Monthly Archives: February 2016

Talk by Joao Messias

You are all cordially invited to the next AMLab colloquium this Tuesday, February 23 at 16:00 in C3.163, where Joao Messias will give a talk titled “Variable-Order Markov Models for Sequence Prediction”. Afterwards there is the ‘borrel’ with drinks and snacks!

Abstract: The problem of learning how to predict future values of discrete stochastic sequences arises in many different domains. (Semi-)Markovian models with latent state representations, such as HMMs, have been widely used for this purpose, but their application can be difficult in domains with complex state or observation spaces. Recently, the first tractable approximations to the AIXI interpretation of Universal Reinforcement Learning have made use of Variable-Order Markov Models (VMMs) to approach this problem from a different perspective, by learning how to predict directly from histories of past observations, without requiring an explicit latent state representation, and in some cases, without knowing a priori how long these histories of observations should be.

In this talk, I will present an introduction to VMM methods for sequence prediction. I will discuss the relationship between VMM prediction and lossless compression; and present a review of the most well-known VMM methods. Finally, I will discuss future directions of research in this topic, in the context of reinforcement learning under partial observability.

Talk by Mandar Chandorkar (CWI)

You are all cordially invited to the next AMLab colloquium on Tuesday, February 16 at 16:00 in C3.163, where Mandar Chandorkar from the Multiscale Dynamics Group at CWI will give a talk titled “Space Weather Prediction using Gaussian Process (GP) Non Linear Auto-Regressive Models ”.

Abstract: Two models for predicting the Dst geo-magnetic time series are proposed and compared.
1. Non Linear Auto-regressive (GP-NAR)
2. Non Linear Auto-regressive GP with exogenous inputs (GP-NARX)
We present the results of extensive scale model testing experiments on the OMNI data set collected from the ACE satellite.