8 citations · 27 across the 10 of their papers we have counts for
11 papers
SCOPE: Cost-Efficient Model Selection for Compound AI Systems under Quality Constraints
Yiqian Huang, Shiqi Zhang, Tianyuan Jin +1
A compound AI system consists of multiple LLM modules, together handling complex and multi-step tasks that exceed the capabilities of a single model. Existing systems often use a s…
SteerConf: Steering LLMs for Confidence Elicitation
Ziang Zhou, Tianyuan Jin, Jieming Shi +1
Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence, limiting their reliability in critical applications. We pro…
Optimal Streaming Algorithms for Multi-Armed Bandits
Tianyuan Jin, Keke Huang, Jing Tang +1
This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of arms with reward distributions supported on $[…
Optimal Batched Linear Bandits
Xuanfei Ren, Tianyuan Jin, Pan Xu
We introduce the E algorithm for the batched linear bandit problem, incorporating an Explore-Estimate-Eliminate-Exploit framework. With a proper choice of exploration rate, we…
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Tianyuan Jin, Hao-Lun Hsu, William Chang +1
We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored into overlapping groups. Each group represents a hyperedge, forming a hypergraph over…
Optimal Batched Best Arm Identification
Tianyuan Jin, Yu Yang, Jing Tang +2
We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we…
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
Tianyuan Jin, Pan Xu, Xiaokui Xiao +1
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers ma…
MOTS: Minimax Optimal Thompson Sampling
Tianyuan Jin, Pan Xu, Jieming Shi +2
Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other…
Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs
Jieming Shi, Tianyuan Jin, Renchi Yang +2
Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation…
Double Explore-then-Commit: Asymptotic Optimality and Beyond
Tianyuan Jin, Pan Xu, Xiaokui Xiao +1
We study the multi-armed bandit problem with subgaussian rewards. The explore-then-commit (ETC) strategy, which consists of an exploration phase followed by an exploitation phase,…
Tracking Top-K Influential Vertices in Dynamic Networks
Yu Yang, Zhefeng Wang, Tianyuan Jin +2
Influence propagation in networks has enjoyed fruitful applications and has been extensively studied in literature. However, only very limited preliminary studies tackled the chall…