Tam Le
25 papers · 2013–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (8) π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge π Academic Marathon (12)
π
Conference Polyglot
(8)
π
Academic Marathon
(12)
π§
Keyword Pioneer
π§¬
Topic Evolution
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(75)
π
Century Club
(25)
π₯
Unstoppable
(5)
π
Trend Setter
β‘
Prolific Year
(7)
Conferences
ICML (8)
AISTATS (5)
ICLR (4)
NIPS (4)
ACML (1)
CLEAR (1)
ICCV (1)
IJCAI (1)
Top co-authors
Keywords
optimal transport
(8)
tree metric
(4)
kernel methods
(4)
probability measure
(3)
topological data analysis
(2)
graph metric
(2)
unbalanced transport
(2)
wasserstein distance
(2)
negative definite kernel
(2)
hyperparameter tuning
(2)
probability metrics
(1)
neural architecture search
(1)
robust optimization
(1)
sliced wasserstein distance
(1)
generative modeling
(1)
fisher kernel
(1)
convex loss
(1)
matrix factorization
(1)
metric learning
(1)
multivariate analysis
(1)
Papers
Distance-Based Tree-Sliced Wasserstein Distance
ICLR 2025
Tree-Sliced Wasserstein Distance with Nonlinear Projection
ICML 2025
Scalable Sobolev IPM for Probability Measures on a Graph
ICML 2025
Spherical Tree-Sliced Wasserstein Distance
ICLR 2025
Universal generalization guarantees for Wasserstein distributionally robust models
ICLR 2025
Tree-Sliced Wasserstein Distance: A Geometric Perspective
ICML 2025
SAVA: Scalable Learning-Agnostic Data Valuation
ICLR 2025
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
CLEAR 2024
Sliced Wasserstein with Random-Path Projecting Directions
ICML 2024
Generalized Sobolev Transport for Probability Measures on a Graph
ICML 2024
Optimal Transport for Measures with Noisy Tree Metric
AISTATS 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
Flow-based Alignment Approaches for Probability Measures in Different Spaces
AISTATS 2021
Point-Set Distances for Learning Representations of 3D Point Clouds
ICCV 2021
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
ICML 2021
Entropy Partial Transport with Tree Metrics: Theory and Practice
AISTATS 2021
Adversarial Regression with Doubly Non-negative Weighting Matrices
NIPS 2021
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
NIPS 2021
Safe Grid Search with Optimal Complexity
ICML 2019
Tree-Sliced Variants of Wasserstein Distances
NIPS 2019
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
NIPS 2018
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations
ICML 2015
Generalized Aitchison Embeddings for Histograms
ACML 2013