Adil SALIM
15 papers · 2019–2025 · 6 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (26)
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Triple Crown
💎
Century Club · Bronze
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Keyword Collector
(81)
Top co-authors
Research topics
Machine Learning
(14)
Optimization & Theory · Machine Learning
(13)
Mathematics & Optimization
(9)
Optimization
(9)
Artificial Intelligence
(4)
Bayesian & Probabilistic · Artificial Intelligence
(4)
Bayesian & Probabilistic · Machine Learning
(4)
Deep Learning
(3)
Learning Types
(2)
Models
(2)
Mathematics
(1)
Optimization & Theory · Deep Learning
(1)
Keywords
wasserstein gradient flow
(3)
convergence guarantee
(3)
gradient descent
(3)
stein variational gradient descent
(3)
bayesian inference
(3)
langevin dynamics
(3)
markov chain monte carlo
(3)
probability distribution
(2)
kernel methods
(2)
log-concave distribution
(2)
log-concave sampling
(2)
kl divergence
(2)
non-convex optimization
(2)
wasserstein distance
(2)
optimal algorithm
(2)
langevin algorithm
(2)
decentralized optimization
(1)
communication complexity
(1)
optimal transport
(1)
probability metrics
(1)
Papers
Long-time asymptotics of noisy SVGD outside the population limit
ICLR 2025
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
ICML 2023
On the complexity of finding stationary points of smooth functions in one dimension
ALT 2023
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
ICLR 2023
The probability flow ODE is provably fast
NeurIPS 2023
A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1
ICML 2022
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
AISTATS 2022
Improved analysis for a proximal algorithm for sampling
COLT 2022
Towards a Theory of Non-Log-Concave Sampling:First-Order Stationarity Guarantees for Langevin Monte Carlo
COLT 2022
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
NeurIPS 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
NeurIPS 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
NeurIPS 2020
The Wasserstein Proximal Gradient Algorithm
NeurIPS 2020
Maximum Mean Discrepancy Gradient Flow
NeurIPS 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
NeurIPS 2019