4 citations · 9 across the 5 of their papers we have counts for
5 papers
Context-Aware Robust Fine-Tuning
Xiaofeng Mao, Yuefeng Chen, Xiaojun Jia +3
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sent…
Instance-optimal PAC Algorithms for Contextual Bandits
Zhaoqi Li, Lillian Ratliff, Houssam Nassif +2
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remai…
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Chao Lin, Zhao Li, Sheng Zhou +6
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce.Existing VTON approaches can be narrowly categorized into Pars…
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
Linhao Luo, Yumeng Li, Buyu Gao +7
Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendat…
Thompson Sampling for Unimodal Bandits
Long Yang, Zhao Li, Zehong Hu +4
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal…