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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Model Agnostic Supervised Local Explanations

Oct 27, 2018
Gregory Plumb, Denali Molitor, Ameet Talwalkar


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Massively Parallel Hyperparameter Tuning

Oct 17, 2018
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

* Corrected typo in Algorithm 1 

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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Jun 18, 2018
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

* Journal of Machine Learning Research 18 (2018) 1-52 
* Changes: - Updated to JMLR version 

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Federated Multi-Task Learning

Feb 27, 2018
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar


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Parle: parallelizing stochastic gradient descent

Sep 10, 2017
Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar, Adam Oberman


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MLlib: Machine Learning in Apache Spark

May 26, 2015
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar


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TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries

Mar 08, 2015
Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska


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Non-stochastic Best Arm Identification and Hyperparameter Optimization

Feb 27, 2015
Kevin Jamieson, Ameet Talwalkar


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