Statistical Learning
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Among potential attacks against FR systems [Galbally et al., 2014, Biggio et al., 2015, Hadid et al., 2015, Mai et al., 2018, Marcel et al., 2023], the template inversion (TI) attack significantly jeopardizes the users' privacy. In a TI attack, the adversary gains access to templates stored in the FR system's database and aims
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts Elliott Ash
We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense).
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,
Appendix Table of Contents
In this section we provide full descriptions of the diffusion processes introduced in Sec. 3 .
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Code is available at this repository: https://github.com/thuml/Koopa.
Deep Recurrent Optimal Stopping
Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.
