Michael Kuchnik

According to our database, Michael Kuchnik authored at least 20 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

AIRA_2: Overcoming Bottlenecks in AI Research Agents.

Karen Hambardzumyan, Nicolas Mario Baldwin, Edan Toledo, Rishi Hazra, Michael Kuchnik, Bassel Al Omari, Thomas Foster, Anton Protopopov, Jean-Christophe Gagnon-Audet, Ishita Mediratta, Kelvin Niu, Michael Shvartsman, Alisia Maria Lupidi, Alexis Audran-Reiss, Parth Pathak, Tatiana Shavrina, Despoina Magka, Hela Momand, Derek Dunfield, Nicola Cancedda, Pontus Stenetorp, Carole-Jean Wu, Jakob Nicolaus Foerster, Yoram Bachrach, Martin Josifoski

CoRR, March, 2026

2025

PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training.

Alicia Golden, Michael Kuchnik, Samuel Hsia, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu

CoRR, October, 2025

Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead.

Feiyang Kang, Michael Kuchnik, Karthik Padthe, Marin Vlastelica, Ruoxi Jia, Carole-Jean Wu, Newsha Ardalani

CoRR, October, 2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench.

Edan Toledo, Karen Hambardzumyan, Martin Josifoski, Rishi Hazra, Nicolas Mario Baldwin, Alexis Audran-Reiss, Michael Kuchnik, Despoina Magka, Minqi Jiang, Alisia Maria Lupidi, Andrei Lupu, Roberta Raileanu, Kelvin Niu, Tatiana Shavrina, Jean-Christophe Gagnon-Audet, Michael Shvartsman, Shagun Sodhani, Alexander H. Miller, Abhishek Charnalia, Derek Dunfield, Carole-Jean Wu, Pontus Stenetorp, Nicola Cancedda, Jakob Nicolaus Foerster, Yoram Bachrach

CoRR, July, 2025

Revisiting Reliability in Large-Scale Machine Learning Research Clusters.

Apostolos Kokolis, Michael Kuchnik, John Hoffman, Adithya Kumar, Parth Malani, Faye Ma, Zachary DeVito, Shubho Sengupta, Kalyan Saladi, Carole-Jean Wu

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2025

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls.

Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024

A Standardized Machine-readable Dataset Documentation Format for Responsible AI.

Nitisha Jain, Mubashara Akhtar, Joan Giner-Miguelez, Rajat Shinde, Joaquin Vanschoren, Steffen Vogler, Subhra S. Goswami, Yuhan Rao, Tim Santos, Luis Oala, Michalis Karamousadakis, Manil Maskey, Pierre Marcenac, Costanza Conforti, Michael Kuchnik, Lora Aroyo, Omar Benjelloun, Elena Simperl

CoRR, 2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.

Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin N. Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren

CoRR, 2024

Croissant: A Metadata Format for ML-Ready Datasets.

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu

CoRR, 2024

Croissant: A Metadata Format for ML-Ready Datasets.

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata S. Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Croissant: A Metadata Format for ML-Ready Datasets.

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Jos van der Velde, Steffen Vogler, Carole-Jean Wu

Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, 2024

2023

Beyond Model Efficiency: Data Optimizations for Machine Learning Systems

Michael Kuchnik

PhD thesis, 2023

Validating Large Language Models with ReLM.

Michael Kuchnik, Virginia Smith, George Amvrosiadis

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

2022

Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines.

Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

2021

Progressive Compressed Records: Taking a Byte out of Deep Learning Data.

Michael Kuchnik, George Amvrosiadis, Virginia Smith

Proc. VLDB Endow., 2021

2020

File Systems Unfit as Distributed Storage Back Ends: Lessons from 10 Years of Ceph Evolution.

Abutalib Aghayev, Sage A. Weil, Michael Kuchnik, Mark Nelson, Greg Ganger, George Amvrosiadis

login Usenix Mag., 2020

The Case for Custom Storage Backends in Distributed Storage Systems.

Abutalib Aghayev, Sage A. Weil, Michael Kuchnik, Mark Nelson, Gregory R. Ganger, George Amvrosiadis

ACM Trans. Storage, 2020

2019

File systems unfit as distributed storage backends: lessons from 10 years of Ceph evolution.

Abutalib Aghayev, Sage A. Weil, Michael Kuchnik, Mark Nelson, Gregory R. Ganger, George Amvrosiadis

Proceedings of the 27th ACM Symposium on Operating Systems Principles, 2019

Efficient Augmentation via Data Subsampling.

Michael Kuchnik, Virginia Smith

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

The Atlas Cluster Trace Repository.

George Amvrosiadis, Michael Kuchnik, Jun Woo Park, Chuck Cranor, Gregory R. Ganger, Elisabeth Moore, Nathan DeBardeleben

login Usenix Mag., 2018