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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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Finding and Fixing Spurious Patterns with Explanations


Jun 03, 2021
Gregory Plumb, Marco Tulio Ribeiro, Ameet Talwalkar


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Sanity Simulations for Saliency Methods


May 13, 2021
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar


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Rethinking Neural Operations for Diverse Tasks


Mar 29, 2021
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar


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Towards Connecting Use Cases and Methods in Interpretable Machine Learning


Mar 10, 2021
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar


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Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability


Feb 26, 2021
Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar

* To appear in ICLR 2021. 72 pages, 107 figures 

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On Data Efficiency of Meta-learning


Jan 30, 2021
Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar

* Preliminary version. An updated version is to appear in AISTATS 2021 

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A Learning Theoretic Perspective on Local Explainability


Nov 02, 2020
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar


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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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Model-Agnostic Characterization of Fairness Trade-offs


Apr 08, 2020
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar


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Explaining Groups of Points in Low-Dimensional Representations


Mar 18, 2020
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar


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FedDANE: A Federated Newton-Type Method


Jan 07, 2020
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

* Asilomar Conference on Signals, Systems, and Computers 2019 

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Differentially Private Meta-Learning


Sep 12, 2019
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar


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Federated Learning: Challenges, Methods, and Future Directions


Aug 21, 2019
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith


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Learning Fair Representations for Kernel Models


Jun 27, 2019
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar


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Adaptive Gradient-Based Meta-Learning Methods


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


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Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)


May 31, 2019
Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

* presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA. arXiv admin note: substantial text overlap with arXiv:1902.06787 

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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning


Mar 12, 2019
Liam Li, Evan Sparks, Kevin Jamieson, Ameet Talwalkar


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One-Shot Federated Learning


Mar 05, 2019
Neel Guha, Ameet Talwalkar, Virginia Smith

* 5 pages, 3 figures, 1 table. 2nd Workshop on Machine Learning on the Phone and other Consumer Devices, NeurIPs 2018 

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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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Random Search and Reproducibility for Neural Architecture Search


Feb 20, 2019
Liam Li, Ameet Talwalkar


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Regularizing Black-box Models for Improved Interpretability


Feb 18, 2019
Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar


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LEAF: A Benchmark for Federated Settings


Jan 09, 2019
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar


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Expanding the Reach of Federated Learning by Reducing Client Resource Requirements


Jan 08, 2019
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, Ameet Talwalkar


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On the Convergence of Federated Optimization in Heterogeneous Networks


Dec 14, 2018
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith

* Preprint. Work in Progress 

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