Ameet Talwalkar
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
44
Venues
18
Active years
2008–2026
Best venue rank
A*
Where they publish
Papers
44 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | ACL | Scaling Collaborative Effort with Agents. | 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 |
| 2026 | CHI | Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows. | Valerie Chen, Ameet Talwalkar, Robert Brennan, Graham Neubig |
| 2025 | AAAI | Learning Personalized Decision Support Policies. | Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar |
| 2025 | ACL | When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback. | Jane Pan, Ryan Shar, Jacob Pfau, Ameet Talwalkar, He He, Valerie Chen |
| 2025 | AIES | Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption. | Rebecca Yu, Valerie Chen, Ameet Talwalkar, Hoda Heidari |
| 2025 | CHI | Need Help? Designing Proactive AI Assistants for Programming. | Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar |
| 2025 | ICLR | Understanding Optimization in Deep Learning with Central Flows. | Jeremy Cohen, Alex Damian, Ameet Talwalkar, J. Zico Kolter, Jason D. Lee |
| 2025 | ICLR | Specialized Foundation Models Struggle to Beat Supervised Baselines. | Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak |
| 2025 | ICML | Copilot Arena: A Platform for Code LLM Evaluation in the Wild. | Wayne Chi, Valerie Chen, Anastasios Nikolas Angelopoulos, Wei-Lin Chiang, Aditya Mittal, Naman Jain, Tianjun Zhang, Ion Stoica, Chris Donahue, Ameet Talwalkar |
| 2024 | AAAI | On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. | Kasun Amarasinghe, Kit T. Rodolfa, Srgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani |
| 2024 | ICLR | Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. | Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar |
| 2023 | CHI | Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. | ngel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer |
| 2023 | ICLR | AANG : Automating Auxiliary Learning. | Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar |
| 2023 | ICML | Cross-Modal Fine-Tuning: Align then Refine. | Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar |
| 2022 | ICLR | Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. | Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig |
| 2022 | ICML | Sanity Simulations for Saliency Methods. | Joon Sik Kim, Gregory Plumb, Ameet Talwalkar |
| 2021 | AISTATS | On Data Efficiency of Meta-learning. | Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar |
| 2021 | ICLR | Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. | Jeremy Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar |
| 2021 | ICLR | Geometry-Aware Gradient Algorithms for Neural Architecture Search. | Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar |
| 2021 | ICLR | A Learning Theoretic Perspective on Local Explainability. | Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar |
| 2020 | AISTATS | Learning Fair Representations for Kernel Models. | Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar |
| 2020 | ICLR | Differentially Private Meta-Learning. | Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar |
| 2020 | ICML | FACT: A Diagnostic for Group Fairness Trade-offs. | Joon Sik Kim, Jiahao Chen, Ameet Talwalkar |
| 2020 | ICML | Explaining Groups of Points in Low-Dimensional Representations. | Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar |
| 2019 | ACSSC | FedDANE: A Federated Newton-Type Method. | Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith |
| 2019 | ICML | Provable Guarantees for Gradient-Based Meta-Learning. | Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar |
| 2019 | UAI | Random Search and Reproducibility for Neural Architecture Search. | Liam Li, Ameet Talwalkar |
| 2017 | CIKM | Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems. | Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar |
| 2017 | ICLR | Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. | Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar |
| 2017 | ICLR | Paleo: A Performance Model for Deep Neural Networks. | Hang Qi, Evan Randall Sparks, Ameet Talwalkar |
| 2016 | AISTATS | Supervised Neighborhoods for Distributed Nonparametric Regression. | Adam E. Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu |
| 2016 | AISTATS | Non-stochastic Best Arm Identification and Hyperparameter Optimization. | Kevin Jamieson, Ameet Talwalkar |
| 2015 | CLOUD | Automating model search for large scale machine learning. | Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska |
| 2014 | RECOMB | Changepoint Analysis for Efficient Variant Calling. | Adam E. Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song |
| 2014 | SIGMOD | Knowing when you're wrong: building fast and reliable approximate query processing systems. | Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica |
| 2013 | CIDR | MLbase: A Distributed Machine-learning System. | Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan |
| 2013 | ICCV | Distributed Low-Rank Subspace Segmentation. | Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan |
| 2013 | ICDM | MLI: An API for Distributed Machine Learning. | Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska |
| 2013 | KDD | A general bootstrap performance diagnostic. | Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan |
| 2012 | ICML | The Big Data Bootstrap. | Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan |
| 2010 | UAI | Matrix Coherence and the Nystrom Method. | Ameet Talwalkar, Afshin Rostamizadeh |
| 2009 | ICML | On sampling-based approximate spectral decomposition. | Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar |
| 2008 | CVPR | Large-scale manifold learning. | Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley |
| 2008 | ICML | Sequence kernels for predicting protein essentiality. | Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar |