Alexander Gasnikov
46 papers · 2016–2026 · 8 conferences · across top CS/AI conferences
Achievements
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(27)
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Conferences
NIPS (22)
ICML (11)
AISTATS (5)
ICLR (3)
COLT (2)
AAAI (1)
EMNLP (1)
UAI (1)
Top co-authors
Keywords
convex optimization
(17)
stochastic optimization
(12)
communication complexity
(9)
distributed learning
(9)
decentralized optimization
(8)
variational inequality
(7)
distributed optimization
(6)
non-convex optimization
(5)
network optimization
(4)
stochastic gradient
(4)
optimal transport
(4)
complexity bound
(4)
oracle complexity
(3)
convergence rate
(3)
optimal algorithm
(3)
variance reduction
(3)
heavy-tailed noise
(3)
linear convergence
(3)
accelerated gradient
(3)
time-varying network
(3)
Papers
Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
AAAI 2026
An Optimal Algorithm for Strongly Convex Min-Min Optimization
UAI 2025
Synthetic Proofs with Tool-Integrated Reasoning: Contrastive Alignment for LLM Mathematics with Lean
EMNLP 2025
Decentralized Optimization with Coupled Constraints
ICLR 2025
OPTAMI: Global Superlinear Convergence of High-order Methods
ICLR 2025
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
ICML 2025
On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms
ICML 2025
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
NIPS 2024
Optimal Flow Matching: Learning Straight Trajectories in Just One Step
NIPS 2024
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
AISTATS 2024
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
AISTATS 2024
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
NIPS 2024
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
NIPS 2024
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
ICML 2024
Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness
ICLR 2024
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
NIPS 2024
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
ICML 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
NIPS 2023
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
NIPS 2023
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
NIPS 2023
Algorithm for Constrained Markov Decision Process with Linear Convergence
AISTATS 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
ICML 2023
Primal-Dual Stochastic Mirror Descent for MDPs
AISTATS 2022
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
NIPS 2022
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
NIPS 2022
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
NIPS 2022
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate
NIPS 2022
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
NIPS 2022
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
NIPS 2022
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
NIPS 2022
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
NIPS 2022
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
NIPS 2022
Acceleration in Distributed Optimization under Similarity
AISTATS 2022
The power of first-order smooth optimization for black-box non-smooth problems
ICML 2022
On a Combination of Alternating Minimization and Nesterovโs Momentum
ICML 2021
Distributed Saddle-Point Problems Under Data Similarity
NIPS 2021
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
NIPS 2021
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
ICML 2021
Newton Method over Networks is Fast up to the Statistical Precision
ICML 2021
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
NIPS 2020
On the Complexity of Approximating Wasserstein Barycenters
ICML 2019
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives
COLT 2019
Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization
COLT 2019
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
NIPS 2018
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhornโs Algorithm
ICML 2018
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
NIPS 2016