A stochastic proximal point algorithm for total variation regularization over large scale graphs. - Adil Salim; Pascal Bianchi; Walid Hachem; Jérémie Jakubowicz (CDC, 2016)

An adaptive distributed asynchronous algorithm with application to target localization. - Rahul Mourya; Pascal Bianchi; Adil Salim; Cédric Richard (CAMSAP, 2017)

A Constant Step Stochastic Douglas-Rachford Algorithm with Application to non Separable Regularizations. - Adil Salim; Pascal Bianchi; Walid Hachem (ICASSP, 2018)

Maximum Mean Discrepancy Gradient Flow. - Michael Arbel; Anna Korba; Adil Salim; Arthur Gretton (NeurIPS, 2019)

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. - Adil Salim; Dmitry Kovalev; Peter Richtárik (NeurIPS, 2019)

Distributed Fixed Point Methods with Compressed Iterates. - Sélim Chraibi; Ahmed Khaled 001; Dmitry Kovalev; Peter Richtárik; Adil Salim; Martin Takác 001 (CoRR, 2019)

Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs. - Adil Salim; Pascal Bianchi; Walid Hachem (IEEE Trans. Autom. Control., 2019)

A Non-Asymptotic Analysis for Stein Variational Gradient Descent. - Anna Korba; Adil Salim; Michael Arbel; Giulia Luise; Arthur Gretton (NeurIPS, 2020)

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. - Dmitry Kovalev; Adil Salim; Peter Richtárik (NeurIPS, 2020)

The Wasserstein Proximal Gradient Algorithm. - Adil Salim; Anna Korba; Giulia Luise (NeurIPS, 2020)

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. - Adil Salim; Peter Richtárik (NeurIPS, 2020)

Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. - Adil Salim; Laurent Condat; Konstantin Mishchenko; Peter Richtárik (CoRR, 2020)

Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1. - Adil Salim; Lukang Sun; Peter Richtárik (CoRR, 2021)

A fully stochastic primal-dual algorithm. - Pascal Bianchi; Walid Hachem; Adil Salim (Optim. Lett., 2021)

An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. - Adil Salim; Laurent Condat; Dmitry Kovalev; Peter Richtárik (AISTATS, 2022)

Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. - Krishna Balasubramanian; Sinho Chewi; Murat A. Erdogdu; Adil Salim; Shunshi Zhang 001 (COLT, 2022)

Improved analysis for a proximal algorithm for sampling. - Yongxin Chen; Sinho Chewi; Adil Salim; Andre Wibisono (COLT, 2022)

A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. - Adil Salim; Lukang Sun; Peter Richtárik (ICML, 2022)

Federated Learning with a Sampling Algorithm under Isoperimetry. - Lukang Sun; Adil Salim; Peter Richtárik (CoRR, 2022)

Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. - Adil Salim; Laurent Condat; Konstantin Mishchenko; Peter Richtárik (J. Optim. Theory Appl., 2022)

On the complexity of finding stationary points of smooth functions in one dimension. - Sinho Chewi; Sébastien Bubeck; Adil Salim (ALT, 2023)

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. - Sitan Chen; Sinho Chewi; Jerry Li 001; Yuanzhi Li; Adil Salim; Anru Zhang (ICLR, 2023)

Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. - Michael Ziyang Diao; Krishna Balasubramanian; Sinho Chewi; Adil Salim (ICML, 2023)

The probability flow ODE is provably fast. - Sitan Chen; Sinho Chewi; Holden Lee; Yuanzhi Li; Jianfeng Lu 001; Adil Salim (NeurIPS, 2023)

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. - Michael Diao; Krishnakumar Balasubramanian 002; Sinho Chewi; Adil Salim (CoRR, 2023)

Textbooks Are All You Need. - Suriya Gunasekar; Yi Zhang 001; Jyoti Aneja; Caio César Teodoro Mendes; Allie Del Giorno; Sivakanth Gopi; Mojan Javaheripi; Piero Kauffmann; Gustavo de Rosa; Olli Saarikivi; Adil Salim; Shital Shah; et al. (CoRR, 2023)

Gaussian random field approximation via Stein's method with applications to wide random neural networks. - Krishnakumar Balasubramanian 002; Larry Goldstein; Nathan Ross; Adil Salim (CoRR, 2023)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)

Phi-4 Technical Report. - Marah I Abdin; Jyoti Aneja; Harkirat S. Behl; Sébastien Bubeck; Ronen Eldan; Suriya Gunasekar; Michael Harrison; Russell J. Hewett; Mojan Javaheripi; Piero Kauffmann; James R. Lee; Yin Tat Lee; et al. (CoRR, 2024)

Federated Sampling with Langevin Algorithm under Isoperimetry. - Lukang Sun; Adil Salim; Peter Richtárik (Trans. Mach. Learn. Res., 2024)

Long-time asymptotics of noisy SVGD outside the population limit. - Victor Priser; Pascal Bianchi; Adil Salim (ICLR, 2025)