Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (CoRR, 2022)

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

A critical look at the evaluation of GNNs under heterophily: Are we really making progress? - Oleg Platonov; Denis Kuznedelev; Michael Diskin; Artem Babenko; Liudmila Prokhorenkova (ICLR, 2023)

A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. - Maksim Velikanov; Denis Kuznedelev; Dmitry Yarotsky (ICLR, 2023)

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. - Gleb Bazhenov; Denis Kuznedelev; Andrey Malinin; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

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

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 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)

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

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

Extreme Compression of Large Language Models via Additive Quantization. - Vage Egiazarian; Andrei Panferov; Denis Kuznedelev; Elias Frantar; Artem Babenko; Dan Alistarh (ICML, 2024)

PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. - Vladimir Malinovskii; Denis Mazur; Ivan Ilin; Denis Kuznedelev; Konstantin Burlachenko; Kai Yi; Dan Alistarh; Peter Richtárik (NeurIPS, 2024)

The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. - Diyuan Wu; Ionut-Vlad Modoranu; Mher Safaryan; Denis Kuznedelev; Dan Alistarh (NeurIPS, 2024)

YaART: Yet Another ART Rendering Technology. - Sergey Kastryulin; Artem Konev; Alexander Shishenya; Eugene Lyapustin; Artem Khurshudov; Alexander Tselousov; Nikita Vinokurov; Denis Kuznedelev; Alexander Markovich; Grigoriy Livshits; Alexey Kirillov; Anastasiia Tabisheva; et al. (CoRR, 2024)

Does Diffusion Beat GAN in Image Super Resolution? - Denis Kuznedelev; Valerii Startsev; Daniil Shlenskii; Sergey Kastryulin (CoRR, 2024)

Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization. - Vage Egiazarian; Denis Kuznedelev; Anton Voronov; Ruslan Svirschevski; Michael Goin; Daniil Pavlov; Dan Alistarh; Dmitry Baranchuk (CoRR, 2024)

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

Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis. - Anton Voronov; Denis Kuznedelev; Mikhail Khoroshikh; Valentin Khrulkov; Dmitry Baranchuk (CoRR, 2024)

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training. - Philip Zmushko; Marat Mansurov; Ruslan Svirschevski; Denis Kuznedelev; Max Ryabinin; Aleksandr Beznosikov (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)

YaART: Yet Another ART Rendering Technology. - Sergey Kastryulin; Artem Konev; Alexander Shishenya; Eugene Lyapustin; Artem Khurshudov; Alexander Tselousov; Nikita Vinokurov; Denis Kuznedelev; Alexander Markovich; Grigoriy Livshits; Alexey Kirillov; Anastasiia Tabisheva; et al. (KDD (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)

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models. - Alina Shutova; Vladimir Malinovskii; Vage Egiazarian; Denis Kuznedelev; Denis Mazur; Nikita Surkov; Ivan Ermakov; Dan Alistarh (CoRR, 2025)

Scale-wise Distillation of Diffusion Models. - Nikita Starodubcev; Denis Kuznedelev; Artem Babenko; Dmitry Baranchuk (CoRR, 2025)

Hogwild! Inference: Parallel LLM Generation via Concurrent Attention. - Gleb Rodionov; Roman Garipov; Alina Shutova; George Yakushev; Vage Egiazarian; Anton Sinitsin; Denis Kuznedelev; Dan Alistarh (CoRR, 2025)