Statistical Learning
Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games Zihan Zhu Duke University Ethan X. Fang Duke University Zhuoran Yang Yale University
We focus on finding the Nash equilibrium of decision-dependent games in the bandit feedback setting.
Mechanic: A Learning Rate Tuner
Modern deep learning is driven by first-order stochastic optimization algorithms.
Transformers over Directed Acyclic Graphs
Graph-structured data is ubiquitous in various disciplines [Gilmer et al., 2017, Zitnik et al., 2018, Sanchez-Gonzalez et al., 2020] and hence graph representation learning has the potential to provide
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Previous work has focused primarily on developing ML-based "meta-learners" that can provide point estimates of the conditional
DEL T A: Diverse Client Sampling for Fasting Federated Learning Lin Wang
Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently.
A Notation Table
When the loss is set as squared loss, we can modify the functions regarding the truncated spectral projection.
