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Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization

8 November 2017

Papers citing "Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization"

21 / 21 papers shown

Title
Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees Ally Yalei Du Eric Huang Dravyansh Sharma 155 1 0 18 Feb 2025
Offline-to-online hyperparameter transfer for stochastic bandits Dravyansh Sharma Arun Sai Suggala OffRL 103 4 0 06 Jan 2025
Learning-Based Heavy Hitters and Flow Frequency Estimation in Streams Rana Shahout Michael Mitzenmacher 70 4 0 24 Jun 2024
Online Algorithms with Uncertainty-Quantified Predictions Bo Sun Jerry Huang Nicolas H. Christianson Mohammad Hajiesmaili Adam Wierman Raouf Boutaba 78 6 0 17 Oct 2023
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs Maria-Florina Balcan Hedyeh Beyhaghi 49 4 0 22 Feb 2023
Triangle and Four Cycle Counting with Predictions in Graph Streams Justin Y. Chen T. Eden Piotr Indyk Honghao Lin Shyam Narayanan R. Rubinfeld Sandeep Silwal Tal Wagner David P. Woodruff Michael Zhang 131 23 0 17 Mar 2022
Learning Predictions for Algorithms with Predictions M. Khodak Maria-Florina Balcan Ameet Talwalkar Sergei Vassilvitskii 87 27 0 18 Feb 2022
Online Learning for Min Sum Set Cover and Pandora's Box Evangelia Gergatsouli Christos Tzamos 51 15 0 10 Feb 2022
Robustification of Online Graph Exploration Methods Franziska Eberle Alexander Lindermayr Nicole Megow L. Nolke Jens Schlöter AAML OOD 64 22 0 10 Dec 2021
Faster Matchings via Learned Duals M. Dinitz Sungjin Im Thomas Lavastida Benjamin Moseley Sergei Vassilvitskii 51 69 0 20 Jul 2021
Data driven semi-supervised learning Maria-Florina Balcan Dravyansh Sharma 68 16 0 18 Mar 2021
Generalization in portfolio-based algorithm selection Maria-Florina Balcan Tuomas Sandholm Ellen Vitercik 73 12 0 24 Dec 2020
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing Thomas Lavastida Benjamin Moseley R. Ravi Chenyang Xu OOD 104 59 0 23 Nov 2020
Data-driven Algorithm Design Maria-Florina Balcan 34 2 0 14 Nov 2020
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC Arun Ganesh Kunal Talwar FedML 84 41 0 27 Oct 2020
Refined bounds for algorithm configuration: The knife-edge of dual class approximability Maria-Florina Balcan Tuomas Sandholm Ellen Vitercik 68 15 0 21 Jun 2020
Smoothed Analysis of Online and Differentially Private Learning Nika Haghtalab Tim Roughgarden Abhishek Shetty 86 51 0 17 Jun 2020
Learning piecewise Lipschitz functions in changing environments Maria-Florina Balcan Travis Dick Dravyansh Sharma 37 2 0 22 Jul 2019
Automatic Discovery of Privacy-Utility Pareto Fronts Brendan Avent Javier I. González Tom Diethe Andrei Paleyes Borja Balle FedML 77 28 0 26 May 2019
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees Maria-Florina Balcan Tuomas Sandholm Ellen Vitercik 63 16 0 26 May 2019
Empirical Bayes Regret Minimization Chih-Wei Hsu Branislav Kveton Ofer Meshi Martin Mladenov Csaba Szepesvári 77 13 0 04 Apr 2019