← Authors

Javad Lavaei

28 papers · 2018–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (7) 🐝 Cross-Pollinator (7) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (35) πŸ† Grand Slam 🧬 Topic Evolution 🀝 Dynamic Duo (11) πŸ† Keyword Champion (7) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (16) πŸ’Ž Century Club (28) ⚑ Prolific Year (12) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (117) ❓ The Questioner

Conferences

NIPS (9) AAAI (7) AISTATS (5) ICML (2) JMLR (2) L4DC (2) ICLR (1)

Top co-authors

Somayeh Sojoudi (11) Yuhao Ding (9) Ming Jin (6) Haixiang Zhang (5) Ziye Ma (5) Donghao Ying (4) Yingjie Bi (4) Igor Molybog (3) Baturalp Yalçın (2) Ying Chen (2)

Keywords

restricted isometry property (7) matrix completion (5) matrix sensing (4) non-convex optimization (4) gradient descent (4) policy optimization (3) constrained markov decision process (3) strict saddle property (3) low-rank matrix recovery (3) nonconvex optimization (3) burer-monteiro factorization (3) primal-dual optimization (3) low-rank optimization (3) dynamic regret (3) spurious local minima (3) low-rank matrix (3) global convergence (2) policy gradient (2) matrix recovery (2) safe reinforcement learning (2)

Papers

A Dynamic Penalization Framework for Online Rank-1 Semidefinite Programming Relaxations L4DC 2025
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses AISTATS 2024
Pausing Policy Learning in Non-stationary Reinforcement Learning ICML 2024
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities NIPS 2023
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing NIPS 2023
Geometric Analysis of Matrix Sensing over Graphs NIPS 2023
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing AAAI 2023
Tempo Adaptation in Non-stationary Reinforcement Learning NIPS 2023
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand NIPS 2023
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints AAAI 2023
Non-stationary Risk-Sensitive Reinforcement Learning: Near-Optimal Dynamic Regret, Adaptive Detection, and Separation Design AAAI 2023
Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes AAAI 2023
A CMDP-within-online framework for Meta-Safe Reinforcement Learning ICLR 2023
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points ICML 2023
Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold L4DC 2023
Sharp Restricted Isometry Property Bounds for Low-Rank Matrix Recovery Problems with Corrupted Measurements AAAI 2022
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods AISTATS 2022
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization AISTATS 2022
On the Global Optimum Convergence of Momentum-based Policy Gradient AISTATS 2022
Local and Global Linear Convergence of General Low-Rank Matrix Recovery Problems AAAI 2022
Stochastic $L^\natural$-convex Function Minimization NIPS 2021
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds NIPS 2021
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems AISTATS 2021
Conic Optimization for Quadratic Regression Under Sparse Noise JMLR 2020
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery JMLR 2019
On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem AAAI 2019
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? NIPS 2018
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization NIPS 2018