Baekjin Kim

11 papers A* 3A 2Journal 6
YearRankTypeTitle / Venue / Authors
2022 A* conf
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation.
KDD
Ahmed El-Kishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi
2022 J jnl
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation.
CoRR
Ahmed El-Kishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi
2022 A conf
Weighted Gaussian Process Bandits for Non-stationary Environments.
AISTATS
Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff
2021 J jnl
Weighted Gaussian Process Bandits for Non-stationary Environments.
CoRR
Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff
2020 A* conf
On the Equivalence between Online and Private Learnability beyond Binary Classification.
NeurIPS
Young Hun Jung, Baekjin Kim, Ambuj Tewari
2020 J jnl
On the Equivalence between Online and Private Learnability beyond Binary Classification.
CoRR
Young Hun Jung, Baekjin Kim, Ambuj Tewari
2020 A conf
Randomized Exploration for Non-Stationary Stochastic Linear Bandits.
UAI
Baekjin Kim, Ambuj Tewari
2019 J jnl
Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits.
CoRR
Baekjin Kim, Ambuj Tewari
2019 A* conf
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems.
NeurIPS
Baekjin Kim, Ambuj Tewari
2019 J jnl
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems.
CoRR
Baekjin Kim, Ambuj Tewari
2018 J jnl
Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data.
Appl. Intell.
Baekjin Kim, Donghyeon Yu, Joong-Ho Won