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Ido Hakimi
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Publications
- 2026
- [c11]Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert i Llaquet, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Durech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolcec, Yixuan Even Xu, Michael Aerni, Badr AlKhamissi, Ines Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu
, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag:
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments. ACL (1) 2026: 46877-46955 - [i14]Jonas Hübotter, Frederike Lübeck, Lejs Behric, Anton Baumann, Marco Bagatella
Reinforcement Learning via Self-Distillation. CoRR abs/2601.20802 (2026) - [i13]Daniel Yang, Samuel Stante, Florian Redhardt, Lena Libon, Parnian Kassraie, Ido Hakimi, Barna Pásztor, Andreas Krause:
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models. CoRR abs/2602.24040 (2026) - [i12]Davit Melikidze, Marian Schneider, Jessica Lam, Martin Wertich, Ido Hakimi, Barna Pásztor, Andreas Krause:
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning. CoRR abs/2603.09692 (2026) - [i11]Tim Launer, Jonas Hübotter, Marco Bagatella, Ido Hakimi, Andreas Krause:
Majority Voting for Code Generation. CoRR abs/2604.15618 (2026) - 2025
- [c10]Jakub Macina, Nico Daheim, Ido Hakimi, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan:
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors. EMNLP 2025: 204-221 - [c9]David Dinucu-Jianu, Jakub Macina, Nico Daheim, Ido Hakimi, Iryna Gurevych, Mrinmaya Sachan:
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning. EMNLP 2025: 272-292 - [c8]Jonas Hübotter, Sascha Bongni, Ido Hakimi, Andreas Krause:
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs. ICLR 2025 - [i10]Jakub Macina, Nico Daheim, Ido Hakimi, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan:
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors. CoRR abs/2502.18940 (2025) - [i9]Ryo Bertolissi, Jonas Hübotter, Ido Hakimi, Andreas Krause:
Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging. CoRR abs/2505.14136 (2025) - [i8]David Dinucu-Jianu, Jakub Macina, Nico Daheim, Ido Hakimi, Iryna Gurevych, Mrinmaya Sachan:
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning. CoRR abs/2505.15607 (2025) - [i7]Matthias Otth, Jonas Hübotter, Ido Hakimi, Andreas Krause:
Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning. CoRR abs/2507.18122 (2025) - [i6]Jonas Hübotter, Leander Diaz-Bone, Ido Hakimi, Andreas Krause, Moritz Hardt:
Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning. CoRR abs/2510.04786 (2025) - 2024
- [i5]Jonas Hübotter, Sascha Bongni, Ido Hakimi, Andreas Krause:
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs. CoRR abs/2410.08020 (2024) - 2022
- [c6]Refael Cohen, Ido Hakimi, Assaf Schuster:
SMEGA2: Distributed Asynchronous Deep Neural Network Training With a Single Momentum Buffer. ICPP 2022: 3:1-3:10 - 2021
- [c5]Ido Hakimi, Rotem Zamir Aviv, Kfir Y. Levy
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. ICDM 2021: 171-180 - [c4]Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy:
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. ICML 2021: 436-445 - [c3]Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein:
Faster Neural Network Training with Approximate Tensor Operations. NeurIPS 2021: 27877-27889 - [c2]Saar Eliad, Ido Hakimi, Alon De Jagger, Mark Silberstein, Assaf Schuster:
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism. USENIX ATC 2021: 381-396 - [i3]Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Y. Levy:
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays. CoRR abs/2106.12261 (2021) - 2020
- [c1]Saar Barkai, Ido Hakimi, Assaf Schuster:
Gap-Aware Mitigation of Gradient Staleness. ICLR 2020 - 2019
- [i2]Ido Hakimi, Saar Barkai, Moshe Gabel, Assaf Schuster:
Taming Momentum in a Distributed Asynchronous Environment. CoRR abs/1907.11612 (2019) - [i1]Saar Barkai, Ido Hakimi, Assaf Schuster:
Gap Aware Mitigation of Gradient Staleness. CoRR abs/1909.10802 (2019)
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last updated on 2026-08-04 23:15 CEST by the dblp team
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