Directed Networks
Personalized Federated Learning via Feature Distribution Adaptation
Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.
Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
Further, we emphasize the significance of principled regularization of the network parameters and prediction.
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL *Pascal Leroy
In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
We compute the convergence rates of the RSVB approximate posterior and the corresponding optimal value.
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers Lorenzo Tiberi 1,2 Francesca Mignacco
Second, generalization--what specific aspects of the transformer architecture are responsible for their effective learning?
Generalization in the Face of Adaptivity: A Bayesian Perspective
However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for
Enhancing Adversarial Robustness via Score-Based Optimization
Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations.