Thesis
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Safe optimization algorithms for variable selection and hyperparameter tuning.
E. Ndiaye.
Université Paris-Saclay, October 4th, 2018.
Manuscript ,
slides .
Publications
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Exact and Approximate Conformal Inference in Multiple Dimensions.
C. Johnstone, E. Ndiaye.
Arxiv, 2022.
paper ,
code in preparation .
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A Confidence Machine for Sparse High-Order Interaction Model.
D. Das, E. Ndiaye, I. Takeuchi.
Arxiv, 2022.
paper ,
code in preparation .
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Stable Conformal Prediction Sets.
E. Ndiaye.
International Conference on Machine Learning, 2022.
paper ,
code .
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Continuation Path with Linear Convergence Rate.
E. Ndiaye, I. Takeuchi.
Arxiv, 2021.
paper ,
code in preparation .
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Root-finding Approaches for Computing Conformal Prediction Set.
E. Ndiaye, I. Takeuchi.
Accepted to Machine Learning , 2021.
paper ,
code .
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Screening Rules and its Complexity for Active Set Identification.
E. Ndiaye, O. Fercoq, J. Salmon.
Journal of Convex Analysis, 2020.
paper ,
code .
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Computing Full Conformal Prediction Set with Approximate Homotopy.
E. Ndiaye, I. Takeuchi.
Advances in Neural Information Processing Systems, 2019.
paper ,
code .
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Safe Grid Search with Optimal Complexity.
E. Ndiaye, T. Le, O. Fercoq, J. Salmon, I. Takeuchi.
International Conference on Machine Learning, 2019.
paper ,
code .
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Gap Safe screening rules for sparsity enforcing penalties.
E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon.
Journal of Machine Learning Research, 2017.
paper ,
code .
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Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression.
E. Ndiaye, O. Fercoq, V. Leclère, A. Gramfort, J. Salmon.
Journal of Physics: Conference Series, 2017.
paper ,
code .
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GAP Safe Screening Rules for Sparse-Group-Lasso.
E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon.
Advances in Neural Information Processing Systems, 2016.
paper ,
code .
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GAP Safe screening rules for sparse multi-task and multi-class models.
E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon.
Advances in Neural Information Processing Systems, 2015.
paper .
code .
Teaching
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Instructor for the class ISyE 6740, Computational Data Science. Spring 2022, Graduate level.
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Teaching assistant at Nagoya Institute of Technology. Book reading in machine learning with both graduate and undergraduate students.
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Teaching assistant at Télécom ParisTech.
Master courses in optimization and machine learning:
Linear Models, Clustering, Bootstrap, Ensemble Methods, First Order Optimization and Stochastic Algorithm etc.