7 papers
Relative Translation Invariant Wasserstein Distance
Binshuai Wang, Qiwei Di, Ming Yin +3
Motivated by the Bures distance, we introduce a new family of distances, \emph{relative translation invariant Wasserstein distances}, denoted by , as an extension of the clas…
On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
Kaixuan Ji, Qiwei Di, Heyang Zhao +2
Kullback-Leibler (KL) regularization is widely used in offline decision-making and offers several benefits, motivating recent work on the sample complexity of offline learning with…
Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
Shiyuan Zhang, Qiwei Di, Xuheng Li +1
Underdamped Langevin dynamics (ULD) is a widely-used sampler for Gibbs distributions , and is often empirically effective in high dimensions. However, existing no…
Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
Kaixuan Ji, Qingyue Zhao, Heyang Zhao +2
Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqr…
On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference
Yue Yu, Qiwei Di, Quanquan Gu +1
Test-time compute (TTC) has become an increasingly prominent paradigm for enhancing large language models (LLMs). Despite the empirical success of methods such as best-of- (BoN)…
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
Qiwei Di, Jiafan He, Quanquan Gu
Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM). However, the effectiveness of this approach can be influenc…