Poster Session and Break
Poster Authors
Towards a Learning-Only Approach for Non-Convex Sum-Rate Maximization
Qingyu Song (The Chinese University of Hong Kong), Guochen Liu (Huawei Technologies Co., Ltd.), Hong Xu (The Chinese University of Hong Kong)
Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location
Priyank Agrawal (Columbia University), Eric Balkanski (Columbia University), Vasilis Gkatzelis (Drexel University), Tingting Ou (Columbia University), Xizhi Tan (Drexel University)
Robust Load Balancing with Machine Learned Advice
Binghui Peng (Columbia University), Sara Ahmadian (Google Research), Hossein Esfandiari (Google), Vahab Mirrokni (Google Research)
Online List Update with Predictions
Arghya Bhattacharya (Stony Brook University), Shahin Kamali (York University), Helen Xu (Lawrence Berkeley National Laboratory)
Learning-augmented Control via Online Adaptive Policy Selection: No Regret via Contractive Perturbations
Yiheng Lin (Caltech), James Preiss (Caltech), Emile Anand (Caltech), Yingying Li (Caltech), Yisong Yue (Caltech), Adam Wierman (Caltech)
Online Algorithms with Costly Predictions
Marina Drygala (EPFL), Sai Ganesh Nagarajan (EPFL), Ola Svensson (EPFL)
On Designing Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids
Ali Menati (Texas A&M University), Sid Chi-Kin Chau (Australian National University), Minghua Chen (City University of Hong Kong),
Fair Online Knapsack with Value Density Predictions
Adam Lechowicz (University of Massachusetts Amherst), Rik Sengupta (University of Massachusetts Amherst), Bo Sun (The Chinese University of Hong Kong), Shahin Kamali (York University), Mohammad Hajiesmaili (University of Massachusetts Amherst)
Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach
Qiulin Lin (City University of Hong Kong), Yanfang Mo (City University of Hong Kong), Junyan Su (City University of Hong Kong), Minghua Chen (City University of Hong Kong)
Online Algorithms with Better-Than-Random Predictions
Cooper Sigrist (University of Massachusetts Amherst), Shahin Kamali (York University), Bo Sun (The Chinese University of Hong Kong), Mohammad Hajiesmaili (University of Massachusetts Amherst)
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems
Nicolas Christianson (California Institute of Technology), Junxuan Shen (California Institute of Technology), Adam Wierman (California Institute of Technology)
Learning-Augmented Decentralized Online Convex Optimization in Networks
Pengfei Li (UC Riverside), Jianyi Yang (University of California, Riverside), Adam Wierman (Caltech), Shaolei Ren (UC Riverside)
Applied Learning-Augmented Algorithms with Heterogeneous Predictors
Jessica Maghakian (Stony Brook University), Russell Lee (University of Massachusetts Amherst), Mohammad Hajiesmaili (University of Massachusetts Amherst), Jian Li (SUNY-Binghamton University), Ramesh Sitaraman (UMass Amherst & Akamai Tech), Zhenhua Liu (Stony Brook University)
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Pengfei Li (UC Riverside), Jianyi Yang (UC Riverside), Shaolei Ren (UC Riverside)
Partitioned Learned Count-Min Sketch
Thuy Trang Nguyen (University of Massachusetts Amherst), Cameron Musco (University of Massachusetts Amherst)
Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints
Tianyu Zhao (City University of Hong Kong), Xiang Pan (The Chinese University of Hong Kong), Minghua Chen (City University of Hong Kong), Steven Low (Caltech)
Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set
Enming Liang (City University of Hong Kong), Minghua Chen (City University of Hong Kong), Steven Low (Caltech)