Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-
points in the polyhedral setting. We propose $(\varepsilon,\delta) $-DP algorithms based on …
points in the polyhedral setting. We propose $(\varepsilon,\delta) $-DP algorithms based on …
LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
Embedding parameterized optimization problems as layers into machine learning
architectures serves as a powerful inductive bias. Training such architectures with stochastic …
architectures serves as a powerful inductive bias. Training such architectures with stochastic …
Joint Learning of Energy-based Models and their Partition Function
Energy-based models (EBMs) offer a flexible framework for parameterizing probability
distributions using neural networks. However, learning EBMs by exact maximum likelihood …
distributions using neural networks. However, learning EBMs by exact maximum likelihood …
Learning with Fitzpatrick Losses
S Rakotomandimby, JP Chancelier, M De Lara… - arxiv preprint arxiv …, 2024 - arxiv.org
Fenchel-Young losses are a family of convex loss functions, encompassing the squared,
logistic and sparsemax losses, among others. Each Fenchel-Young loss is implicitly …
logistic and sparsemax losses, among others. Each Fenchel-Young loss is implicitly …
Machine learning and combinatorial optimization algorithms, with applications to railway planning
G Dalle - 2022 - pastel.hal.science
This thesis investigates the frontier between machine learning and combinatorial
optimization, two active areas of applied mathematics research. We combine theoretical …
optimization, two active areas of applied mathematics research. We combine theoretical …
A dual-receptor model of serotonergic psychedelics: therapeutic insights from simulated cortical dynamics
Serotonergic psychedelics have been identified as promising next-generation therapeutic
agents in the treatment of mood and anxiety disorders. While their efficacy has been …
agents in the treatment of mood and anxiety disorders. While their efficacy has been …
Stochastic first-order methods for differentially private machine learning
TG Lara - 2023 - search.proquest.com
En esta tesis estudiamos, desde un punto de vista teórico, dos problemas relevantes en los
campos de aprendizaje automático y análisis de datos con restricciones de privacidad: la …
campos de aprendizaje automático y análisis de datos con restricciones de privacidad: la …
Lagrangian Proximal Gradient Descent for Learning Convex Optimization Models
We propose Lagrangian Proximal Gradient Descent (LPGD), a flexible framework for
learning convex optimization models. Similar to traditional proximal gradient methods, LPGD …
learning convex optimization models. Similar to traditional proximal gradient methods, LPGD …
[HTML][HTML] Apprenticeship learning: transferring human motivations to artificial agents
L Hussenot - 2022 - lilloa.univ-lille.fr
L'apprentissage par renforcement est un cadre mathématique et algorithmique générique
qui vise à developper des algorithmes qui interagissent avec leur environnement et s' …
qui vise à developper des algorithmes qui interagissent avec leur environnement et s' …