7 citations · 9 across the 9 of their papers we have counts for
10 papers
No-Regret Linear Bandits under Gap-Adjusted Misspecification
Chong Liu, Dan Qiao, Ming Yin +2
This work studies linear bandits under a new notion of gap-adjusted misspecification and is an extension of Liu et al. (2023). When the underlying reward function is not linear, ex…
Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li, Chong Liu, Yu-Xiang Wang
Federated optimization studies the problem of collaborative function optimization among multiple clients (e.g. mobile devices or organizations) under the coordination of a central…
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
Chong Liu, Xiaoyang Liu, Ruobing Xie +3
Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive ite…
No-Regret Linear Bandits beyond Realizability
Chong Liu, Ming Yin, Yu-Xiang Wang
We study linear bandits when the underlying reward function is not linear. Existing work relies on a uniform misspecification parameter that measures the sup-norm error of the…
Global Optimization with Parametric Function Approximation
Chong Liu, Yu-Xiang Wang
We consider the problem of global optimization with noisy zeroth order oracles - a well-motivated problem useful for various applications ranging from hyper-parameter tuning for de…
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Cristopher Salvi, Maud Lemercier, Chong Liu +3
Stochastic processes are random variables with values in some space of paths. However, reducing a stochastic process to a path-valued random variable ignores its filtration, i.e. t…
Vision-Language Navigation with Random Environmental Mixup
Chong Liu, Fengda Zhu, Xiaojun Chang +3
Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Large da…
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri +1
The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning. Existing theoretical analysis shows…
Doubly Robust Crowdsourcing
Chong Liu, Yu-Xiang Wang
Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon…