10 citations · 11 across the 4 of their papers we have counts for
6 papers
Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices
Qian Xie, Yueli He, Nairen Cao
Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware…
LLM-Driven Composite Neural Architecture Search for Multi-Source RL State Encoding
Yu Yu, Qian Xie, Nairen Cao +1
Designing state encoders for reinforcement learning (RL) with multiple information sources -- such as sensor measurements, time-series signals, image observations, and textual inst…
Static to Dynamic Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee +7
Correlation clustering is a well-studied problem, first proposed by Bansal, Blum, and Chawla [Mach. Learn. '04]. The input is an unweighted, undirected graph. The problem is to clu…
Solving the Correlation Cluster LP in Sublinear Time
Nairen Cao, Vincent Cohen-Addad, Shi Li +7
Correlation Clustering is a fundamental and widely-studied problem in unsupervised learning and data mining. The input is a graph and the goal is to construct a clustering minimizi…
Simultaneously Approximating All Norms for Massively Parallel Correlation Clustering
Nairen Cao, Shi Li, Jia Ye
We revisit the simultaneous approximation model for the correlation clustering problem introduced by Davies, Moseley, and Newman[DMN24]. The objective is to find a clustering that…
Understanding the Cluster LP for Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee +3
In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla (FOCS 2002), the input is a complete graph where edges are labeled either or , and the g…