#### Filter Results:

#### Publication Year

2005

2012

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

— A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of our environment, and then plan to maximize reward. Unfortunately, learning algorithms often recover a model which is too inaccurate to support planning or too large and complex… (More)

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However , they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We… (More)

Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approximation of the problem as a convex program, start with a solution to a relaxed version of the program, and incrementally… (More)

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n discrete observations, with a transition matrix of rank k ≤… (More)

For Hidden Markov Models (HMMs) with fully connected transition models, the three fundamental problems of evaluating the likelihood of an observation sequence, estimating an optimal state sequence for the observations, and learning the model parameters, all have quadratic time complexity in the number of states. We introduce a novel class of non-sparse… (More)

Choosing the number of hidden states and their topology (model selection) and estimating model parameters (learning) are important problems for Hidden Markov Models. This paper presents a new state-splitting algorithm that addresses both these problems. The algorithm models more information about the dynamic context of a state during a split, enabling it to… (More)

We consider dynamic co-occurrence data, such as author-word links in papers published in successive years of the same conference. For static co-occurrence data, researchers often seek an embedding of the entities (authors and words) into a low-dimensional Euclidean space. We generalize a recent static co-occurrence model, the CODE model of Globerson et al.… (More)

Scarcity of labelled data often hampers the learning of Hidden Markov Models in applications such as speech, text, and video processing. Although current active learning algorithms can select examples to be labelled, it is not clear how to select examples to be completely annotated. This work presents a novel information gain solution to the problem. The… (More)

In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robustness. Our scheme is based on our recently introduced machine learning algorithm called Simultaneous Temporal And Con-textual Splitting (STACS) that discovers the appropriate number… (More)

OBJECTIVE We propose a novel technique for building generative models of real-valued multivariate time series data streams. Such models are of considerable utility as baseline simulators in anomaly detection systems. The proposed algorithm, based on Linear Dynamical Systems (LDS) [1], learns stable parameters efficiently while yielding more accurate results… (More)