29 citations · 97 across the 89 of their papers we have counts for
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Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards
Zixun Huang, Jiayi Sheng, Zeyu Zheng
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective paradigm for post-training large language models, yet the design of its baselines and learning-rat…
SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations
Haoting Zhang, Haoxian Chen, Donglin Zhan +5
The field of simulation optimization (SO) encompasses various methods developed to optimize complex, expensive-to-sample stochastic systems. Established methods include, but are no…
Budgeted Prompt-Set Expansion and Refinement for Language Models via Simulation Optimization
Haoting Zhang, Jinghai He, Rhonda Righter +1
A prompt is an instruction or description provided by the user to guide a generative language model toward outputs that meet application requirements. When these requirements chang…
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
David Simchi-Levi, Zeyu Zheng, Feng Zhu
We study the optimal trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit model. We fully characterize the interplay among three…
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits
David Simchi-Levi, Zeyu Zheng, Feng Zhu
We study the stochastic multi-armed bandit problem and design new policies that enjoy both worst-case optimality for expected regret and light-tailed risk for regret distribution.…
Offline Planning and Online Learning under Recovering Rewards
David Simchi-Levi, Zeyu Zheng, Feng Zhu
Motivated by emerging applications such as live-streaming e-commerce, promotions and recommendations, we introduce and solve a general class of non-stationary multi-armed bandit pr…
A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
Yufeng Zheng, Zeyu Zheng, Tingyu Zhu
We propose a framework that integrates classical Monte Carlo simulators and Wasserstein generative adversarial networks to model, estimate, and simulate a broad class of arrival pr…