5 papers
LLM Agents for Automated Post-Training via Structured Recipe Optimization
Ruohong Liu, Bohan Tang, Lekang Jiang +2
Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructing and optimizing a recipe of…
Robust and learning-augmented algorithms for degradation-aware battery optimization
Jack Umenberger, Anna Osguthorpe Rasmussen
This paper studies the problem of maximizing revenue from a grid-scale battery energy storage system, accounting for uncertain future electricity prices and the effect of degradati…
BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
Ruohong Liu, Jack Umenberger, Yize Chen
Recent years have seen significant advancements in designing reinforcement learning (RL)-based agents for building energy management. While individual success is observed in simula…
Smoothed Analysis of Online Metric Problems
Christian Coester, Jack Umenberger
We study three classical online problems -- -server, -taxi, and chasing size sets -- through a lens of smoothed analysis. Our setting allows request locations to be adver…
Constrained Diffusers for Safe Planning and Control
Jichen Zhang, Liqun Zhao, Antonis Papachristodoulou +1
Diffusion models have shown remarkable potential in planning and control tasks due to their ability to represent multimodal distributions over actions and trajectories. However, en…