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Tianyi Lin

21 papers · 2019–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (6) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (3) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) 🀝 Dynamic Duo (10) πŸ”¬ Deep Specialist (15) 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (100) πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (21) ⚑ Prolific Year (5)

Conferences

JMLR (5) AISTATS (4) ICML (4) NIPS (4) COLT (2) AACL (1) IJCNLP (1)

Top co-authors

Michael I. Jordan (10) Michael Jordan (8) nhαΊ­t Hα»“ (6) Marco Cuturi (5) Chi Jin (3) Jeffrey Huang (2) Leonid Boytsov (2) Fangwei Gao (2) Yaodong Yu (2) Yutian Zhao (2)

Keywords

optimal transport (7) sinkhorn algorithm (3) convex optimization (3) probability measure (3) gradient descent ascent (3) wasserstein distance (3) entropic regularization (3) nash equilibrium (3) minimax optimization (3) nonconvex optimization (2) stochastic optimization (2) min-max optimization (2) positional bia (2) first-order algorithm (2) game theory (2) iteration complexity (2) complexity bound (2) nonsmooth optimization (2) stationary point (2) long-document ranking (2)

Papers

Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation AACL 2025
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization JMLR 2025
Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation IJCNLP 2025
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport AISTATS 2024
Deterministic Nonsmooth Nonconvex Optimization COLT 2023
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems JMLR 2023
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ICML 2022
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization AISTATS 2022
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization NIPS 2022
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms AISTATS 2022
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport JMLR 2022
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling JMLR 2022
On the Complexity of Approximating Multimarginal Optimal Transport JMLR 2022
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces NIPS 2022
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification AISTATS 2021
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games ICML 2020
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems ICML 2020
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm NIPS 2020
Projection Robust Wasserstein Distance and Riemannian Optimization NIPS 2020
Near-Optimal Algorithms for Minimax Optimization COLT 2020
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms ICML 2019