Ameet Talwalkar

Orcid: 0000-0001-6650-1893

Affiliations:
  • Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA


According to our database1, Ameet Talwalkar authored at least 112 papers between 2008 and 2024.

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Bibliography

2024
Multitask Learning Can Improve Worst-Group Outcomes.
Trans. Mach. Learn. Res., 2024

Need Help? Designing Proactive AI Assistants for Programming.
CoRR, 2024

The Impact of Element Ordering on LM Agent Performance.
CoRR, 2024

Modulating Language Model Experiences through Frictions.
CoRR, 2024

Revisiting Cascaded Ensembles for Efficient Inference.
CoRR, 2024

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers.
CoRR, 2024

UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation.
CoRR, 2024

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes.
CoRR, 2024

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Perspectives on incorporating expert feedback into model updates.
Patterns, July, 2023

Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models.
Trans. Mach. Learn. Res., 2023

Assisting Human Decisions in Document Matching.
Trans. Mach. Learn. Res., 2023

Do LLMs exhibit human-like response biases? A case study in survey design.
CoRR, 2023

Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms.
CoRR, 2023

Learning Personalized Decision Support Policies.
CoRR, 2023

On Noisy Evaluation in Federated Hyperparameter Tuning.
Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

Cross-Modal Fine-Tuning: Align then Refine.
Proceedings of the International Conference on Machine Learning, 2023

AANG : Automating Auxiliary Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines.
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning.
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022
Finding and Fixing Spurious Patterns with Explanations.
Trans. Mach. Learn. Res., 2022

AutoML for Climate Change: A Call to Action.
CoRR, 2022

SONAR: Joint Architecture and System Optimization Search.
CoRR, 2022

Provably tuning the ElasticNet across instances.
CoRR, 2022

Evaluating Systemic Error Detection Methods using Synthetic Images.
CoRR, 2022

Learning Predictions for Algorithms with Predictions.
CoRR, 2022

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient Architecture Search for Diverse Tasks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Predictions for Algorithms with Predictions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Persuasion for Algorithmic Recourse.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Use-Case-Grounded Simulations for Explanation Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Provably tuning the ElasticNet across instances.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sanity Simulations for Saliency Methods.
Proceedings of the International Conference on Machine Learning, 2022

Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative.
Proceedings of the Tenth International Conference on Learning Representations, 2022


2021
Interpretable Machine Learning: Moving from mythos to diagnostics.
ACM Queue, 2021

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search.
CoRR, 2021

A Field Guide to Federated Optimization.
CoRR, 2021

Towards Connecting Use Cases and Methods in Interpretable Machine Learning.
CoRR, 2021

Rethinking Neural Operations for Diverse Tasks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021


Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning-to-learn non-convex piecewise-Lipschitz functions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Learning Theoretic Perspective on Local Explainability.
Proceedings of the 9th International Conference on Learning Representations, 2021

Geometry-Aware Gradient Algorithms for Neural Architecture Search.
Proceedings of the 9th International Conference on Learning Representations, 2021

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.
Proceedings of the 9th International Conference on Learning Representations, 2021

On Data Efficiency of Meta-learning.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Federated Learning: Challenges, Methods, and Future Directions.
IEEE Signal Process. Mag., 2020

Model-Agnostic Characterization of Fairness Trade-offs.
CoRR, 2020

Regularizing Black-box Models for Improved Interpretability.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Optimization in Heterogeneous Networks.
Proceedings of the Third Conference on Machine Learning and Systems, 2020

A System for Massively Parallel Hyperparameter Tuning.
Proceedings of the Third Conference on Machine Learning and Systems, 2020

Explaining Groups of Points in Low-Dimensional Representations.
Proceedings of the 37th International Conference on Machine Learning, 2020

FACT: A Diagnostic for Group Fairness Trade-offs.
Proceedings of the 37th International Conference on Machine Learning, 2020

Differentially Private Meta-Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Fair Representations for Kernel Models.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version).
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.
CoRR, 2019

One-Shot Federated Learning.
CoRR, 2019

Regularizing Black-box Models for Improved Interpretability.
CoRR, 2019

Random Search and Reproducibility for Neural Architecture Search.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Adaptive Gradient-Based Meta-Learning Methods.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provable Guarantees for Gradient-Based Meta-Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

FedDANE: A Federated Newton-Type Method.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.
CoRR, 2018

On the Convergence of Federated Optimization in Heterogeneous Networks.
CoRR, 2018

LEAF: A Benchmark for Federated Settings.
CoRR, 2018

Massively Parallel Hyperparameter Tuning.
CoRR, 2018

Supervised Local Modeling for Interpretability.
CoRR, 2018

Model Agnostic Supervised Local Explanations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
J. Mach. Learn. Res., 2017

Parle: parallelizing stochastic gradient descent.
CoRR, 2017

Federated Multi-Task Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variable Importance Using Decision Trees.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Paleo: A Performance Model for Deep Neural Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization.
Proceedings of the 5th International Conference on Learning Representations, 2017

Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016
MLlib: Machine Learning in Apache Spark.
J. Mach. Learn. Res., 2016

Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits.
CoRR, 2016

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Non-stochastic Best Arm Identification and Hyperparameter Optimization.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Supervised Neighborhoods for Distributed Nonparametric Regression.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Distributed matrix completion and robust factorization.
J. Mach. Learn. Res., 2015

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.
CoRR, 2015

Automating model search for large scale machine learning.
Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015

2014
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network.
ACM Trans. Intell. Syst. Technol., 2014

SMaSH: a benchmarking toolkit for human genome variant calling.
Bioinform., 2014

Knowing when you're wrong: building fast and reliable approximate query processing systems.
Proceedings of the International Conference on Management of Data, 2014

Changepoint Analysis for Efficient Variant Calling.
Proceedings of the Research in Computational Molecular Biology, 2014

2013
Large-scale SVD and manifold learning.
J. Mach. Learn. Res., 2013

Divide-and-Conquer Subspace Segmentation
CoRR, 2013

A general bootstrap performance diagnostic.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

MLI: An API for Distributed Machine Learning.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Distributed Low-Rank Subspace Segmentation.
Proceedings of the IEEE International Conference on Computer Vision, 2013

MLbase: A Distributed Machine-learning System.
Proceedings of the Sixth Biennial Conference on Innovative Data Systems Research, 2013

2012
Sampling Methods for the Nyström Method.
J. Mach. Learn. Res., 2012

The Big Data Bootstrap.
Proceedings of the 29th International Conference on Machine Learning, 2012

Foundations of Machine Learning.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-01825-8, 2012

2011
Can matrix coherence be efficiently and accurately estimated?
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)
CoRR, 2011

Divide-and-Conquer Matrix Factorization.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Matrix Approximation for Large-scale Learning.
PhD thesis, 2010

On the Impact of Kernel Approximation on Learning Accuracy.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On the Estimation of Coherence
CoRR, 2010

Matrix Coherence and the Nystrom Method.
Proceedings of the UAI 2010, 2010

2009
Sampling Techniques for the Nystrom Method.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Ensemble Nystrom Method.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

On sampling-based approximate spectral decomposition.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Sequence kernels for predicting protein essentiality.
Proceedings of the Machine Learning, 2008

Large-scale manifold learning.
Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008


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