12 citations · 25 across the 6 of their papers we have counts for
8 papers
Training language models in simulation for psychiatric intake
Samuel Schmidgall, Tim Strother, Alex Bijamov +53
In psychiatric practice, the clinical intake interview remains a primary diagnostic instrument, requiring the clinician to establish rapport while disentangling complex, overlappin…
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
Tanguy Marchand, Régis Loeb, Ulysse Marteau-Ferey +2
We consider a cross-silo federated learning (FL) setting where a machine learning model with a fully connected first layer is trained between different clients and a central server…
Second order conditions to decompose smooth functions as sums of squares
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
We consider the problem of decomposing a regular non-negative function as a sum of squares of functions which preserve some form of regularity. In the same way as decomposing non-n…
Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a…
Finding Global Minima via Kernel Approximations
Alessandro Rudi, Ulysse Marteau-Ferey, Francis Bach
We consider the global minimization of smooth functions based solely on function evaluations. Algorithms that achieve the optimal number of function evaluations for a given precisi…
Non-parametric Models for Non-negative Functions
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Linear models have shown great effectiveness and flexibility in many fields such as machine learning, signal processing and statistics. They can represent rich spaces of functions…
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regr…
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis Bach +1
We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictor…