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Provably tuning the ElasticNet across instances


Jul 20, 2022
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar


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Evaluating Systemic Error Detection Methods using Synthetic Images


Jul 08, 2022
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Tulio Ribeiro, Ameet Talwalkar


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On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods


Jun 30, 2022
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani


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Use-Case-Grounded Simulations for Explanation Evaluation


Jun 05, 2022
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar


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AANG: Automating Auxiliary Learning


May 27, 2022
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar

* 18 pages, 9 tables and 4 figures 

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Perspectives on Incorporating Expert Feedback into Model Updates


May 13, 2022
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar


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Efficient Architecture Search for Diverse Tasks


Apr 15, 2022
Junhong Shen, Mikhail Khodak, Ameet Talwalkar

* Code available at https://github.com/sjunhongshen/DASH 

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Learning Predictions for Algorithms with Predictions


Feb 18, 2022
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii


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Bayesian Persuasion for Algorithmic Recourse


Dec 12, 2021
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu


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