6 papers
A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
Chengyi Nie, Nian Si, Zijie Zhou
The rapid adoption of large language models (LLMs) has created significant challenges for efficient inference at scale. Unlike traditional workloads, LLM inference is constrained b…
FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains
Jiashuo Liu, Siyuan Chen, Zaiyuan Wang +38
Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, Futu…
Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces
Shengbo Wang, Jason Meng, Nian Si +2
We study data-driven learning of robust stochastic control for infinite-horizon systems with potentially continuous state and action spaces. In many managerial settings--supply cha…
Selecting the Best Optimizing System
Nian Si, Yifu Tang, Zeyu Zheng
We formulate selecting the best optimizing system (SBOS) problems and provide solutions for those problems. In an SBOS problem, a finite number of systems are contenders. Inside ea…
On the Foundation of Distributionally Robust Reinforcement Learning
Shengbo Wang, Nian Si, Jose Blanchet +1
Motivated by the need for a robust policy in the face of environment shifts between training and deployment, we contribute to the theoretical foundation of distributionally robust…
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters
Hao Liu, Junze Tony Ye, Jose Blanchet +1
We introduce ScoreFusion, a theoretically grounded method for fusing multiple pre-trained diffusion models that are assumed to generate from auxiliary populations. ScoreFusion is p…