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Improved Learning Bounds for Branch-and-Cut


Nov 18, 2021
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik


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Learning-to-learn non-convex piecewise-Lipschitz functions


Aug 19, 2021
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar


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Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing


Jun 08, 2021
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar


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Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond


Jun 08, 2021
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, Ellen Vitercik


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Data driven algorithms for limited labeled data learning


Mar 18, 2021
Maria-Florina Balcan, Dravyansh Sharma

* 31 pages, 9 figures 

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Generalization in portfolio-based algorithm selection


Dec 24, 2020
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik

* AAAI 2021 

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Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning


Dec 19, 2020
Kaiwen Wang, Travis Dick, Maria-Florina Balcan

* In AAAI Workshop on Privacy-Preserving Artificial Intelligence, 2020 

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Data-driven Algorithm Design


Nov 14, 2020
Maria-Florina Balcan

* Chapter 29 of the book Beyond the Worst-Case Analysis of Algorithms, edited by Tim Roughgarden and published by Cambridge University Press (2020) 

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On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness


Oct 13, 2020
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

* 31 pages, 9 figures 

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Noise in Classification


Oct 10, 2020
Maria-Florina Balcan, Nika Haghtalab

* Chapter 16 of the book Beyond the Worst-Case Analysis of Algorithms 

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Refined bounds for algorithm configuration: The knife-edge of dual class approximability


Jun 21, 2020
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik


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Geometry-Aware Gradient Algorithms for Neural Architecture Search


Apr 16, 2020
Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar

* 31 pages, 5 figures 

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How much data is sufficient to learn high-performing algorithms?


Sep 09, 2019
Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, Ellen Vitercik


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Online optimization of piecewise Lipschitz functions in changing environments


Jul 22, 2019
Maria-Florina Balcan, Travis Dick, Dravyansh Sharma


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Learning to Link


Jul 16, 2019
Maria-Florina Balcan, Travis Dick, Manuel Lang


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Adaptive Gradient-Based Meta-Learning Methods


Jun 17, 2019
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar


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Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees


May 26, 2019
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik


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Semi-bandit Optimization in the Dispersed Setting


Apr 18, 2019
Maria-Florina Balcan, Travis Dick, Wesley Pegden


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Provable Guarantees for Gradient-Based Meta-Learning


Feb 27, 2019
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar


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


Oct 22, 2018
Maria-Florina Balcan, Travis Dick, Ellen Vitercik


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Testing Matrix Rank, Optimally


Oct 18, 2018
Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang

* 51 pages. To appear in SODA 2019 

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Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems


Oct 16, 2018
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White


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Envy-Free Classification


Sep 23, 2018
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia


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Data-Driven Clustering via Parameterized Lloyd's Families


Sep 19, 2018
Maria-Florina Balcan, Travis Dick, Colin White


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A General Theory of Sample Complexity for Multi-Item Profit Maximization


Aug 08, 2018
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik


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Learning to Branch


May 16, 2018
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik


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Matrix Completion and Related Problems via Strong Duality


Apr 25, 2018
Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang

* 37 pages, 4 figures 

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Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions


Jan 27, 2018
Maria-Florina Balcan, Hongyang Zhang

* Appear in NIPS 2017 

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