Kimad: Adaptive Gradient Compression with Bandwidth Awareness. - Jihao Xin; Ivan Ilin; Shunkang Zhang; Marco Canini; Peter Richtárik (DistributedML@CoNEXT, 2023)

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)

Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. - Alexander Tyurin; Marta Pozzi; Ivan Ilin; Peter Richtárik (NeurIPS, 2024)

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. - Vladimir Malinovskii; Andrei Panferov; Ivan Ilin; Han Guo; Peter Richtárik; Dan Alistarh (CoRR, 2024)

HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. - Vladimir Malinovskii; Andrei Panferov; Ivan Ilin; Han Guo; Peter Richtárik; Dan Alistarh (NAACL (Long Papers), 2025)

Hessian of Perplexity for Large Language Models by PyTorch autograd (Open Source). - Ivan Ilin (CoRR, 2025)

Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression. - Ivan Ilin; Peter Richtárik (CoRR, 2025)