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)

Sequoia: Scalable and Robust Speculative Decoding. - Zhuoming Chen; Avner May; Ruslan Svirschevski; Yuhsun Huang; Max Ryabinin; Zhihao Jia; Beidi Chen (NeurIPS, 2024)

SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices. - Ruslan Svirschevski; Avner May; Zhuoming Chen; Beidi Chen; Zhihao Jia; Max Ryabinin (NeurIPS, 2024)

Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding. - Zhuoming Chen; Avner May; Ruslan Svirschevski; Yuhsun Huang; Max Ryabinin; Zhihao Jia; Beidi Chen (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)

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)

AutoJudge: Judge Decoding Without Manual Annotation. - Roman Garipov; Fedor Velikonivtsev; Ruslan Svirschevski; Vage Egiazarian; Max Ryabinin (CoRR, 2025)