10 citations · 32 across the 25 of their papers we have counts for
6 papers · 1 filter
Distributionally Robust Average-Reward Reinforcement Learning: Finite-Sample Guarantees under Weak Communication
Chenyu Lu, Zijun Chen, Nian Si
We study distributionally robust reinforcement learning (DR-RL) in the average-reward setting under weak communication. Our main result provides finite-sample guarantees for estima…
Diffusion-Based Policies for Dynamic Control of Stochastic Processing Networks
Baris Ata, Jin Guang, J. Michael Harrison +1
We consider a processing network model with job classes or buffers, exogenous input flows into some classes, processing activities, and servers. Each activity is either…
Causal Estimation of Share-Induced Engagement with Flywheel Effects
Weitao Cheng, Yilin Li, Yong Wang +1
Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-desi…
Experimentation for Different Scheduling Policies on Queues: Mixed Differences-in-Q Estimators Based on Little's Law
Nanshan Jia, Ramesh Johari, Nian Si +1
In data centers, tasks are dispatched to various servers to evenly distribute the workload. When a data center considers implementing a new scheduling algorithm, it typically condu…
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…