Adil Salim

According to our database, Adil Salim authored at least 27 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Gaussian random field approximation via Stein's method with applications to wide random neural networks.

Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim

CoRR, 2023

Textbooks Are All You Need.

Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li

CoRR, 2023

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space.

Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim

CoRR, 2023

The probability flow ODE is provably fast.

Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space.

Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim

Proceedings of the International Conference on Machine Learning, 2023

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On the complexity of finding stationary points of smooth functions in one dimension.

Sinho Chewi, Sébastien Bubeck, Adil Salim

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms.

Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik

J. Optim. Theory Appl., 2022

Federated Learning with a Sampling Algorithm under Isoperimetry.

Lukang Sun, Adil Salim, Peter Richtárik

CoRR, 2022

A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1.

Adil Salim, Lukang Sun, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2022

Improved analysis for a proximal algorithm for sampling.

Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 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

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints.

Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A fully stochastic primal-dual algorithm.

Pascal Bianchi, Walid Hachem, Adil Salim

Optim. Lett., 2021

Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1.

Adil Salim, Lukang Sun, Peter Richtárik

CoRR, 2021

2020

Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms.

Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik

CoRR, 2020

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm.

Adil Salim, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Wasserstein Proximal Gradient Algorithm.

Adil Salim, Anna Korba, Giulia Luise

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization.

Dmitry Kovalev, Adil Salim, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Non-Asymptotic Analysis for Stein Variational Gradient Descent.

Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs.

Adil Salim, Pascal Bianchi, Walid Hachem

IEEE Trans. Autom. Control., 2019

Distributed Fixed Point Methods with Compressed Iterates.

Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác

CoRR, 2019

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates.

Adil Salim, Dmitry Kovalev, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Maximum Mean Discrepancy Gradient Flow.

Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

A Constant Step Stochastic Douglas-Rachford Algorithm with Application to non Separable Regularizations.

Adil Salim, Pascal Bianchi, Walid Hachem

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

An adaptive distributed asynchronous algorithm with application to target localization.

Rahul Mourya, Pascal Bianchi, Adil Salim, Cédric Richard

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

2016

A stochastic proximal point algorithm for total variation regularization over large scale graphs.

Adil Salim, Pascal Bianchi, Walid Hachem, Jérémie Jakubowicz

Proceedings of the 55th IEEE Conference on Decision and Control, 2016