1 citations · 1 across the 3 of their papers we have counts for
10 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…