← Authors

Mehrdad Mahdavi

35 papers · 2012–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (5) 🐝 Cross-Pollinator (7) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (13) 🌟 Keyword Trendsetter Combo (3) 🀝 Dynamic Duo (10) πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion (3) ❓ The Questioner πŸ—ƒοΈ Keyword Collector (123) πŸ“ˆ Trend Setter πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (7) ⚑ Prolific Year (5)

Conferences

NIPS (17) AISTATS (7) COLT (4) ICLR (3) ICML (3) JMLR (1)

Top co-authors

Rong Jin (10) Yuyang Deng (9) Mohammad Mahdi Kamani (7) Tianbao Yang (6) Weilin Cong (5) Farzin Haddadpour (4) Lijun Zhang (4) Shenghuo Zhu (3) Morteza Ramezani (3) Fuli Qiao (2)

Keywords

stochastic gradient descent (5) communication efficiency (4) convex optimization (4) federated learning (4) convergence analysis (3) empirical risk minimization (3) distributed optimization (3) minimax optimization (3) optimization algorithm (3) distributed learning (3) low-rank matrix (2) online convex optimization (2) non-convex optimization (2) strong convexity (2) stochastic optimization (2) strongly convex (2) stochastic gradient (2) map inference (2) knowledge transfer (2) gradient descent (2)

Papers

Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees AISTATS 2025
Learn more, but bother less: parameter efficient continual learning NIPS 2024
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions ICML 2024
On the Generalization Ability of Unsupervised Pretraining AISTATS 2024
Do We Really Need Complicated Model Architectures For Temporal Networks? ICLR 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions NIPS 2023
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation NIPS 2023
Distributed Personalized Empirical Risk Minimization NIPS 2023
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection AISTATS 2023
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time AISTATS 2022
Learning Distributionally Robust Models at Scale via Composite Optimization ICLR 2022
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks ICLR 2022
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems NIPS 2022
Federated Learning with Compression: Unified Analysis and Sharp Guarantees AISTATS 2021
Meta-learning with an Adaptive Task Scheduler NIPS 2021
On Provable Benefits of Depth in Training Graph Convolutional Networks NIPS 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency AISTATS 2021
Distributionally Robust Federated Averaging NIPS 2020
Online Structured Meta-learning NIPS 2020
GCN meets GPU: Decoupling β€œWhen to Sample” from β€œHow to Sample” NIPS 2020
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization NIPS 2019
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization ICML 2019
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data AISTATS 2017
Train and Test Tightness of LP Relaxations in Structured Prediction ICML 2016
Smooth and Strong: MAP Inference with Linear Convergence NIPS 2015
Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization COLT 2015
Mixed Optimization for Smooth Functions NIPS 2013
Linear Convergence with Condition Number Independent Access of Full Gradients NIPS 2013
Stochastic Convex Optimization with Multiple Objectives NIPS 2013
Recovering the Optimal Solution by Dual Random Projection COLT 2013
Passive Learning with Target Risk COLT 2013
Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints JMLR 2012
Stochastic Gradient Descent with Only One Projection NIPS 2012
Online Optimization with Gradual Variations COLT 2012
NystrΓΆm Method vs Random Fourier Features: A Theoretical and Empirical Comparison NIPS 2012