Ameet Talwalkar

Ameet Talwalkar

Carnegie Mellon University

H-index: 49

North America-United States

About Ameet Talwalkar

Ameet Talwalkar, With an exceptional h-index of 49 and a recent h-index of 44 (since 2020), a distinguished researcher at Carnegie Mellon University, specializes in the field of Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers

On the importance of application-grounded experimental design for evaluating explainable ml methods

Ups: Towards foundation models for pde solving via cross-modal adaptation

Feedbacklogs: Recording and incorporating stakeholder feedback into machine learning pipelines

On noisy evaluation in federated hyperparameter tuning

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances

Multitask Learning Can Improve Worst-Group Outcomes

Ameet Talwalkar Information

University

Position

and Determined AI

Citations(all)

31430

Citations(since 2020)

25265

Cited By

12621

hIndex(all)

49

hIndex(since 2020)

44

i10Index(all)

82

i10Index(since 2020)

76

Email

University Profile Page

Carnegie Mellon University

Google Scholar

View Google Scholar Profile

Ameet Talwalkar Skills & Research Interests

Machine Learning

Top articles of Ameet Talwalkar

Title

Journal

Author(s)

Publication Date

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes

arXiv preprint arXiv:2402.05406

Lucio Dery

Steven Kolawole

Jean-Francois Kagey

Virginia Smith

Graham Neubig

...

2024/2/8

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers

arXiv preprint arXiv:2404.02806

Hussein Mozannar

Valerie Chen

Mohammed Alsobay

Subhro Das

Sebastian Zhao

...

2024/4/3

On the importance of application-grounded experimental design for evaluating explainable ml methods

Proceedings of the AAAI Conference on Artificial Intelligence

Kasun Amarasinghe

Kit T Rodolfa

Sérgio Jesus

Valerie Chen

Vladimir Balayan

...

2024/3/24

Ups: Towards foundation models for pde solving via cross-modal adaptation

arXiv preprint arXiv:2403.07187

Junhong Shen

Tanya Marwah

Ameet Talwalkar

2024/3/11

Feedbacklogs: Recording and incorporating stakeholder feedback into machine learning pipelines

Matthew Barker

Emma Kallina

Dhananjay Ashok

Katherine Collins

Ashley Casovan

...

2023/10/30

On noisy evaluation in federated hyperparameter tuning

Proceedings of Machine Learning and Systems

Kevin Kuo

Pratiksha Thaker

Mikhail Khodak

John Nguyen

Daniel Jiang

...

2023/3/18

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances

arXiv preprint arXiv:2310.02246

Mikhail Khodak

Edmond Chow

Maria-Florina Balcan

Ameet Talwalkar

2023/10/3

Multitask Learning Can Improve Worst-Group Outcomes

Transactions on Machine Learning Research

Atharva Kulkarni

Lucio M Dery

Amrith Setlur

Aditi Raghunathan

Ameet Talwalkar

...

2023/12/5

Assisting Human Decisions in Document Matching

arXiv preprint arXiv:2302.08450

Joon Sik Kim

Valerie Chen

Danish Pruthi

Nihar B Shah

Ameet Talwalkar

2023/2/16

Perspectives on incorporating expert feedback into model updates

Valerie Chen

Umang Bhatt

Hoda Heidari

Adrian Weller

Ameet Talwalkar

2023/7/14

Simulating Iterative Human-AI Interaction in Programming with LLMs

Hussein Mozannar

Valerie Chen

Dennis Wei

Prasanna Sattigeri

Manish Nagireddy

...

2023/11/26

Cross-modal fine-tuning: Align then refine

Junhong Shen

Liam Li

Lucio M Dery

Corey Staten

Mikhail Khodak

...

2023/7/3

Do llms exhibit human-like response biases? a case study in survey design

arXiv preprint arXiv:2311.04076

Lindia Tjuatja

Valerie Chen

Sherry Tongshuang Wu

Ameet Talwalkar

Graham Neubig

2023/11/7

Zeno: An interactive framework for behavioral evaluation of machine learning

Ángel Alexander Cabrera

Erica Fu

Donald Bertucci

Kenneth Holstein

Ameet Talwalkar

...

2023/4/19

Where does my model underperform? a human evaluation of slice discovery algorithms

ICML 2023: The Second Workshop on Spurious Correlations, Invariance and Stability

Nari Johnson

Ángel Alexander Cabrera

Gregory Plumb

Ameet Talwalkar

2023/6/13

Learning personalized decision support policies

arXiv preprint arXiv:2304.06701

Umang Bhatt

Valerie Chen

Katherine M Collins

Parameswaran Kamalaruban

Emma Kallina

...

2023/4/13

Interpretable machine learning: Moving from mythos to diagnostics

Queue

Valerie Chen

Jeffrey Li

Joon Sik Kim

Gregory Plumb

Ameet Talwalkar

2022/1/12

Towards a More Rigorous Science of Blindspot Discovery in Image Models

Gregory Plumb

Nari Johnson

Angel Cabrera

Ameet Talwalkar

2022/9/29

Use-case-grounded simulations for explanation evaluation

Valerie Chen

Nari Johnson

Nicholay Topin

Gregory Plumb

Ameet Talwalkar

2022/6/5

Paleo: A performance model for deep neural networks

Hang Qi

Evan R Sparks

Ameet Talwalkar

2022/7/21

See List of Professors in Ameet Talwalkar University(Carnegie Mellon University)

Co-Authors

H-index: 203
Michael I. Jordan

Michael I. Jordan

University of California, Berkeley

H-index: 106
Michael Franklin

Michael Franklin

University of Chicago

H-index: 58
Tim Kraska

Tim Kraska

Massachusetts Institute of Technology

H-index: 53
Maria-Florina Balcan

Maria-Florina Balcan

Carnegie Mellon University

H-index: 28
Virginia Smith

Virginia Smith

Carnegie Mellon University

H-index: 27
Kevin Jamieson

Kevin Jamieson

University of Washington

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