← Authors

Alexander Gasnikov

46 papers · 2016–2026 · 8 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (8) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (9) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (36) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (22) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ”¬ Deep Specialist (27) 🧬 Topic Evolution ⚑ Prolific Year (9) ❓ The Questioner πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (45) πŸ—ƒοΈ Keyword Collector (144)

Conferences

NIPS (22) ICML (11) AISTATS (5) ICLR (3) COLT (2) AAAI (1) EMNLP (1) UAI (1)

Top co-authors

Dmitry Kovalev (12) Aleksandr Beznosikov (11) Eduard Gorbunov (10) Pavel Dvurechensky (9) Martin Takac (8) Peter Richtarik (8) Gesualdo Scutari (5) Dmitry Kamzolov (5) Samuel HorvΓ‘th (4) Pavel Dvurechenskii (4)

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 Nesterov’s 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 Sinkhorn’s Algorithm ICML 2018
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NIPS 2016