Ameet Talwalkar

Copilot Arena: A Platform for Code LLM Evaluation in the Wild ICML 2025 Learning Personalized Decision Support Policies AAAI 2025 Specialized Foundation Models Struggle to Beat Supervised Baselines ICLR 2025 Understanding Optimization in Deep Learning with Central Flows ICLR 2025 When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback ACL 2025 Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances ICLR 2024 On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods AAAI 2024 AANG : Automating Auxiliary Learning ICLR 2023 Cross-Modal Fine-Tuning: Align then Refine ICML 2023 Bayesian Persuasion for Algorithmic Recourse NeurIPS 2022 Efficient Architecture Search for Diverse Tasks NeurIPS 2022 Learning Predictions for Algorithms with Predictions NeurIPS 2022 NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks NeurIPS 2022 Provably tuning the ElasticNet across instances NeurIPS 2022 Sanity Simulations for Saliency Methods ICML 2022 Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative ICLR 2022 Use-Case-Grounded Simulations for Explanation Evaluation NeurIPS 2022 A Learning Theoretic Perspective on Local Explainability ICLR 2021 Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing NeurIPS 2021 Geometry-Aware Gradient Algorithms for Neural Architecture Search ICLR 2021 Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability ICLR 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021 On Data Efficiency of Meta-learning AISTATS 2021 Rethinking Neural Operations for Diverse Tasks NeurIPS 2021 Differentially Private Meta-Learning ICLR 2020 Explaining Groups of Points in Low-Dimensional Representations ICML 2020 FACT: A Diagnostic for Group Fairness Trade-offs ICML 2020 Learning Fair Representations for Kernel Models AISTATS 2020 Regularizing Black-box Models for Improved Interpretability NeurIPS 2020 Provable Guarantees for Gradient-Based Meta-Learning ICML 2019 Random Search and Reproducibility for Neural Architecture Search UAI 2019 Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization JMLR 2018 MLlib: Machine Learning in Apache Spark JMLR 2016 Non-stochastic Best Arm Identification and Hyperparameter Optimization AISTATS 2016 Supervised Neighborhoods for Distributed Nonparametric Regression AISTATS 2016 Distributed Matrix Completion and Robust Factorization JMLR 2015 Distributed Low-Rank Subspace Segmentation ICCV 2013 Large-scale SVD and Manifold Learning JMLR 2013 Sampling Methods for the Nyström Method JMLR 2012 Can matrix coherence be efficiently and accurately estimated? AISTATS 2011 Divide-and-Conquer Matrix Factorization NeurIPS 2011 On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010 Ensemble Nystrom Method NeurIPS 2009 Sampling Techniques for the Nystrom Method AISTATS 2005