Project Details
Project Description
Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into individual layer-wise sub-problems, and minimizing per-layer error, measured via various metrics.
Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, and Peter Richtárik
PV-Tuning: Beyond straight-through estimation for extreme LLM compression
Advances in Neural Information Processing Systems 38 (NeurIPS 2024)
Oral at NeurIPS 2024 (0.4\% acceptance rate)
Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, and Dan Alistarh
Pushing the limits of large language model quantization via the linearity theorem
arXiv:2411.17525
2024
About the Researcher
Affiliations
Education Profile
- PhD, Operations Research, Cornell University, 2007
- MS, Operations Research, Cornell University, 2006
- Mgr, Mathematics, Comenius University, 2001
- Bc, Management Comenius University, 2001
- Bc, Mathematics, Comenius University, 2000
Research Interests
Prof. Richtarik's research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high performance computing and applied probability. He is interested in developing zero, first, and second-order algorithms for convex and nonconvex optimization problems described by big data, with a particular focus on randomized, parallel and distributed methods. He is the co-inventor of federated learning, a Google platform for machine learning on mobile devices preserving privacy of users' data.Selected Publications
- R. M. Gower, D. Goldfarb and P. Richtarik. Stochastic block BFGS: squeezing more curvature out of data, Proceedings of The 33rd International Conference on Machine Learning, pp. 1869-1878, 2016
- J. Konecny, J. Liu, P. Richtarik and M. Takac. Mini-batch semi-stochastic gradient descent in the proximal setting, IEEE Journal of Selected Topics in Signal Processing 10(2), 242a-255, 2016
- P. Richtarik and M. Takac. Parallel coordinate descent methods for big data optimization Mathematical Programming 156(1):433a-484, 2016
- R. M. Gower and P. Richtarik. Randomized iterative methods for linear systems, SIAM Journal on Matrix Analysis and Applications 36(4):1660-1690, 2015
- O. Fercoq and P. Richtarik. Accelerated, parallel and proximal coordinate descent. SIAM Journal on Optimization 25(4):1997a-2023, 2015
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