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. Appl. Intell. 48 (8) (2018)