9 citations · 17 across the 13 of their papers we have counts for
15 papers
Maximizing Compute Capacity in AI Data Centers through Cooling, Energy Storage, and Computing Adaptation
Shaolei Ren, Mohammad A. Islam, Adam Wierman
The deployment of artificial intelligence is increasingly constrained by limited site-level power capacity, which must support both compute systems and non-compute systems (primari…
Online Smoothed Demand Management
Adam Lechowicz, Nicolas Christianson, Mohammad Hajiesmaili +2
We introduce and study a class of online problems called online smoothed demand management , motivated by paradigm shifts in grid integration and energy storage fo…
Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
Christopher Yeh, Nicolas Christianson, Adam Wierman +1
While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical f…
Competitive Algorithms for Multi-Agent Ski-Rental Problems
Xuchuang Wang, Bo Sun, Hedyeh Beyhaghi +3
This paper introduces a novel multi-agent ski-rental problem that generalizes the classical ski-rental dilemma to a group setting where agents incur individual and shared costs. In…
Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization
James A. Preiss, Fengze Xie, Yiheng Lin +2
We study online algorithms to tune the parameters of a robot controller in a setting where the dynamics, policy class, and optimality objective are all time-varying. The system fol…
Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
Yiheng Lin, Christopher Yeh, Zaiwei Chen +1
We study the value of stochastic predictions in online optimal control with random disturbances. Prior work provides performance guarantees based on prediction error but ignores th…
Fusing Reward and Dueling Feedback in Stochastic Bandits
Xuchuang Wang, Qirun Zeng, Jinhang Zuo +4
This paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits, where both feedback types are gathered in each decision round. We der…
Carbon- and Precedence-Aware Scheduling for Data Processing Clusters
Adam Lechowicz, Rohan Shenoy, Noman Bashir +3
As large-scale data processing workloads continue to grow, their carbon footprint raises concerns. Prior research on carbon-aware schedulers has focused on shifting computation to…
Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers
Yuelin Han, Zhifeng Wu, Pengfei Li +2
The surging demand for artificial intelligence (AI) has led to a rapid expansion of energy-intensive data centers, contributing to criteria air pollutant emissions and raising publ…
End-to-End Conformal Calibration for Optimization Under Uncertainty
Christopher Yeh, Nicolas Christianson, Alan Wu +2
Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-c…
Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study spatiotemporal online allocation with deadline constraints (), a new online problem motivated by emerging challenges in sustainability and ene…
Approximate Global Convergence of Independent Learning in Multi-Agent Systems
Ruiyang Jin, Zaiwei Chen, Yiheng Lin +2
Independent learning (IL), despite being a popular approach in practice to achieve scalability in large-scale multi-agent systems, usually lacks global convergence guarantees. In t…
Risk-Sensitive Online Algorithms
Nicolas Christianson, Bo Sun, Steven Low +1
We study the design of risk-sensitive online algorithms, in which risk measures are used in the competitive analysis of randomized online algorithms. We introduce the CVaR-comp…
Online Policy Optimization in Unknown Nonlinear Systems
Yiheng Lin, James A. Preiss, Fengze Xie +4
We study online policy optimization in nonlinear time-varying dynamical systems where the true dynamical models are unknown to the controller. This problem is challenging because,…
Learning-Augmented Decentralized Online Convex Optimization in Networks
Pengfei Li, Jianyi Yang, Adam Wierman +1
This paper studies decentralized online convex optimization in a networked multi-agent system and proposes a novel algorithm, Learning-Augmented Decentralized Online optimization (…