Research

Projects

1. Safe Grid Search with Optimal Complexity (ICML'19)
2. Persistence Fisher Kernel for Persistence Diagrams (NIPS'18)
3. Unsupervised Riemannian Metric Learning (ICML'15)
4. Generalized Aitchison Embeddings for Histograms (ML'14, ACML'13)
5. Hierarchical Spatial Matching Kernel for Object Categorization
6. Traffic Sign Detection

Selected Publications:

• Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi, Safe Grid Search with Optimal Complexity, to appear in International Conference on Machine Learning (ICML), US, 2019. [ArXiv/Code] (Acceptance rate: 773/3424=22.6%)
• Tam Le, Makoto Yamada, Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams, The 32nd Conference on Neural Information Processing Systems (NeurIPS), Canada, 2018. [PDF/Supplemental/PROJECT/POSTER] (Acceptance rate: 1011/4856=20.8%)
• Tam Le, Marco Cuturi, Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations, International Conference on Machine Learning (ICML), France, 2015. [VideoLecture/PDF/Supplemental/SLIDE/POSTER] (Acceptance rate: 270/1037 = 26.0%)
• Tam Le, Marco Cuturi, Adaptive Euclidean Maps for Histograms: Generalized Aitchison Embeddings, Machine Learning Journal (MLJ), 2014. [PDF/PROJECT]