Adil Salim

39 papers A* 13A 1B 1Misc 1Journal 21Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
High-accuracy and dimension-free sampling with diffusions.
CoRR
Khashayar Gatmiry, Sitan Chen, Adil Salim
2025 A* conf
Long-time asymptotics of noisy SVGD outside the population limit.
ICLR
Victor Priser, Pascal Bianchi, Adil Salim
2024 J jnl
Federated Sampling with Langevin Algorithm under Isoperimetry.
Trans. Mach. Learn. Res.
Lukang Sun, Adil Salim, Peter Richtárik
2024 J jnl
Long-time asymptotics of noisy SVGD outside the population limit.
CoRR
Victor Priser, Pascal Bianchi, Adil Salim
2024 J jnl
Phi-4 Technical Report.
CoRR
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, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, Yi Zhang
2023 A* conf
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space.
ICML
Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim
2023 J jnl
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space.
CoRR
Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim
2023 J jnl
Gaussian random field approximation via Stein's method with applications to wide random neural networks.
CoRR
Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim
2023 B conf
On the complexity of finding stationary points of smooth functions in one dimension.
ALT
Sinho Chewi, Sébastien Bubeck, Adil Salim
2023 A* conf
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.
ICLR
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang
2023 J jnl
Textbooks Are All You Need.
CoRR
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
2023 A* conf
The probability flow ODE is provably fast.
NeurIPS
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
2023 J jnl
The probability flow ODE is provably fast.
CoRR
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
2022 A* conf
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1.
ICML
Adil Salim, Lukang Sun, Peter Richtárik
2022 A conf
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints.
AISTATS
Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik
2022 J jnl
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms.
J. Optim. Theory Appl.
Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik
2022 J jnl
Federated Learning with a Sampling Algorithm under Isoperimetry.
CoRR
Lukang Sun, Adil Salim, Peter Richtárik
2022 A* conf
Improved analysis for a proximal algorithm for sampling.
COLT
Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono
2022 J jnl
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.
CoRR
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
2022 A* conf
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo.
COLT
Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang
2021 J jnl
A fully stochastic primal-dual algorithm.
Optim. Lett.
Pascal Bianchi, Walid Hachem, Adil Salim
2021 J jnl
Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1.
CoRR
Adil Salim, Lukang Sun, Peter Richtárik
2020 A* conf
A Non-Asymptotic Analysis for Stein Variational Gradient Descent.
NeurIPS
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020 J jnl
A Non-Asymptotic Analysis for Stein Variational Gradient Descent.
CoRR
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020 J jnl
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms.
CoRR
Adil Salim, Laurent Condat, Konstantin Mishchenko, Peter Richtárik
2020 A* conf
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization.
NeurIPS
Dmitry Kovalev, Adil Salim, Peter Richtárik
2020 A* conf
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm.
NeurIPS
Adil Salim, Peter Richtárik
2020 J jnl
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm.
CoRR
Adil Salim, Peter Richtárik
2020 A* conf
The Wasserstein Proximal Gradient Algorithm.
NeurIPS
Adil Salim, Anna Korba, Giulia Luise
2019 J jnl
Distributed Fixed Point Methods with Compressed Iterates.
CoRR
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác
2019 A* conf
Maximum Mean Discrepancy Gradient Flow.
NeurIPS
Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2019 J jnl
Maximum Mean Discrepancy Gradient Flow.
CoRR
Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2019 J jnl
Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs.
IEEE Trans. Autom. Control.
Adil Salim, Pascal Bianchi, Walid Hachem
2019 A* conf
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates.
NeurIPS
Adil Salim, Dmitry Kovalev, Peter Richtárik
2019 J jnl
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates.
CoRR
Adil Salim, Dmitry Kovalev, Peter Richtárik
2018 Misc conf
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to non Separable Regularizations.
ICASSP
Adil Salim, Pascal Bianchi, Walid Hachem
2017 — conf
An adaptive distributed asynchronous algorithm with application to target localization.
CAMSAP
Rahul Mourya, Pascal Bianchi, Adil Salim, Cédric Richard
2017 J jnl
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs.
CoRR
Adil Salim, Pascal Bianchi, Walid Hachem
2016 — conf
A stochastic proximal point algorithm for total variation regularization over large scale graphs.
CDC
Adil Salim, Pascal Bianchi, Walid Hachem, Jérémie Jakubowicz