A common setting in multitask reinforcement learning (RL) demands that a...
We introduce a new approach to prediction in graphical models with
laten...
Exploration is essential in reinforcement learning, particularly in
envi...
We introduce a new approach to probabilistic unsupervised learning based...
A key goal of unsupervised learning is to go beyond density estimation a...
We propose a novel framework for multitask reinforcement learning based ...
Artificial neural systems trained using reinforcement, supervised, and
u...
Both animals and artificial agents benefit from state representations th...
Models that employ latent variables to capture structure in observed dat...
Animals need to devise strategies to maximize returns while interacting ...
Instrumental variable regression is a strategy for learning causal
relat...
We develop an approach to learn an interpretable semi-parametric model o...
In a common experimental setting, the behaviour of a noisy dynamical sys...
We introduce a new approach to learning in hierarchical latent-variable
...
We consider the problem of inference in a causal generative model where ...
We introduce the Locally Linear Latent Variable Model (LL-LVM), a
probab...
Neural language models (LMs) based on recurrent neural networks (RNN) ar...
The past decade has seen substantial work on the use of non-negative mat...