Kurtic, Eldar

13 publications

ICLRW 2025 Cheap and Effective Personalization of Foundation Language Models for Imitating a User's Writing Style Armand Mihai Nicolicioiu, Eugenia Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir N Shavit, Dan Alistarh

ICML 2025 EvoPress: Accurate Dynamic Model Compression via Evolutionary Search Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh

TMLR 2025 TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh

TMLR 2024 Accurate Neural Network Pruning Requires Rethinking Sparse Optimization Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh

ICML 2024 Error Feedback Can Accurately Compress Preconditioners Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh

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

NeurIPS 2024 MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh

NeurIPS 2023 CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models Denis Kuznedelev, Eldar Kurtić, Elias Frantar, Dan Alistarh

ICLR 2023 CrAM: A Compression-Aware Minimizer Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H Lampert, Dan Alistarh

ICML 2023 SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

NeurIPS 2023 ZipLM: Inference-Aware Structured Pruning of Language Models Eldar Kurtić, Elias Frantar, Dan Alistarh

ICMLW 2023 ZipLM: Inference-Aware Structured Pruning of Language Models Eldar Kurtic, Elias Frantar, Dan Alistarh

NeurIPS 2021 M-FAC: Efficient Matrix-Free Approximations of Second-Order Information Elias Frantar, Eldar Kurtic, Dan Alistarh