Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike
most previous literature on this topic, our bounds are anytime-valid (ie, time-uniform) …

The speed with which a learning algorithm converges as it is presented with more data is a
central problem in machine learning—a fast rate of convergence means less data is needed …

Capturing predictor-dependent correlations amongst the elements of a multivariate
response vector is fundamental to numerous applied domains, including neuroscience …

We present a PAC-Bayesian analysis of lifelong learning. In the lifelong learning problem, a
sequence of learning tasks is observed one-at-a-time, and the goal is to transfer information …

Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy
evaluation and improvement via two separate function approximators. The practicality of this …

This paper proposes a Markov Decision Process and reinforcement learning based
approach for domain selection in a multidomain Spoken Dialogue System built on a …

Our goal is to learn control policies for robots that provably generalize well to novel
environments given a dataset of example environments. The key technical idea behind our …