20 papers
Refined Thompson Learning for Adaptive Bandits: Sustainable Power-Efficient Flexibility Scheduling Across Data Centers
Yifu Ding, Zixi Chen, Ruicheng Ao +2
The rapid rise in energy consumption from large-scale AI workloads in data centers placed the increasing pressures on power grids in recent years. Since grids must maintain real-ti…
Semiparametric Efficiency in Sequential Experiments: Characterization and Design via Average Propensity
Jiachun Li, David Simchi-Levi
Modern experiments, including evaluations of AI-enabled services and platform interventions, often depart from independent and identically distributed (i.i.d.) sampling because ass…
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Jiachun Li, David Simchi-Levi
Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-depe…
Service-Induced Congestion in Memory-Constrained LLM Serving
Ruicheng Ao, Jing Dong, Gan Luo +1
In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated to…
Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
Ruicheng Ao, Gan Luo, David Simchi-Levi +1
Large language models now serve millions of users daily, with providers incurring costs exceeding $700,000 per day. Each request requires token-by-token inference, making GPU sched…
Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
Hongyu Chen, David Simchi-Levi, Ruoxuan Xiong
Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNA…