Ameet Talwalkar

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 115

Top Venues

Publications

Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
Revisiting Cascaded Ensembles for Efficient Inference. CoRR (2024)
Junhong Shen, Tanya Marwah, Ameet Talwalkar
UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation. CoRR (2024)
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. AAAI (2024)
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
Multitask Learning Can Improve Worst-Group Outcomes. Trans. Mach. Learn. Res. 2024 (2024)
Lucio M. Dery, Steven Kolawole, Jean-Francois Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. CoRR (2024)
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. ICLR (2024)
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR (2024)
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky, Hannah Rose Kirk, Malak Sadek, Holli Sargeant, Ameet Talwalkar, Adrian Weller, Umang Bhatt
Modulating Language Model Experiences through Frictions. CoRR (2024)
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
Perspectives on incorporating expert feedback into model updates. Patterns 4 (7) (2023)
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
Multitask Learning Can Improve Worst-Group Outcomes. CoRR (2023)
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. CoRR (2023)
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. EAAMO (2023)
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. CoRR (2023)
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
AANG : Automating Auxiliary Learning. ICLR (2023)
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine. ICML (2023)
Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig
Do LLMs exhibit human-like response biases? A case study in survey design. CoRR (2023)
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Ameet Talwalkar
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models. Trans. Mach. Learn. Res. 2023 (2023)
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. CoRR (2023)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. MLSys (2023)
Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb, Ameet Talwalkar
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms. CoRR (2023)
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
Assisting Human Decisions in Document Matching. Trans. Mach. Learn. Res. 2023 (2023)
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. CHI (2023)
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine. CoRR (2023)
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
Assisting Human Decisions in Document Matching. CoRR (2023)
Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
Learning Personalized Decision Support Policies. CoRR (2023)
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. CoRR (2022)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. CoRR (2022)
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
Perspectives on Incorporating Expert Feedback into Model Updates. CoRR (2022)
Maria-Florina F. Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar
Provably tuning the ElasticNet across instances. NeurIPS (2022)
Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. NeurIPS (2022)
Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White
AutoML for Climate Change: A Call to Action. CoRR (2022)
Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar
Finding and Fixing Spurious Patterns with Explanations. Trans. Mach. Learn. Res. 2022 (2022)
Elias Jääsaari, Michelle Ma, Ameet Talwalkar, Tianqi Chen
SONAR: Joint Architecture and System Optimization Search. CoRR (2022)
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR (2022)
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks. NeurIPS (2022)
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Túlio Ribeiro, Ameet Talwalkar
Evaluating Systemic Error Detection Methods using Synthetic Images. CoRR (2022)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Interpretable machine learning: moving from mythos to diagnostics. Commun. ACM 65 (8) (2022)
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
AANG: Automating Auxiliary Learning. CoRR (2022)
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks. CoRR (2022)
Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. ICLR (2022)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. CoRR (2022)
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Sanity Simulations for Saliency Methods. ICML (2022)
Misha Khodak, Maria-Florina F. Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Learning Predictions for Algorithms with Predictions. NeurIPS (2022)
Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC (2022)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Provably tuning the ElasticNet across instances. CoRR (2022)
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
Bayesian Persuasion for Algorithmic Recourse. NeurIPS (2022)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS (2022)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. CoRR (2021)
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR (2021)
Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. CoRR (2021)
Maruan Al-Shedivat, Liam Li, Eric Po Xing, Ameet Talwalkar
On Data Efficiency of Meta-learning. CoRR (2021)
Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan R. Sparks
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) (2021)
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
A Learning Theoretic Perspective on Local Explainability. ICLR (2021)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Interpretable Machine Learning: Moving from mythos to diagnostics. ACM Queue 19 (6) (2021)
Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar
Finding and Fixing Spurious Patterns with Explanations. CoRR (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS (2021)
Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar
Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR (2021)
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
Rethinking Neural Operations for Diverse Tasks. CoRR (2021)
Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. ICLR (2021)
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
Rethinking Neural Operations for Diverse Tasks. NeurIPS (2021)
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Sanity Simulations for Saliency Methods. CoRR (2021)
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR (2021)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Towards Connecting Use Cases and Methods in Interpretable Machine Learning. CoRR (2021)
Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar
On Data Efficiency of Meta-learning. AISTATS (2021)
Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. CoRR (2021)
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
Bayesian Persuasion for Algorithmic Recourse. CoRR (2021)
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar
Model-Agnostic Characterization of Fairness Trade-offs. CoRR (2020)
Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar
Regularizing Black-box Models for Improved Interpretability. NeurIPS (2020)
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar
FACT: A Diagnostic for Group Fairness Trade-offs. ICML (2020)
Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR (2020)
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
Explaining Groups of Points in Low-Dimensional Representations. CoRR (2020)
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
Differentially Private Meta-Learning. ICLR (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. CoRR (2020)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37 (3) (2020)
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
A Learning Theoretic Perspective on Local Explainability. CoRR (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Federated Optimization in Heterogeneous Networks. MLSys (2020)
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar
Learning Fair Representations for Kernel Models. AISTATS (2020)
Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
A System for Massively Parallel Hyperparameter Tuning. MLSys (2020)
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
Explaining Groups of Points in Low-Dimensional Representations. ICML (2020)
Neel Guha, Ameet Talwalkar, Virginia Smith
One-Shot Federated Learning. CoRR (2019)
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. ICML (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Adaptive Gradient-Based Meta-Learning Methods. CoRR (2019)
Liam Li, Evan R. Sparks, Kevin G. Jamieson, Ameet Talwalkar
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. CoRR (2019)
Liam Li, Ameet Talwalkar
Random Search and Reproducibility for Neural Architecture Search. CoRR (2019)
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Provable Guarantees for Gradient-Based Meta-Learning. CoRR (2019)
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar
Learning Fair Representations for Kernel Models. CoRR (2019)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. ACSSC (2019)
Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version). CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. CoRR (2019)
Liam Li, Ameet Talwalkar
Random Search and Reproducibility for Neural Architecture Search. UAI (2019)
Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar
Regularizing Black-box Models for Improved Interpretability. CoRR (2019)
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
Differentially Private Meta-Learning. CoRR (2019)
Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
Massively Parallel Hyperparameter Tuning. CoRR (2018)
Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. CoRR (2018)
Gregory Plumb, Denali Molitor, Ameet Talwalkar
Supervised Local Modeling for Interpretability. CoRR (2018)
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
LEAF: A Benchmark for Federated Settings. CoRR (2018)
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ameet Talwalkar