Hoefler, Torsten

24 publications

ICLR 2026 Beyond Outliers: A Study of Optimizers Under Quantization Georgios Vlassis, Saleh Ashkboos, Alexandra Volkova, Torsten Hoefler, Dan Alistarh

ICLR 2026 Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization Vage Egiazarian, Roberto L. Castro, Denis Kuznedelev, Andrei Panferov, Eldar Kurtic, Shubhra Pandit, Alexandre Noll Marques, Mark Kurtz, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh

ICLR 2026 The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm Jiale Chen, Yalda Shabanzadeh, Elvir Crnčević, Torsten Hoefler, Dan Alistarh

AISTATS 2025 All Models Are Wrong, Some Are Useful: Model Selection with Limited Labels Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Gürel

NeurIPS 2025 HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs Saleh Ashkboos, Mahdi Nikdan, Soroush Tabesh, Roberto L. Castro, Torsten Hoefler, Dan Alistarh

ICML 2024 DiffDA: A Diffusion Model for Weather-Scale Data Assimilation Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter Dominik Dueben, Torsten Hoefler

AAAI 2024 Graph of Thoughts: Solving Elaborate Problems with Large Language Models Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler

CPAL 2024 How to Prune Your Language Model: Recovering Accuracy on the “Sparsity May Cry” Benchmark Eldar Kurtic, Torsten Hoefler, Dan Alistarh

NeurIPS 2024 QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman

ICLR 2024 SliceGPT: Compress Large Language Models by Deleting Rows and Columns Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, James Hensman

ICLR 2024 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression Tim Dettmers, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

ICLR 2023 Compressing Multidimensional Weather and Climate Data into Neural Networks Langwen Huang, Torsten Hoefler

ICCV 2023 Differentiable Transportation Pruning Yunqiang Li, Jan C. van Gemert, Torsten Hoefler, Bert Moons, Evangelos Eleftheriou, Bram-Ernst Verhoef

LoG 2023 HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi, Nils Blach, Piotr Nyczyk, Hubert Niewiadomski, Torsten Hoefler

ICLR 2023 OPTQ: Accurate Quantization for Generative Pre-Trained Transformers Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh

NeurIPS 2022 ENS-10: A Dataset for Post-Processing Ensemble Weather Forecasts Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler

LoG 2022 Neural Graph Databases Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler

ICLR 2022 Neural Parameter Allocation Search Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko

NeurIPS 2022 Spatial Mixture-of-Experts Nikoli Dryden, Torsten Hoefler

ICML 2021 ProGraML: A Graph-Based Program Representation for Data Flow Analysis and Compiler Optimizations Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F P O’Boyle, Hugh Leather

JMLR 2021 Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste

CVPR 2020 Augment Your Batch: Improving Generalization Through Instance Repetition Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, Daniel Soudry

NeurIPS 2018 Neural Code Comprehension: A Learnable Representation of Code Semantics Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler

NeurIPS 2018 The Convergence of Sparsified Gradient Methods Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cedric Renggli