Statistical Learning
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Communication can be a significant bottleneck.
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Machine learning is also increasingly used by law enforcement, where ethical questions become civil rights questions.
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state-and time-dependent switching dynamics.
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state-and time-dependent switching dynamics.
Topology-Imbalance Learning for Semi-Supervised Node Classification Deli Chen
Thus, TINL is independent of QINL in terms of the object of study.
Supplementary Material: Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
We provide the supplementary material for our paper "Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning" [
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Our algorithm incorporates constraints into the Riemannian version of Hamiltonian Monte Carlo and maintains sparsity.
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Our algorithm incorporates constraints into the Riemannian version of Hamiltonian Monte Carlo and maintains sparsity.