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Adil Salim
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2020 – today
- 2024
- [j4]Lukang Sun, Adil Salim, Peter Richtárik:
Federated Sampling with Langevin Algorithm under Isoperimetry. Trans. Mach. Learn. Res. 2024 (2024) - [i16]Marah I Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat S. Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou:
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. CoRR abs/2404.14219 (2024) - [i15]Victor Priser, Pascal Bianchi, Adil Salim:
Long-time asymptotics of noisy SVGD outside the population limit. CoRR abs/2406.11929 (2024) - 2023
- [c17]Sinho Chewi, Sébastien Bubeck, Adil Salim:
On the complexity of finding stationary points of smooth functions in one dimension. ALT 2023: 358-374 - [c16]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR 2023 - [c15]Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim:
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. ICML 2023: 7960-7991 - [c14]Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim:
The probability flow ODE is provably fast. NeurIPS 2023 - [i14]Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim:
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. CoRR abs/2304.05398 (2023) - [i13]Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim:
The probability flow ODE is provably fast. CoRR abs/2305.11798 (2023) - [i12]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:
Textbooks Are All You Need. CoRR abs/2306.11644 (2023) - [i11]Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim:
Gaussian random field approximation via Stein's method with applications to wide random neural networks. CoRR abs/2306.16308 (2023) - 2022
- [j3]Adil Salim, Laurent Condat
, Konstantin Mishchenko
, Peter Richtárik
:
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. 195(1): 102-130 (2022) - [c13]Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik:
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. AISTATS 2022: 4482-4498 - [c12]Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang:
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. COLT 2022: 2896-2923 - [c11]Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono:
Improved analysis for a proximal algorithm for sampling. COLT 2022: 2984-3014 - [c10]Adil Salim, Lukang Sun, Peter Richtárik:
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. ICML 2022: 19139-19152 - [i10]Lukang Sun, Adil Salim, Peter Richtárik:
Federated Learning with a Sampling Algorithm under Isoperimetry. CoRR abs/2206.00920 (2022) - [i9]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR abs/2209.11215 (2022) - 2021
- [j2]Pascal Bianchi, Walid Hachem, Adil Salim
:
A fully stochastic primal-dual algorithm. Optim. Lett. 15(2): 701-710 (2021) - [i8]Adil Salim, Lukang Sun, Peter Richtárik
:
Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1. CoRR abs/2106.03076 (2021) - 2020
- [c9]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS 2020 - [c8]Dmitry Kovalev, Adil Salim, Peter Richtárik:
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. NeurIPS 2020 - [c7]Adil Salim, Anna Korba, Giulia Luise:
The Wasserstein Proximal Gradient Algorithm. NeurIPS 2020 - [c6]Adil Salim, Peter Richtárik:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. NeurIPS 2020 - [i7]Adil Salim, Laurent Condat
, Konstantin Mishchenko
, Peter Richtárik
:
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. CoRR abs/2004.02635 (2020) - [i6]Adil Salim, Peter Richtárik
:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. CoRR abs/2006.09270 (2020) - [i5]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR abs/2006.09797 (2020)
2010 – 2019
- 2019
- [j1]Adil Salim
, Pascal Bianchi
, Walid Hachem
:
Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs. IEEE Trans. Autom. Control. 64(5): 1832-1847 (2019) - [c5]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. NeurIPS 2019: 6481-6491 - [c4]Adil Salim, Dmitry Kovalev, Peter Richtárik:
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. NeurIPS 2019: 6649-6661 - [i4]Adil Salim, Dmitry Kovalev
, Peter Richtárik
:
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. CoRR abs/1905.11768 (2019) - [i3]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. CoRR abs/1906.04370 (2019) - [i2]Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác:
Distributed Fixed Point Methods with Compressed Iterates. CoRR abs/1912.09925 (2019) - 2018
- [c3]Adil Salim, Pascal Bianchi, Walid Hachem:
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to non Separable Regularizations. ICASSP 2018: 2886-2890 - 2017
- [c2]Rahul Mourya
, Pascal Bianchi, Adil Salim, Cédric Richard:
An adaptive distributed asynchronous algorithm with application to target localization. CAMSAP 2017: 1-5 - [i1]Adil Salim, Pascal Bianchi, Walid Hachem:
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs. CoRR abs/1712.07027 (2017) - 2016
- [c1]Adil Salim, Pascal Bianchi, Walid Hachem, Jérémie Jakubowicz:
A stochastic proximal point algorithm for total variation regularization over large scale graphs. CDC 2016: 4490-4495
Coauthor Index
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last updated on 2024-08-10 00:30 CEST by the dblp team
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