22 citations · 91 across the 35 of their papers we have counts for
8 papers · 1 filter
Smoothed Online Optimization for Target Tracking: Robust and Learning-Augmented Algorithms
Ali Zeynali, Mahsa Sahebdel, Qingsong Liu +2
We introduce the Smoothed Online Optimization for Target Tracking (SOOTT) problem, a new framework that integrates three key objectives in online decision-making under uncertainty:…
Towards Environmentally Equitable AI
Mohammad Hajiesmaili, Shaolei Ren, Ramesh K. Sitaraman +1
The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI…
Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz +3
This paper introduces a family of learning-augmented algorithms for online knapsack problems that achieve near Pareto-optimal consistency-robustness trade-offs through a simple com…
Online Algorithms with Uncertainty-Quantified Predictions
Bo Sun, Jerry Huang, Nicolas Christianson +3
The burgeoning field of algorithms with predictions studies the problem of using possibly imperfect machine learning predictions to improve online algorithm performance. While near…
Adversarial Attacks on Online Learning to Rank with Click Feedback
Jinhang Zuo, Zhiyao Zhang, Zhiyong Wang +3
Online learning to rank (OLTR) is a sequential decision-making problem where a learning agent selects an ordered list of items and receives feedback through user clicks. Although p…
Time Fairness in Online Knapsack Problems
Adam Lechowicz, Rik Sengupta, Bo Sun +2
The online knapsack problem is a classic problem in the field of online algorithms. Its canonical version asks how to pack items of different values and weights arriving online int…
Online Search with Predictions: Pareto-optimal Algorithm and its Applications in Energy Markets
Russell Lee, Bo Sun, Mohammad Hajiesmaili +1
This paper develops learning-augmented algorithms for energy trading in volatile electricity markets. The basic problem is to sell (or buy) units of energy for the highest reve…
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
Bo Sun, Russell Lee, Mohammad Hajiesmaili +2
This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions a…