32 citations · 107 across the 13 of their papers we have counts for
11 papers · 1 filter
Learning Elastic Costs to Shape Monge Displacements
Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin +3
Given a source and a target probability measure supported on , the Monge problem asks to find the most efficient way to map one distribution to the other. This effici…
A Confidence Machine for Sparse High-Order Interaction Model
Diptesh Das, Eugene Ndiaye, Ichiro Takeuchi
In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the c…
Stable Conformal Prediction Sets
Eugene Ndiaye
When one observes a sequence of variables , Conformal Prediction (CP) is a methodology that allows to estimate a confidence set for given…
Root-finding Approaches for Computing Conformal Prediction Set
Eugene Ndiaye, Ichiro Takeuchi
Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses…
Screening Rules and its Complexity for Active Set Identification
Eugene Ndiaye, Olivier Fercoq, Joseph Salmon
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning. This has…
Computing Full Conformal Prediction Set with Approximate Homotopy
Eugene Ndiaye, Ichiro Takeuchi
If you are predicting the label of a new object with , how confident are you that ? Conformal prediction methods provide an elegant framework for answering…
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
Gap Safe screening rules for sparsity enforcing penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules…
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +2
In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider penalty to enf…
GAP Safe Screening Rules for Sparse-Group-Lasso
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more ref…
GAP Safe screening rules for sparse multi-task and multi-class models
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimiza…