Choshen, Leshem

19 publications

ICML 2025 A Hitchhiker’s Guide to Scaling Law Estimation Leshem Choshen, Yang Zhang, Jacob Andreas

TMLR 2025 A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning Prateek Yadav, Colin Raffel, Mohammed Muqeeth, Lucas Caccia, Haokun Liu, Tianlong Chen, Mohit Bansal, Leshem Choshen, Alessandro Sordoni

TMLR 2025 ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization Prateek Yadav, Leshem Choshen, Colin Raffel, Mohit Bansal

ICML 2025 Compress Then Serve: Serving Thousands of LoRA Adapters with Little Overhead Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

ICLR 2025 LiveXiv - A Multi-Modal Live Benchmark Based on arXiv Papers Content Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh, Wei Lin, Muhammad Jehanzeb Mirza, Leshem Choshen, Mikhail Yurochkin, Yuekai Sun, Assaf Arbelle, Leonid Karlinsky, Raja Giryes

ICLR 2025 Model Merging with SVD to Tie the Knots George Stoica, Pratik Ramesh, Boglarka Ecsedi, Leshem Choshen, Judy Hoffman

NeurIPS 2025 Sloth: Scaling Laws for LLM Skills to Predict Multi-Benchmark Performance Across Families Felipe Maia Polo, Seamus Somerstep, Leshem Choshen, Yuekai Sun, Mikhail Yurochkin

ICLR 2024 Achieving Human Parity in Content-Grounded Datasets Generation Asaf Yehudai, Boaz Carmeli, Yosi Mass, Ofir Arviv, Nathaniel Mills, Eyal Shnarch, Leshem Choshen

ICML 2024 Asymmetry in Low-Rank Adapters of Foundation Models Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez De Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon

ICLRW 2024 Asymmetry in Low-Rank Adapters of Foundation Models Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon

ICMLW 2024 Compress Then Serve: Serving Thousands of LoRA Adapters with Little Overhead Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

NeurIPS 2024 Efficient Multi-Prompt Evaluation of LLMs Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin

ICMLW 2024 Efficient Multi-Prompt Evaluation of LLMs Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin

ICML 2024 tinyBenchmarks: Evaluating LLMs with Fewer Examples Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin

NeurIPS 2023 TIES-Merging: Resolving Interference When Merging Models Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, Mohit Bansal

AAAI 2020 Corpus Wide Argument Mining - A Working Solution Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim

ICML 2020 Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets Guy Hacohen, Leshem Choshen, Daphna Weinshall

ICLR 2020 On the Weaknesses of Reinforcement Learning for Neural Machine Translation Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend

ICLR 2018 DORA the Explorer: Directed Outreaching Reinforcement Action-Selection Lior Fox, Leshem Choshen, Yonatan Loewenstein