Mark Paskin

NeurIPS Conference 2006 Conference Paper

Distributed Inference in Dynamical Systems

• Stanislav Funiak
• Carlos Guestrin
• Rahul Sukthankar
• Mark Paskin

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout the network, and to advance this estimate to the next time step. In addition, we identify a significant challenge for probabilistic inference in dynamical systems: message losses or network partitions can cause nodes to have inconsistent beliefs about the current state of the system. We address this problem by developing distributed algorithms that guarantee that nodes will reach an informative consistent distribution when communication is re-established. We present a suite of experimental results on real-world sensor data for two real sensor network deployments: one with 25 cameras and another with 54 temperature sensors.

UAI Conference 2005 Conference Paper

Robotic Mapping with Polygonal Random Fields

• Mark Paskin
• Sebastian Thrun

NeurIPS Conference 2003 Conference Paper

Sample Propagation

• Mark Paskin

Rao–Blackwellization is an approximation technique for probabilistic in- ference that flexibly combines exact inference with sampling. It is useful in models where conditioning on some of the variables leaves a sim- pler inference problem that can be solved tractably. This paper presents Sample Propagation, an efficient implementation of Rao–Blackwellized approximate inference for a large class of models. Sample Propagation tightly integrates sampling with message passing in a junction tree, and is named for its simple, appealing structure: it walks the clusters of a junction tree, sampling some of the current cluster’s variables and then passing a message to one of its neighbors. We discuss the application of Sample Propagation to conditional Gaussian inference problems such as switching linear dynamical systems.

NeurIPS Conference 2001 Conference Paper

Grammatical Bigrams

• Mark Paskin

Unsupervised learning algorithms have been derived for several sta(cid: 173) tistical models of English grammar, but their computational com(cid: 173) plexity makes applying them to large data sets intractable. This paper presents a probabilistic model of English grammar that is much simpler than conventional models, but which admits an effi(cid: 173) cient EM training algorithm. The model is based upon grammat(cid: 173) ical bigrams, i. e. , syntactic relationships between pairs of words. We present the results of experiments that quantify the represen(cid: 173) tational adequacy of the grammatical bigram model, its ability to generalize from labelled data, and its ability to induce syntactic structure from large amounts of raw text.

NeurIPS Conference 2001 Conference Paper

Linear-time inference in Hierarchical HMMs

• Kevin Murphy
• Mark Paskin

The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original infer- is ence algorithm is rather complicated, and takes the length of the sequence, making it impractical for many domains. In this paper, we show how HHMMs are a special kind of dynamic Bayesian network (DBN), and thereby derive a much simpler inference algorithm, which only takes time. Furthermore, by drawing the connection between HHMMs and DBNs, we enable the application of many stan- dard approximation techniques to further speed up inference.