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

Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. - Elias Frantar; Eldar Kurtic; Dan Alistarh (CoRR, 2021)

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. - Eldar Kurtic; Daniel Campos; Tuan Nguyen; Elias Frantar; Mark Kurtz; Benjamin Fineran; Michael Goin; Dan Alistarh (EMNLP, 2022)

Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals. - Zlatan Ajanovic; Emina Alickovic; Aida Brankovic; Sead Delalic; Eldar Kurtic; Salem Malikic; Adnan Mehonic; Hamza Merzic; Kenan Sehic; Bahrudin Trbalic (CoRR, 2022)

GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods. - Eldar Kurtic; Dan Alistarh (CoRR, 2022)

oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. - Denis Kuznedelev; Eldar Kurtic; Elias Frantar; Dan Alistarh (CoRR, 2022)

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

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

ZipLM: Inference-Aware Structured Pruning of Language Models. - Eldar Kurtic; Elias Frantar; Dan Alistarh (NeurIPS, 2023)

CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models. - Denis Kuznedelev; Eldar Kurtic; Elias Frantar; Dan Alistarh (NeurIPS, 2023)

ZipLM: Hardware-Aware Structured Pruning of Language Models. - Eldar Kurtic; Elias Frantar; Dan Alistarh (CoRR, 2023)

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks. - Mahdi Nikdan; Tommaso Pegolotti; Eugenia Iofinova; Eldar Kurtic; Dan Alistarh (CoRR, 2023)

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 (CoRR, 2023)

Error Feedback Can Accurately Compress Preconditioners. - Ionut-Vlad Modoranu; Aleksei Kalinov; Eldar Kurtic; Dan Alistarh (CoRR, 2023)

Sparse Fine-tuning for Inference Acceleration of Large Language Models. - Eldar Kurtic; Denis Kuznedelev; Elias Frantar; Michael Goin; Dan Alistarh (CoRR, 2023)

How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark. - Eldar Kurtic; Torsten Hoefler; Dan Alistarh (CPAL, 2024)

Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models. - Eldar Kurtic; Amir Moeini; Dan Alistarh (EMNLP, 2024)

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

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. - Ionut-Vlad Modoranu; Mher Safaryan; Grigory Malinovsky; Eldar Kurtic; Thomas Robert 007; Peter Richtárik; Dan Alistarh (NeurIPS, 2024)

Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment. - Abhinav Agarwalla; Abhay Gupta; Alexandre Noll Marques; Shubhra Pandit; Michael Goin; Eldar Kurtic; Kevin Leong; Tuan Nguyen; Mahmoud Salem; Dan Alistarh; Sean Lie; Mark Kurtz (CoRR, 2024)

Panza: A Personalized Text Writing Assistant via Data Playback and Local Fine-Tuning. - Armand Nicolicioiu; Eugenia Iofinova; Eldar Kurtic; Mahdi Nikdan; Andrei Panferov; Ilia Markov; Nir Shavit; Dan Alistarh (CoRR, 2024)

EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search. - Oliver Sieberling; Denis Kuznedelev; Eldar Kurtic; Dan Alistarh (CoRR, 2024)

Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. - Denis Kuznedelev; Eldar Kurtic; Eugenia Iofinova; Elias Frantar; Alexandra Peste; Dan Alistarh (Trans. Mach. Learn. Res., 2024)

"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization. - Eldar Kurtic; Alexandre Noll Marques; Shubhra Pandit; Mark Kurtz; Dan Alistarh (ACL (1), 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 (Trans. Mach. Learn. Res., 2025)

Good Practices for Institutional Organization of Research Institutes: Excellence in Research and Positive Impact on Society. - Zlatan Ajanovic; Hamza Merzic; Suad Krilasevic; Eldar Kurtic; Bakir Kudic; Rialda Spahic; Emina Alickovic; Aida Brankovic; Kenan Sehic; Mirsad Cosovic; Admir Greljo; Sead Delalic; et al. (CoRR, 2025)

DarwinLM: Evolutionary Structured Pruning of Large Language Models. - Shengkun Tang; Oliver Sieberling; Eldar Kurtic; Zhiqiang Shen; Dan Alistarh (CoRR, 2025)