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Quoc Tran Dinh

10 papers · 2013–2024 · 4 venues · across top CS/AI venues

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Achievements

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+5 more ↓ 🐣 Hot Topic Early Bird 🌍 Conference Polyglot Β· Bronze πŸƒ Academic Marathon Β· Half πŸŒ‰ Interdisciplinary Bridge Β· Bronze πŸ—ΊοΈ Taxonomy Completionist (16)
πŸ—ƒοΈ Keyword Collector (54) πŸ“ˆ Trend Setter πŸ’Ž Century Club Β· Bronze πŸ”₯ Unstoppable Β· Bronze πŸš€ Conference Pioneer

Venues

NeurIPS (7) AISTATS (1) ICLR (1) ICML (1)

Top co-authors

Volkan Cevher (5) Lam Nguyen (2) Quoc Tran-Dinh (2) Anastasios Kyrillidis (2) Frank Hutter (1) Moritz Diehl (1) Olivier Fercoq (1) Luca Baldassarre (1) Deyi Liu (1) Rouzbeh Hasheminezhad (1)

Research topics

Mathematics & Optimization (10) Optimization (10) Machine Learning (5) Optimization & Theory Β· Machine Learning (5) Deep Learning (3) Optimization & Theory Β· Deep Learning (3) Artificial Intelligence (1) Application Areas (1) Core Methods (1) Learning Paradigms (1) Learning Types (1)

Keywords

convex optimization (5) non-convex optimization (4) convergence rate (3) primal-dual method (3) coordinate descent (2) minimax optimization (2) stochastic gradient descent (2) oracle complexity (2) proximal algorithm (2) transfer learning (1) gradient-based method (1) constrained optimization (1) stochastic optimization (1) linear regression (1) sparse matrix (1) distributed optimization (1) convex minimization (1) precision matrix estimation (1) graph learning (1) primal-dual algorithm (1)

Papers

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization NeurIPS 2024 FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization NeurIPS 2021 Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function NeurIPS 2020 Transferring Optimality Across Data Distributions via Homotopy Methods ICLR 2020 Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates NeurIPS 2018 Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization NeurIPS 2017 Convex Block-sparse Linear Regression with Expanders – Provably AISTATS 2016 A Universal Primal-Dual Convex Optimization Framework NeurIPS 2015 Constrained convex minimization via model-based excessive gap NeurIPS 2014 A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions ICML 2013