Chasing Convex Bodies and Functions with Black-Box Advice. - Nicolas Christianson; Tinashe Handina; Adam Wierman (COLT, 2022)

Online Optimization with Untrusted Predictions. - Daan Rutten; Nicolas Christianson; Debankur Mukherjee; Adam Wierman (CoRR, 2022)

Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems. - Nicolas Christianson; Junxuan Shen; Adam Wierman (AISTATS, 2023)

Pricing Uncertainty in Stochastic Multi-Stage Electricity Markets. - Lucien Werner; Nicolas Christianson; Alessandro Zocca; Adam Wierman; Steven H. Low (CDC, 2023)

SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems. - Christopher Yeh; Victor Li; Rajeev Datta; Julio Arroyo; Nicolas Christianson; Chi Zhang; Yize Chen; Mohammad Mehdi Hosseini; Azarang Golmohammadi; Yuanyuan Shi; Yisong Yue; Adam Wierman (NeurIPS, 2023)

Online Algorithms with Uncertainty-Quantified Predictions. - Bo Sun 004; Jerry Huang; Nicolas Christianson; Mohammad Hajiesmaili; Adam Wierman (CoRR, 2023)

Smoothed Online Optimization with Unreliable Predictions. - Daan Rutten; Nicolas Christianson; Debankur Mukherjee; Adam Wierman (Proc. ACM Meas. Anal. Comput. Syst., 2023)

The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting. - Adam Lechowicz; Nicolas Christianson; Jinhang Zuo; Noman Bashir; Mohammad Hajiesmaili; Adam Wierman; Prashant J. Shenoy (Proc. ACM Meas. Anal. Comput. Syst., 2023)

Risk-Sensitive Online Algorithms (Extended Abstract). - Nicolas Christianson; Bo Sun 004; Steven H. Low; Adam Wierman (COLT, 2024)

Online Algorithms with Uncertainty-Quantified Predictions. - Bo Sun 004; Jerry Huang; Nicolas Christianson; Mohammad Hajiesmaili; Adam Wierman; Raouf Boutaba (ICML, 2024)

Chasing Convex Functions with Long-term Constraints. - Adam Lechowicz; Nicolas Christianson; Bo Sun 004; Noman Bashir; Mohammad Hajiesmaili; Adam Wierman; Prashant J. Shenoy (ICML, 2024)

SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming. - Tianyu Chen; Yiheng Lin 001; Nicolas Christianson; Zahaib Akhtar; Sharath Dharmaji; Mohammad Hajiesmaili; Adam Wierman; Ramesh K. Sitaraman (SIGCOMM, 2024)

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms. - Adam Lechowicz; Nicolas Christianson; Bo Sun 004; Noman Bashir; Mohammad Hajiesmaili; Adam Wierman; Prashant J. Shenoy (SIGMETRICS/Performance (Abstracts), 2024)

CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management. - Adam Lechowicz; Nicolas Christianson; Bo Sun 004; Noman Bashir; Mohammad Hajiesmaili; Adam Wierman; Prashant J. Shenoy (CoRR, 2024)

End-to-End Conformal Calibration for Optimization Under Uncertainty. - Christopher Yeh; Nicolas Christianson; Alan Wu; Adam Wierman; Yisong Yue (CoRR, 2024)

Risk-Sensitive Online Algorithms. - Nicolas Christianson; Bo Sun 004; Steven H. Low; Adam Wierman (SIGMETRICS Perform. Evaluation Rev., 2024)

Learning for Sustainable Online Scheduling with Competitive Fairness Guarantees. - Pengfei Li 008; Nicolas Christianson; Jianyi Yang 001; Adam Wierman; Shaolei Ren (e-Energy, 2025)

Fast and Reliable N - k Contingency Screening with Input-Convex Neural Networks. - Nicolas Christianson; Wenqi Cui; Steven H. Low; Weiwei Yang; Baosen Zhang (L4DC, 2025)

Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints. - Adam Lechowicz; Nicolas Christianson; Bo Sun 004; Noman Bashir; Mohammad Hajiesmaili; Adam Wierman; Prashant J. Shenoy (Proc. ACM Meas. Anal. Comput. Syst., 2025)