Large-scale manifold learning. - Ameet Talwalkar; Sanjiv Kumar; Henry A. Rowley (CVPR, 2008)

Sequence kernels for predicting protein essentiality. - Cyril Allauzen; Mehryar Mohri; Ameet Talwalkar (ICML, 2008)

On sampling-based approximate spectral decomposition. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (ICML, 2009)

Ensemble Nystrom Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (NIPS, 2009)

Sampling Techniques for the Nystrom Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (AISTATS, 2009)

Matrix Coherence and the Nystrom Method. - Ameet Talwalkar; Afshin Rostamizadeh (UAI, 2010)

On the Estimation of Coherence - Mehryar Mohri; Ameet Talwalkar (CoRR, 2010)

On the Impact of Kernel Approximation on Learning Accuracy. - Corinna Cortes; Mehryar Mohri; Ameet Talwalkar (AISTATS, 2010)

Matrix Approximation for Large-scale Learning. - Ameet Talwalkar (2010)

Divide-and-Conquer Matrix Factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (NIPS, 2011)

Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN) - Neil Zhenqiang Gong; Ameet Talwalkar; Lester W. Mackey; Ling Huang 001; Eui Chul Richard Shin; Emil Stefanov; Elaine Shi; Dawn Song (CoRR, 2011)

Can matrix coherence be efficiently and accurately estimated? - Mehryar Mohri; Ameet Talwalkar (AISTATS, 2011)

Foundations of Machine Learning. - Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar (MIT Press, 2012)

The Big Data Bootstrap. - Ariel Kleiner; Ameet Talwalkar; Purnamrita Sarkar; Michael I. Jordan (ICML, 2012)

Sampling Methods for the Nyström Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (J. Mach. Learn. Res., 2012)

MLbase: A Distributed Machine-learning System. - Tim Kraska; Ameet Talwalkar; John C. Duchi; Rean Griffith; Michael J. Franklin; Michael I. Jordan (CIDR, 2013)

Distributed Low-Rank Subspace Segmentation. - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (ICCV, 2013)

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 (ICDM, 2013)

A general bootstrap performance diagnostic. - Ariel Kleiner; Ameet Talwalkar; Sameer Agarwal 002; Ion Stoica; Michael I. Jordan (KDD, 2013)

Divide-and-Conquer Subspace Segmentation - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (CoRR, 2013)

Large-scale SVD and manifold learning. - Ameet Talwalkar; Sanjiv Kumar; Mehryar Mohri; Henry A. Rowley (J. Mach. Learn. Res., 2013)

Changepoint Analysis for Efficient Variant Calling. - Adam E. Bloniarz; Ameet Talwalkar; Jonathan Terhorst; Michael I. Jordan; David A. Patterson 001; Bin Yu 001; Yun S. Song (RECOMB, 2014)

Knowing when you're wrong: building fast and reliable approximate query processing systems. - Sameer Agarwal 002; Henry Milner; Ariel Kleiner; Ameet Talwalkar; Michael I. Jordan; Samuel Madden 001; Barzan Mozafari; Ion Stoica (SIGMOD Conference, 2014)

SMaSH: a benchmarking toolkit for human genome variant calling. - Ameet Talwalkar; Jesse Liptrap; Julie Newcomb; Christopher Hartl; Jonathan Terhorst; Kristal Curtis; Ma'ayan Bresler; Yun S. Song; Michael I. Jordan; David A. Patterson 001 (Bioinform., 2014)

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. - Neil Zhenqiang Gong; Ameet Talwalkar; Lester W. Mackey; Ling Huang 001; Eui Chul Richard Shin; Emil Stefanov; Elaine Shi; Dawn Song (ACM Trans. Intell. Syst. Technol., 2014)

Automating model search for large scale machine learning. - Evan Randall Sparks; Ameet Talwalkar; Daniel Haas; Michael J. Franklin; Michael I. Jordan; Tim Kraska (SoCC, 2015)

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. - Evan Randall Sparks; Ameet Talwalkar; Michael J. Franklin; Michael I. Jordan; Tim Kraska (CoRR, 2015)

Distributed matrix completion and robust factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (J. Mach. Learn. Res., 2015)

Supervised Neighborhoods for Distributed Nonparametric Regression. - Adam E. Bloniarz; Ameet Talwalkar; Bin Yu 001; Christopher Wu (AISTATS, 2016)

Non-stochastic Best Arm Identification and Hyperparameter Optimization. - Kevin G. Jamieson; Ameet Talwalkar (AISTATS, 2016)

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. - Firas Abuzaid; Joseph K. Bradley; Feynman T. Liang; Andrew Feng; Lee Yang; Matei Zaharia; Ameet Talwalkar (NIPS, 2016)

Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. - Lisha Li; Kevin G. Jamieson; Giulia DeSalvo; Afshin Rostamizadeh; Ameet Talwalkar (CoRR, 2016)

MLlib: Machine Learning in Apache Spark. - 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; et al. (J. Mach. Learn. Res., 2016)

Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems. - Ariyam Das; Ishan Upadhyaya; Xiangrui Meng; Ameet Talwalkar (CIKM, 2017)

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. - Lisha Li; Kevin G. Jamieson; Giulia DeSalvo; Afshin Rostamizadeh; Ameet Talwalkar (ICLR (Poster), 2017)

Paleo: A Performance Model for Deep Neural Networks. - Hang Qi; Evan Randall Sparks; Ameet Talwalkar (ICLR (Poster), 2017)

Variable Importance Using Decision Trees. - S. Jalil Kazemitabar; Arash A. Amini; Adam E. Bloniarz; Ameet Talwalkar (NIPS, 2017)

Federated Multi-Task Learning. - Virginia Smith; Chao-Kai Chiang; Maziar Sanjabi; Ameet Talwalkar (NIPS, 2017)

Parle: parallelizing stochastic gradient descent. - Pratik Chaudhari; Carlo Baldassi; Riccardo Zecchina; Stefano Soatto; Ameet Talwalkar (CoRR, 2017)

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. - Lisha Li; Kevin G. Jamieson; Giulia DeSalvo; Afshin Rostamizadeh; Ameet Talwalkar (J. Mach. Learn. Res., 2017)

Model Agnostic Supervised Local Explanations. - Gregory Plumb; Denali Molitor; Ameet Talwalkar (NeurIPS, 2018)

Supervised Local Modeling for Interpretability. - Gregory Plumb; Denali Molitor; Ameet Talwalkar (CoRR, 2018)

Massively Parallel Hyperparameter Tuning. - Liam Li; Kevin G. Jamieson; Afshin Rostamizadeh; Ekaterina Gonina; Moritz Hardt; Benjamin Recht; Ameet Talwalkar (CoRR, 2018)

LEAF: A Benchmark for Federated Settings. - Sebastian Caldas; Peter Wu; Tian Li 005; Jakub Konecný; H. Brendan McMahan; Virginia Smith; Ameet Talwalkar (CoRR, 2018)

On the Convergence of Federated Optimization in Heterogeneous Networks. - Anit Kumar Sahu; Tian Li 005; Maziar Sanjabi; Manzil Zaheer; Ameet Talwalkar; Virginia Smith (CoRR, 2018)

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. - Sebastian Caldas; Jakub Konecný; H. Brendan McMahan; Ameet Talwalkar (CoRR, 2018)

FedDANE: A Federated Newton-Type Method. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (ACSSC, 2019)

Provable Guarantees for Gradient-Based Meta-Learning. - Maria-Florina Balcan; Mikhail Khodak; Ameet Talwalkar (ICML, 2019)

Adaptive Gradient-Based Meta-Learning Methods. - Mikhail Khodak; Maria-Florina Balcan; Ameet Talwalkar (NeurIPS, 2019)

Random Search and Reproducibility for Neural Architecture Search. - Liam Li; Ameet Talwalkar (UAI, 2019)

Regularizing Black-box Models for Improved Interpretability. - Gregory Plumb; Maruan Al-Shedivat; Eric P. Xing; Ameet Talwalkar (CoRR, 2019)

One-Shot Federated Learning. - Neel Guha; Ameet Talwalkar; Virginia Smith (CoRR, 2019)

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. - Liam Li; Evan Randall Sparks; Kevin G. Jamieson; Ameet Talwalkar (CoRR, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version). - Gregory Plumb; Maruan Al-Shedivat; Eric P. Xing; Ameet Talwalkar (CoRR, 2019)

Learning Fair Representations for Kernel Models. - Zilong Tan; Samuel Yeom; Matt Fredrikson; Ameet Talwalkar (AISTATS, 2020)

Differentially Private Meta-Learning. - Jeffrey Li; Mikhail Khodak; Sebastian Caldas; Ameet Talwalkar (ICLR, 2020)

FACT: A Diagnostic for Group Fairness Trade-offs. - Joon Sik Kim; Jiahao Chen 001; Ameet Talwalkar (ICML, 2020)

Explaining Groups of Points in Low-Dimensional Representations. - Gregory Plumb; Jonathan Terhorst; Sriram Sankararaman; Ameet Talwalkar (ICML, 2020)

A System for Massively Parallel Hyperparameter Tuning. - Liam Li; Kevin G. Jamieson; Afshin Rostamizadeh; Ekaterina Gonina; Jonathan Ben-tzur; Moritz Hardt; Benjamin Recht; Ameet Talwalkar (MLSys, 2020)

Federated Optimization in Heterogeneous Networks. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (MLSys, 2020)

Regularizing Black-box Models for Improved Interpretability. - Gregory Plumb; Maruan Al-Shedivat; Ángel Alexander Cabrera; Adam Perer; Eric P. Xing; Ameet Talwalkar (NeurIPS, 2020)

Model-Agnostic Characterization of Fairness Trade-offs. - Joon Sik Kim; Jiahao Chen 001; Ameet Talwalkar (CoRR, 2020)

Federated Learning: Challenges, Methods, and Future Directions. - Tian Li 005; Anit Kumar Sahu; Ameet Talwalkar; Virginia Smith (IEEE Signal Process. Mag., 2020)

On Data Efficiency of Meta-learning. - Maruan Al-Shedivat; Liam Li; Eric P. Xing; Ameet Talwalkar (AISTATS, 2021)

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. - Jeremy Cohen 001; Simran Kaur 001; Yuanzhi Li; J. Zico Kolter; Ameet Talwalkar (ICLR, 2021)

Geometry-Aware Gradient Algorithms for Neural Architecture Search. - Liam Li; Mikhail Khodak; Nina Balcan; Ameet Talwalkar (ICLR, 2021)

A Learning Theoretic Perspective on Local Explainability. - Jeffrey Li; Vaishnavh Nagarajan; Gregory Plumb; Ameet Talwalkar (ICLR, 2021)

Learning-to-learn non-convex piecewise-Lipschitz functions. - Maria-Florina Balcan; Mikhail Khodak; Dravyansh Sharma; Ameet Talwalkar (NeurIPS, 2021)

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. - Mikhail Khodak; Renbo Tu; Tian Li 005; Liam Li; Maria-Florina Balcan; Virginia Smith; Ameet Talwalkar (NeurIPS, 2021)

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. - Nicholas Roberts; Samuel Guo; Cong Xu; Ameet Talwalkar; David Lander; Lvfang Tao; Linhang Cai; Shuaicheng Niu; Jianyu Heng; Hongyang Qin; Minwen Deng; Johannes Hog; et al. (NeurIPS (Competition and Demos), 2021)

Rethinking Neural Operations for Diverse Tasks. - Nicholas Roberts; Mikhail Khodak; Tri Dao; Liam Li; Christopher Ré; Ameet Talwalkar (NeurIPS, 2021)

Towards Connecting Use Cases and Methods in Interpretable Machine Learning. - Valerie Chen; Jeffrey Li; Joon Sik Kim; Gregory Plumb; Ameet Talwalkar (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. - Renbo Tu; Mikhail Khodak; Nicholas Roberts; Ameet Talwalkar (CoRR, 2021)

Interpretable Machine Learning: Moving from mythos to diagnostics. - Valerie Chen; Jeffrey Li; Joon Sik Kim; Gregory Plumb; Ameet Talwalkar (ACM Queue, 2021)

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. - Sen Lin 001; Ming Shi 003; Anish Arora; Raef Bassily; Elisa Bertino; Constantine Caramanis; Kaushik R. Chowdhury; Eylem Ekici; Atilla Eryilmaz; Stratis Ioannidis; Nan Jiang 008; Gauri Joshi; et al. (CIC, 2022)

Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. - Lucio M. Dery; Paul Michel; Ameet Talwalkar; Graham Neubig (ICLR, 2022)

Sanity Simulations for Saliency Methods. - Joon Sik Kim; Gregory Plumb; Ameet Talwalkar (ICML, 2022)

Provably tuning the ElasticNet across instances. - Maria-Florina Balcan; Misha Khodak; Dravyansh Sharma; Ameet Talwalkar (NeurIPS, 2022)

Use-Case-Grounded Simulations for Explanation Evaluation. - Valerie Chen; Nari Johnson; Nicholay Topin; Gregory Plumb; Ameet Talwalkar (NeurIPS, 2022)

Bayesian Persuasion for Algorithmic Recourse. - Keegan Harris; Valerie Chen; Joon Sik Kim; Ameet Talwalkar; Hoda Heidari; Zhiwei Steven Wu (NeurIPS, 2022)

Learning Predictions for Algorithms with Predictions. - Misha Khodak; Maria-Florina Balcan; Ameet Talwalkar; Sergei Vassilvitskii (NeurIPS, 2022)

Efficient Architecture Search for Diverse Tasks. - Junhong Shen; Mikhail Khodak; Ameet Talwalkar (NeurIPS, 2022)

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. - Renbo Tu; Nicholas Roberts; Mikhail Khodak; Junhong Shen; Frederic Sala; Ameet Talwalkar (NeurIPS, 2022)

Learning Predictions for Algorithms with Predictions. - Mikhail Khodak; Maria-Florina Balcan; Ameet Talwalkar; Sergei Vassilvitskii (CoRR, 2022)

Evaluating Systemic Error Detection Methods using Synthetic Images. - Gregory Plumb; Nari Johnson; Ángel Alexander Cabrera; Marco Túlio Ribeiro; Ameet Talwalkar (CoRR, 2022)

Provably tuning the ElasticNet across instances. - Maria-Florina Balcan; Mikhail Khodak; Dravyansh Sharma; Ameet Talwalkar (CoRR, 2022)

SONAR: Joint Architecture and System Optimization Search. - Elias Jääsaari; Michelle Ma; Ameet Talwalkar; Tianqi Chen (CoRR, 2022)

AutoML for Climate Change: A Call to Action. - Renbo Tu; Nicholas Roberts; Vishak Prasad; Sibasis Nayak; Paarth Jain; Frederic Sala; Ganesh Ramakrishnan; Ameet Talwalkar; Willie Neiswanger; Colin White (CoRR, 2022)

Finding and Fixing Spurious Patterns with Explanations. - Gregory Plumb; Marco Túlio Ribeiro; Ameet Talwalkar (Trans. Mach. Learn. Res., 2022)

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. - Ángel Alexander Cabrera; Erica Fu; Donald Bertucci; Kenneth Holstein; Ameet Talwalkar; Jason I. Hong; Adam Perer (CHI, 2023)

FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. - Matthew Barker; Emma Kallina; Dhananjay Ashok; Katherine M. Collins; Ashley Casovan; Adrian Weller; Ameet Talwalkar; Valerie Chen; Umang Bhatt (EAAMO, 2023)

AANG : Automating Auxiliary Learning. - Lucio M. Dery; Paul Michel; Mikhail Khodak; Graham Neubig; Ameet Talwalkar (ICLR, 2023)

Cross-Modal Fine-Tuning: Align then Refine. - Junhong Shen; Liam Li; Lucio M. Dery; Corey Staten; Mikhail Khodak; Graham Neubig; Ameet Talwalkar (ICML, 2023)

On Noisy Evaluation in Federated Hyperparameter Tuning. - Kevin Kuo; Pratiksha Thaker; Mikhail Khodak; John Nguyen; Daniel Jiang; Ameet Talwalkar; Virginia Smith (MLSys, 2023)

Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms. - Nari Johnson; Ángel Alexander Cabrera; Gregory Plumb; Ameet Talwalkar (CoRR, 2023)

Do LLMs exhibit human-like response biases? A case study in survey design. - Lindia Tjuatja; Valerie Chen; Sherry Tongshuang Wu; Ameet Talwalkar; Graham Neubig (CoRR, 2023)

Assisting Human Decisions in Document Matching. - Joon Sik Kim; Valerie Chen; Danish Pruthi; Nihar B. Shah; Ameet Talwalkar (Trans. Mach. Learn. Res., 2023)

Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models. - Gregory Plumb; Nari Johnson; Ángel Alexander Cabrera; Ameet Talwalkar (Trans. Mach. Learn. Res., 2023)

Perspectives on incorporating expert feedback into model updates. - Valerie Chen; Umang Bhatt; Hoda Heidari; Adrian Weller; Ameet Talwalkar (Patterns, 2023)

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. - Kasun Amarasinghe; Kit T. Rodolfa; Sérgio M. Jesus; Valerie Chen; Vladimir Balayan; Pedro Saleiro; Pedro Bizarro; Ameet Talwalkar; Rayid Ghani (AAAI, 2024)

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances. - Mikhail Khodak; Edmond Chow; Maria-Florina Balcan; Ameet Talwalkar (ICLR, 2024)

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. - Lucio M. Dery; Steven Kolawole; Jean-François Kagey; Virginia Smith; Graham Neubig; Ameet Talwalkar (CoRR, 2024)

UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation. - Junhong Shen; Tanya Marwah; Ameet Talwalkar (CoRR, 2024)

Revisiting Cascaded Ensembles for Efficient Inference. - Steven Kolawole; Don Kurian Dennis; Ameet Talwalkar; Virginia Smith (CoRR, 2024)

Modulating Language Model Experiences through Frictions. - Katherine M. Collins; Valerie Chen; Ilia Sucholutsky; Hannah Rose Kirk; Malak Sadek; Holli Sargeant; Ameet Talwalkar; Adrian Weller; Umang Bhatt (CoRR, 2024)

The Impact of Element Ordering on LM Agent Performance. - Wayne Chi; Ameet Talwalkar; Chris Donahue (CoRR, 2024)

ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data. - Junhong Shen; Atishay Jain; Zedian Xiao; Ishan Amlekar; Mouad Hadji; Aaron Podolny; Ameet Talwalkar (CoRR, 2024)

Multitask Learning Can Improve Worst-Group Outcomes. - Atharva Kulkarni; Lucio M. Dery; Amrith Setlur; Aditi Raghunathan; Ameet Talwalkar; Graham Neubig (Trans. Mach. Learn. Res., 2024)

UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation. - Junhong Shen; Tanya Marwah; Ameet Talwalkar (Trans. Mach. Learn. Res., 2024)

Learning Personalized Decision Support Policies. - Umang Bhatt; Valerie Chen; Katherine M. Collins; Parameswaran Kamalaruban; Emma Kallina; Adrian Weller; Ameet Talwalkar (AAAI, 2025)

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback. - Jane Pan; Ryan Shar; Jacob Pfau; Ameet Talwalkar; He He 001; Valerie Chen (ACL (Findings), 2025)

Need Help? Designing Proactive AI Assistants for Programming. - Valerie Chen; Alan Zhu 003; Sebastian Zhao; Hussein Mozannar; David A. Sontag; Ameet Talwalkar (CHI, 2025)

Understanding Optimization in Deep Learning with Central Flows. - Jeremy Cohen 001; Alex Damian; Ameet Talwalkar; J. Zico Kolter; Jason D. Lee (ICLR, 2025)

Specialized Foundation Models Struggle to Beat Supervised Baselines. - Zongzhe Xu; Ritvik Gupta; Wenduo Cheng; Alexander Shen 003; Junhong Shen; Ameet Talwalkar; Mikhail Khodak (ICLR, 2025)

CodingGenie: A Proactive LLM-Powered Programming Assistant. - Sebastian Zhao; Alan Zhu 003; Hussein Mozannar; David A. Sontag; Ameet Talwalkar; Valerie Chen (SIGSOFT FSE Companion, 2025)

L2G: Repurposing Language Models for Genomics Tasks. - Wenduo Cheng; Junhong Shen; Mikhail Khodak; Jian Ma; Ameet Talwalkar (Trans. Mach. Learn. Res., 2025)

Agreement-Based Cascading for Efficient Inference. - Steven Kolawole; Don Kurian Dennis; Ameet Talwalkar; Virginia Smith (Trans. Mach. Learn. Res., 2025)

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Hussein Mozannar; Valerie Chen; Mohammed Alsobay; Subhro Das; Sebastian Zhao; Dennis Wei; Manish Nagireddy; Prasanna Sattigeri; Ameet Talwalkar; David A. Sontag (Trans. Mach. Learn. Res., 2025)

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 (CoRR, 2025)

CodePDE: An Inference Framework for LLM-driven PDE Solver Generation. - Shanda Li; Tanya Marwah; Junhong Shen; Weiwei Sun; Andrej Risteski; Yiming Yang; Ameet Talwalkar (CoRR, 2025)

This Time is Different: An Observability Perspective on Time Series Foundation Models. - 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; et al. (CoRR, 2025)

A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial Optimization. - Shengyu Feng; Weiwei Sun; Shanda Li; Ameet Talwalkar; Yiming Yang (CoRR, 2025)

Sample Complexity and Representation Ability of Test-time Scaling Paradigms. - Baihe Huang; Shanda Li; Tianhao Wu 002; Yiming Yang; Ameet Talwalkar; Kannan Ramchandran; Michael I. Jordan; Jiantao Jiao (CoRR, 2025)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. - Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang 008; Tong Zhang 001; Ameet Talwalkar; Aviral Kumar (CoRR, 2025)

Towards Community-Driven Agents for Machine Learning Engineering. - Sijie Li; Weiwei Sun; Shanda Li; Ameet Talwalkar; Yiming Yang (CoRR, 2025)

Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows. - Valerie Chen; Ameet Talwalkar; Robert Brennan; Graham Neubig (CoRR, 2025)

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption. - Rebecca Yu; Valerie Chen; Ameet Talwalkar; Hoda Heidari (CoRR, 2025)