Tam Le
25 papers · 2013–2025 · 8 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+6 more ↓ Show less ↑
🗺️ Taxonomy Completionist (31) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🔬
Deep Specialist
(12)
🏆
Keyword Champion
(2)
⚡
Prolific Year · Productive
🔥
Unstoppable · Bronze
🗃️
Keyword Collector
(105)
💎
Century Club · Bronze
Top co-authors
Research topics
Machine Learning
(22)
Core Methods
(14)
Optimization & Theory · Machine Learning
(14)
Mathematics & Optimization
(12)
Mathematics
(9)
Optimization
(8)
Deep Learning
(5)
Artificial Intelligence
(3)
Learning Types
(3)
Architectures
(2)
Core AI
(2)
Optimization & Theory · Deep Learning
(2)
Computer Science
(1)
Data Science & Analytics
(1)
Bayesian & Probabilistic
(1)
Keywords
optimal transport
(13)
wasserstein distance
(5)
kernel methods
(4)
gradient flow
(4)
tree metric
(4)
sliced wasserstein
(3)
graph metric space
(3)
probability measure
(3)
radon transform
(2)
sobolev transport
(2)
unbalanced transport
(2)
graph metric
(2)
generative model
(2)
topological data analysis
(2)
sliced wasserstein distance
(2)
hyperparameter tuning
(2)
negative definite kernel
(2)
entropic regularization
(2)
neural architecture search
(1)
probability metrics
(1)
Papers
Distance-Based Tree-Sliced Wasserstein Distance
ICLR 2025
SAVA: Scalable Learning-Agnostic Data Valuation
ICLR 2025
Scalable Sobolev IPM for Probability Measures on a Graph
ICML 2025
Spherical Tree-Sliced Wasserstein Distance
ICLR 2025
Tree-Sliced Wasserstein Distance: A Geometric Perspective
ICML 2025
Tree-Sliced Wasserstein Distance with Nonlinear Projection
ICML 2025
Universal generalization guarantees for Wasserstein distributionally robust models
ICLR 2025
Generalized Sobolev Transport for Probability Measures on a Graph
ICML 2024
Optimal Transport for Measures with Noisy Tree Metric
AISTATS 2024
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
CLeaR 2024
Sliced Wasserstein with Random-Path Projecting Directions
ICML 2024
Dynamic Flows on Curved Space Generated by Labeled Data
IJCAI 2023
Scalable Unbalanced Sobolev Transport for Measures on a Graph
AISTATS 2023
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
AISTATS 2022
Adversarial Regression with Doubly Non-negative Weighting Matrices
NeurIPS 2021
Entropy Partial Transport with Tree Metrics: Theory and Practice
AISTATS 2021
Flow-based Alignment Approaches for Probability Measures in Different Spaces
AISTATS 2021
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
NeurIPS 2021
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
ICML 2021
Point-Set Distances for Learning Representations of 3D Point Clouds
ICCV 2021
Safe Grid Search with Optimal Complexity
ICML 2019
Tree-Sliced Variants of Wasserstein Distances
NeurIPS 2019
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
NeurIPS 2018
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations
ICML 2015
Generalized Aitchison Embeddings for Histograms
ACML 2013