Ameet Talwalkar

187 papers A* 39A 11B 1Misc 1Journal 115Unranked 17
YearRankTypeTitle / Venue / Authors
2026 J jnl
Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants.
CoRR
Valerie Chen, Jasmyn He, Behnjamin Williams, Jason Valentino, Ameet Talwalkar
2026 A* conf
Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows.
CHI
Valerie Chen, Ameet Talwalkar, Robert Brennan, Graham Neubig
2026 J jnl
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation.
Trans. Mach. Learn. Res.
Shanda Li, Tanya Marwah, Junhong Shen, Weiwei Sun, Andrej Risteski, Yiming Yang, Ameet Talwalkar
2026 J jnl
Comparing Developer and LLM Biases in Code Evaluation.
CoRR
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donahue, Ameet Talwalkar, Wayne Chi, Valerie Chen
2026 J jnl
GameDevBench: Evaluating Agentic Capabilities Through Game Development.
CoRR
Wayne Chi, Yixiong Fang, Arnav Yayavaram, Siddharth Yayavaram, Seth Karten, Qiuhong Anna Wei, Runkun Chen, Alexander Wang, Valerie Chen, Ameet Talwalkar, Chris Donahue
2026 J jnl
Learn Hard Problems During RL with Reference Guided Fine-tuning.
CoRR
Yangzhen Wu, Shanda Li, Zixin Wen, Xin Zhou, Ameet Talwalkar, Yiming Yang, Wenhao Huang, Tianle Cai
2025 J jnl
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial Optimization.
CoRR
Shengyu Feng, Weiwei Sun, Shanda Li, Ameet Talwalkar, Yiming Yang
2025 J jnl
Agreement-Based Cascading for Efficient Inference.
Trans. Mach. Learn. Res.
Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
2025 J jnl
Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows.
CoRR
Valerie Chen, Ameet Talwalkar, Robert Brennan, Graham Neubig
2025 J jnl
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation.
CoRR
Shanda Li, Tanya Marwah, Junhong Shen, Weiwei Sun, Andrej Risteski, Yiming Yang, Ameet Talwalkar
2025 — conf
CodingGenie: A Proactive LLM-Powered Programming Assistant.
SIGSOFT FSE Companion
Sebastian Zhao, Alan Zhu, Hussein Mozannar, David A. Sontag, Ameet Talwalkar, Valerie Chen
2025 J jnl
CodingGenie: A Proactive LLM-Powered Programming Assistant.
CoRR
Sebastian Zhao, Alan Zhu, Hussein Mozannar, David A. Sontag, Ameet Talwalkar, Valerie Chen
2025 J jnl
Completion ≠ Collaboration: Scaling Collaborative Effort with Agents.
CoRR
Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J. Shen, Ameet Talwalkar, Tongshuang Wu, David A. Sontag
2025 A* conf
Copilot Arena: A Platform for Code LLM Evaluation in the Wild.
ICML
Wayne Chi, Valerie Chen, Anastasios Nikolas Angelopoulos, Wei-Lin Chiang, Aditya Mittal, Naman Jain, Tianjun Zhang, Ion Stoica, Chris Donahue, Ameet Talwalkar
2025 J jnl
Copilot Arena: A Platform for Code LLM Evaluation in the Wild.
CoRR
Wayne Chi, Valerie Chen, Anastasios Nikolas Angelopoulos, Wei-Lin Chiang, Aditya Mittal, Naman Jain, Tianjun Zhang, Ion Stoica, Chris Donahue, Ameet Talwalkar
2025 J jnl
EDIT-Bench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits.
CoRR
Wayne Chi, Valerie Chen, Ryan Shar, Aditya Mittal, Jenny Liang, Wei-Lin Chiang, Anastasios Nikolas Angelopoulos, Ion Stoica, Graham Neubig, Ameet Talwalkar, Chris Donahue
2025 J jnl
How can we assess human-agent interactions? Case studies in software agent design.
CoRR
Valerie Chen, Rohit Malhotra, Xingyao Wang, Juan Michelini, Xuhui Zhou, Aditya Bharat Soni, Hoang H. Tran, Calvin Smith, Ameet Talwalkar, Graham Neubig
2025 J jnl
L2G: Repurposing Language Models for Genomics Tasks.
Trans. Mach. Learn. Res.
Wenduo Cheng, Junhong Shen, Mikhail Khodak, Jian Ma, Ameet Talwalkar
2025 A* conf
Learning Personalized Decision Support Policies.
AAAI
Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
2025 A* conf
Need Help? Designing Proactive AI Assistants for Programming.
CHI
Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar
2025 J jnl
Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers.
CoRR
Naman Choudhary, Vedant Singh, Ameet Talwalkar, Nicholas Matthew Boffi, Mikhail Khodak, Tanya Marwah
2025 J jnl
Sample Complexity and Representation Ability of Test-time Scaling Paradigms.
CoRR
Baihe Huang, Shanda Li, Tianhao Wu, Yiming Yang, Ameet Talwalkar, Kannan Ramchandran, Michael I. Jordan, Jiantao Jiao
2025 A* conf
Specialized Foundation Models Struggle to Beat Supervised Baselines.
ICLR
Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak
2025 J jnl
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers.
Trans. Mach. Learn. Res.
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
2025 J jnl
Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction.
CoRR
Junhong Shen, Hao Bai, Lunjun Zhang, Yifei Zhou, Amrith Setlur, Shengbang Tong, Diego Caples, Nan Jiang, Tong Zhang, Ameet Talwalkar, Aviral Kumar
2025 J jnl
This Time is Different: An Observability Perspective on Time Series Foundation Models.
CoRR
Ben Cohen, Emaad Khwaja, Youssef Doubli, Salahidine Lemaachi, Chris Lettieri, Charles Masson, Hugo Miccinilli, Elise Ramé, Qiqi Ren, Afshin Rostamizadeh, Jean Ogier du Terrail, Anna-Monica Toon, Kan Wang, Stephan Xie, Zongzhe Xu, Viktoriya Zhukova, David Asker, Ameet Talwalkar, Othmane Abou-Amal
2025 J jnl
Towards Community-Driven Agents for Machine Learning Engineering.
CoRR
Sijie Li, Weiwei Sun, Shanda Li, Ameet Talwalkar, Yiming Yang
2025 A* conf
Understanding Optimization in Deep Learning with Central Flows.
ICLR
Jeremy Cohen, Alex Damian, Ameet Talwalkar, J. Zico Kolter, Jason D. Lee
2025 — conf
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback.
ACL (Findings)
Jane Pan, Ryan Shar, Jacob Pfau, Ameet Talwalkar, He He, Valerie Chen
2025 J jnl
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback.
CoRR
Jane Pan, Ryan Shar, Jacob Pfau, Ameet Talwalkar, He He, Valerie Chen
2025 J jnl
Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption.
CoRR
Rebecca Yu, Valerie Chen, Ameet Talwalkar, Hoda Heidari
2024 J jnl
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes.
CoRR
Lucio M. Dery, Steven Kolawole, Jean-François Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar
2024 A* conf
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances.
ICLR
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
2024 J jnl
Modulating Language Model Experiences through Frictions.
CoRR
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky, Hannah Rose Kirk, Malak Sadek, Holli Sargeant, Ameet Talwalkar, Adrian Weller, Umang Bhatt
2024 J jnl
Multitask Learning Can Improve Worst-Group Outcomes.
Trans. Mach. Learn. Res.
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2024 J jnl
Need Help? Designing Proactive AI Assistants for Programming.
CoRR
Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar
2024 A* conf
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods.
AAAI
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
2024 J jnl
Revisiting Cascaded Ensembles for Efficient Inference.
CoRR
Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith
2024 J jnl
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data.
CoRR
Junhong Shen, Atishay Jain, Zedian Xiao, Ishan Amlekar, Mouad Hadji, Aaron Podolny, Ameet Talwalkar
2024 J jnl
Specialized Foundation Models Struggle to Beat Supervised Baselines.
CoRR
Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak
2024 J jnl
The Impact of Element Ordering on LM Agent Performance.
CoRR
Wayne Chi, Ameet Talwalkar, Chris Donahue
2024 J jnl
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers.
CoRR
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
2024 J jnl
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation.
Trans. Mach. Learn. Res.
Junhong Shen, Tanya Marwah, Ameet Talwalkar
2024 J jnl
UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation.
CoRR
Junhong Shen, Tanya Marwah, Ameet Talwalkar
2024 J jnl
Understanding Optimization in Deep Learning with Central Flows.
CoRR
Jeremy Cohen, Alex Damian, Ameet Talwalkar, Zico Kolter, Jason D. Lee
2023 A* conf
AANG : Automating Auxiliary Learning.
ICLR
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
2023 J jnl
Assisting Human Decisions in Document Matching.
CoRR
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
2023 J jnl
Assisting Human Decisions in Document Matching.
Trans. Mach. Learn. Res.
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
2023 A* conf
Cross-Modal Fine-Tuning: Align then Refine.
ICML
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
2023 J jnl
Cross-Modal Fine-Tuning: Align then Refine.
CoRR
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
2023 J jnl
Do LLMs exhibit human-like response biases? A case study in survey design.
CoRR
Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig
2023 — conf
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines.
EAAMO
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
2023 J jnl
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines.
CoRR
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
2023 J jnl
Learning Personalized Decision Support Policies.
CoRR
Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
2023 J jnl
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances.
CoRR
Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar
2023 J jnl
Multitask Learning Can Improve Worst-Group Outcomes.
CoRR
Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig
2023 — conf
On Noisy Evaluation in Federated Hyperparameter Tuning.
MLSys
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
2023 J jnl
Perspectives on incorporating expert feedback into model updates.
Patterns
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
2023 J jnl
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models.
Trans. Mach. Learn. Res.
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Ameet Talwalkar
2023 J jnl
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms.
CoRR
Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb, Ameet Talwalkar
2023 A* conf
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning.
CHI
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer
2023 J jnl
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning.
CoRR
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer
2022 J jnl
AANG: Automating Auxiliary Learning.
CoRR
Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
2022 J jnl
AutoML for Climate Change: A Call to Action.
CoRR
Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White
2022 A* conf
Bayesian Persuasion for Algorithmic Recourse.
NeurIPS
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
2022 A* conf
Efficient Architecture Search for Diverse Tasks.
NeurIPS
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
2022 J jnl
Efficient Architecture Search for Diverse Tasks.
CoRR
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
2022 J jnl
Evaluating Systemic Error Detection Methods using Synthetic Images.
CoRR
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Túlio Ribeiro, Ameet Talwalkar
2022 J jnl
Finding and Fixing Spurious Patterns with Explanations.
Trans. Mach. Learn. Res.
Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar
2022 J jnl
Interpretable machine learning: moving from mythos to diagnostics.
Commun. ACM
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
2022 A* conf
Learning Predictions for Algorithms with Predictions.
NeurIPS
Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
2022 J jnl
Learning Predictions for Algorithms with Predictions.
CoRR
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
2022 — conf
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks.
CIC
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 B. Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
2022 A* conf
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks.
NeurIPS
Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar
2022 J jnl
On Noisy Evaluation in Federated Hyperparameter Tuning.
CoRR
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith
2022 J jnl
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods.
CoRR
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
2022 J jnl
Perspectives on Incorporating Expert Feedback into Model Updates.
CoRR
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
2022 A* conf
Provably tuning the ElasticNet across instances.
NeurIPS
Maria-Florina Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar
2022 J jnl
Provably tuning the ElasticNet across instances.
CoRR
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
2022 J jnl
SONAR: Joint Architecture and System Optimization Search.
CoRR
Elias Jääsaari, Michelle Ma, Ameet Talwalkar, Tianqi Chen
2022 A* conf
Sanity Simulations for Saliency Methods.
ICML
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
2022 A* conf
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative.
ICLR
Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig
2022 A* conf
Use-Case-Grounded Simulations for Explanation Evaluation.
NeurIPS
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
2022 J jnl
Use-Case-Grounded Simulations for Explanation Evaluation.
CoRR
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
2021 J jnl
A Field Guide to Federated Optimization.
CoRR
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 Horváth, 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
2021 A* conf
A Learning Theoretic Perspective on Local Explainability.
ICLR
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
2021 — conf
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.
NeurIPS (Competition and Demos)
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 Sanjay Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks
2021 J jnl
Bayesian Persuasion for Algorithmic Recourse.
CoRR
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
2021 A* conf
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing.
NeurIPS
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021 J jnl
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing.
CoRR
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar
2021 J jnl
Finding and Fixing Spurious Patterns with Explanations.
CoRR
Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar
2021 A* conf
Geometry-Aware Gradient Algorithms for Neural Architecture Search.
ICLR
Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar
2021 A* conf
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.
ICLR
Jeremy Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar
2021 J jnl
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.
CoRR
Jeremy Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar
2021 J jnl
Interpretable Machine Learning: Moving from mythos to diagnostics.
ACM Queue
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
2021 A* conf
Learning-to-learn non-convex piecewise-Lipschitz functions.
NeurIPS
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
2021 J jnl
Learning-to-learn non-convex piecewise-Lipschitz functions.
CoRR
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
2021 J jnl
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search.
CoRR
Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar
2021 A conf
On Data Efficiency of Meta-learning.
AISTATS
Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar
2021 J jnl
On Data Efficiency of Meta-learning.
CoRR
Maruan Al-Shedivat, Liam Li, Eric Poe Xing, Ameet Talwalkar
2021 A* conf
Rethinking Neural Operations for Diverse Tasks.
NeurIPS
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
2021 J jnl
Rethinking Neural Operations for Diverse Tasks.
CoRR
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar
2021 J jnl
Sanity Simulations for Saliency Methods.
CoRR
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
2021 J jnl
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative.
CoRR
Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig
2021 J jnl
Towards Connecting Use Cases and Methods in Interpretable Machine Learning.
CoRR
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
2020 J jnl
A Learning Theoretic Perspective on Local Explainability.
CoRR
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar
2020 — conf
A System for Massively Parallel Hyperparameter Tuning.
MLSys
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
2020 A* conf
Differentially Private Meta-Learning.
ICLR
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
2020 A* conf
Explaining Groups of Points in Low-Dimensional Representations.
ICML
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
2020 J jnl
Explaining Groups of Points in Low-Dimensional Representations.
CoRR
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
2020 A* conf
FACT: A Diagnostic for Group Fairness Trade-offs.
ICML
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar
2020 J jnl
FedDANE: A Federated Newton-Type Method.
CoRR
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020 J jnl
Federated Learning: Challenges, Methods, and Future Directions.
IEEE Signal Process. Mag.
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2020 — conf
Federated Optimization in Heterogeneous Networks.
MLSys
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2020 J jnl
Geometry-Aware Gradient Algorithms for Neural Architecture Search.
CoRR
Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
2020 A conf
Learning Fair Representations for Kernel Models.
AISTATS
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar
2020 J jnl
Model-Agnostic Characterization of Fairness Trade-offs.
CoRR
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar
2020 A* conf
Regularizing Black-box Models for Improved Interpretability.
NeurIPS
Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar
2019 A* conf
Adaptive Gradient-Based Meta-Learning Methods.
NeurIPS
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
2019 J jnl
Adaptive Gradient-Based Meta-Learning Methods.
CoRR
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
2019 J jnl
Differentially Private Meta-Learning.
CoRR
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar
2019 J jnl
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.
CoRR
Liam Li, Evan Randall Sparks, Kevin Jamieson, Ameet Talwalkar
2019 Misc conf
FedDANE: A Federated Newton-Type Method.
ACSSC
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
2019 J jnl
Federated Learning: Challenges, Methods, and Future Directions.
CoRR
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
2019 J jnl
Learning Fair Representations for Kernel Models.
CoRR
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar
2019 J jnl
One-Shot Federated Learning.
CoRR
Neel Guha, Ameet Talwalkar, Virginia Smith
2019 — ed.
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, Stanford, CA, USA, March 31 - April 2, 2019
SysML
Ameet Talwalkar, Virginia Smith, Matei Zaharia
2019 A* conf
Provable Guarantees for Gradient-Based Meta-Learning.
ICML
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar
2019 J jnl
Provable Guarantees for Gradient-Based Meta-Learning.
CoRR
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
2019 A conf
Random Search and Reproducibility for Neural Architecture Search.
UAI
Liam Li, Ameet Talwalkar
2019 J jnl
Random Search and Reproducibility for Neural Architecture Search.
CoRR
Liam Li, Ameet Talwalkar
2019 J jnl
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version).
CoRR
Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar
2019 J jnl
Regularizing Black-box Models for Improved Interpretability.
CoRR
Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar
2019 J jnl
SysML: The New Frontier of Machine Learning Systems.
CoRR
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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 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 Randall 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
2018 J jnl
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.
CoRR
Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar
2018 J jnl
LEAF: A Benchmark for Federated Settings.
CoRR
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
2018 J jnl
Massively Parallel Hyperparameter Tuning.
CoRR
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
2018 A* conf
Model Agnostic Supervised Local Explanations.
NeurIPS
Gregory Plumb, Denali Molitor, Ameet Talwalkar
2018 J jnl
On the Convergence of Federated Optimization in Heterogeneous Networks.
CoRR
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
2018 J jnl
Supervised Local Modeling for Interpretability.
CoRR
Gregory Plumb, Denali Molitor, Ameet Talwalkar
2017 A conf
Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems.
CIKM
Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar
2017 — conf
Federated Multi-Task Learning.
NIPS
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar
2017 J jnl
Federated Multi-Task Learning.
CoRR
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar
2017 J jnl
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
J. Mach. Learn. Res.
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
2017 — conf
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization.
ICLR (Poster)
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
2017 — conf
Paleo: A Performance Model for Deep Neural Networks.
ICLR (Poster)
Hang Qi, Evan Randall Sparks, Ameet Talwalkar
2017 J jnl
Parle: parallelizing stochastic gradient descent.
CoRR
Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar
2017 — conf
Variable Importance Using Decision Trees.
NIPS
S. Jalil Kazemitabar, Arash A. Amini, Adam E. Bloniarz, Ameet Talwalkar
2016 J jnl
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits.
CoRR
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
2016 J jnl
MLlib: Machine Learning in Apache Spark.
J. Mach. Learn. Res.
Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
2016 A conf
Non-stochastic Best Arm Identification and Hyperparameter Optimization.
AISTATS
Kevin Jamieson, Ameet Talwalkar
2016 A conf
Supervised Neighborhoods for Distributed Nonparametric Regression.
AISTATS
Adam E. Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu
2016 — conf
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale.
NIPS
Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet Talwalkar
2015 — conf
Automating model search for large scale machine learning.
SoCC
Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2015 J jnl
Distributed matrix completion and robust factorization.
J. Mach. Learn. Res.
Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2015 J jnl
MLlib: Machine Learning in Apache Spark.
CoRR
Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
2015 J jnl
Non-stochastic Best Arm Identification and Hyperparameter Optimization.
CoRR
Kevin Jamieson, Ameet Talwalkar
2015 J jnl
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.
CoRR
Evan Randall Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2014 B conf
Changepoint Analysis for Efficient Variant Calling.
RECOMB
Adam E. Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song
2014 J jnl
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network.
ACM Trans. Intell. Syst. Technol.
Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song
2014 — conf
Knowing when you're wrong: building fast and reliable approximate query processing systems.
SIGMOD Conference
Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica
2014 J jnl
Matrix Coherence and the Nystrom Method.
CoRR
Ameet Talwalkar, Afshin Rostamizadeh
2014 J jnl
SMaSH: a benchmarking toolkit for human genome variant calling.
Bioinform.
Ameet Talwalkar, Jesse Liptrap, Julie Newcomb, Christopher Hartl, Jonathan Terhorst, Kristal Curtis, Ma'ayan Bresler, Yun S. Song, Michael I. Jordan, David A. Patterson
2013 A* conf
A general bootstrap performance diagnostic.
KDD
Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan
2013 A* conf
Distributed Low-Rank Subspace Segmentation.
ICCV
Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan
2013 J jnl
Divide-and-Conquer Subspace Segmentation
CoRR
Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan
2013 J jnl
Large-scale SVD and manifold learning.
J. Mach. Learn. Res.
Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley
2013 A* conf
MLI: An API for Distributed Machine Learning.
ICDM
Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2013 J jnl
MLI: An API for Distributed Machine Learning.
CoRR
Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2013 A conf
MLbase: A Distributed Machine-learning System.
CIDR
Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan
2012 — book
Foundations of Machine Learning.
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
2012 J jnl
Sampling Methods for the Nyström Method.
J. Mach. Learn. Res.
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
2012 A* conf
The Big Data Bootstrap.
ICML
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan
2011 A conf
Can matrix coherence be efficiently and accurately estimated?
AISTATS
Mehryar Mohri, Ameet Talwalkar
2011 J jnl
Divide-and-Conquer Matrix Factorization
CoRR
Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2011 — conf
Divide-and-Conquer Matrix Factorization.
NIPS
Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2011 J jnl
Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)
CoRR
Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song
2010 — —
Matrix Approximation for Large-scale Learning.
Ameet Talwalkar
2010 J jnl
Matrix Coherence and the Nystrom Method
CoRR
Ameet Talwalkar, Afshin Rostamizadeh
2010 A conf
Matrix Coherence and the Nystrom Method.
UAI
Ameet Talwalkar, Afshin Rostamizadeh
2010 J jnl
On the Estimation of Coherence
CoRR
Mehryar Mohri, Ameet Talwalkar
2010 A conf
On the Impact of Kernel Approximation on Learning Accuracy.
AISTATS
Corinna Cortes, Mehryar Mohri, Ameet Talwalkar
2009 — conf
Ensemble Nystrom Method.
NIPS
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
2009 A* conf
On sampling-based approximate spectral decomposition.
ICML
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
2009 A conf
Sampling Techniques for the Nystrom Method.
AISTATS
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
2008 A* conf
Large-scale manifold learning.
CVPR
Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley
2008 A* conf
Sequence kernels for predicting protein essentiality.
ICML
Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar