I am a Senior Researcher in the Machine Learning Foundations group at Microsoft Research (Redmond, USA). Previously, I was a Google Research Fellow at the Simons Institute, UC Berkeley, USA.
I did my Ph.D at Telecom Paris and Paris–Saclay University, France, under the supervision of Pascal Bianchi and Walid Hachem. Then, I did a postdoc at KAUST, Saudi Arabia, hosted by Peter Richtárik.
I use optimization, statistics, optimal transport, convex analysis etc. to study machine learning algorithms. I received the Masters degrees in 2015 from ENSAE Paris, where I studied statistics, and from Paris–Saclay University, where I studied probability theory. Here is my CV.
News
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Anna Korba and I have presented a tutorial on Sampling as Optimization at ICML 2022. Here are our slides. If you have an ICML account, you can watch the video here. If you don’t, the video will be on Youtube soon.
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Sinho Chewi is writing a book on Sampling.
Selected papers
Sampling and optimal transport
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Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono, “Improved analysis for a proximal algorithm for sampling”, COLT 2022.
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Adil Salim, Lukang Sun, Peter Richtárik, “A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1”, ICML 2022.
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Anna Korba, Adil Salim, Michael Arbel, Giulia Luise and Arthur Gretton, “A Non-Asymptotic Analysis for Stein Variational Gradient Descent”, NeurIPS 2020.
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Adil Salim and Peter Richtárik, “Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm”, NeurIPS 2020.
Optimization and monotone operators
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Adil Salim, Laurent Condat, Dmitry Kovalev and Peter Richtárik, “An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints”, AISTATS 2022.
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Dmitry Kovalev, Adil Salim and Peter Richtárik, “Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization”, NeurIPS 2020.