We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm
performs linear regression in a high-dimensional feature space induced by a Mercer kernel …

Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the
policy evaluation problem of reinforcement learning. In this paper we extend the GPTD …

Bayesian approaches to learning and estimation have played a significant role in the Statistics
literature over many years. While they are often provably optimal in a frequentist setting, …

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble
learning, providing a useful reference for researchers in the field of Boosting as well as for …

We present a novel Bayesian approach to the problem of value function estimation in
continuous state spaces. We define a probabilistic generative model for the value function by …

Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based
methods, such as BackPropagation (BP). Inference in probabilistic graphical models is often …

In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning
of novel future tasks. Under the assumption that future tasks are ‘related’to previous tasks…

We compute upper and lower bounds on the VC dimension of feedforward networks of units
with piecewise polynomial activa (cid: 173) tion functions. We show that if the number of …

10.7554/eLife.10094.001 Many recent models study the downstream projection from grid
cells to place cells, while recent data have pointed out the importance of the feedback …

We consider the problem of one-step ahead prediction for time series generated by an
underlying stationary stochastic process obeying the condition of absolute regularity, describing …