Sequential bayesian experimental design with variable cost structure

Integrates upper/lower MI bounds to formulate Bayesian Optimal Experiment Design (BOED) with successive refinement/tightening via adaptive allocation of computation to achieve a desired performance guarantee. Achieves the same guarantee as existing methods with fewer evaluations of the costly MI reward.

Belief-dependent macro-action discovery in pomdps using the value of information

This work introduces macro-action discovery using value-of-information (VoI) for robust and efficient planning in partially observable Markov decision processes (POMDPs). POMDPs are a powerful framework for planning under uncertainty. Previous approaches have used high-level macro-actions within POMDP policies to reduce planning complexity, but are often heuristic and rarely come with performance guarantees. Here, we present a method for extracting belief-dependent, variable-length macro-actions directly from a low-level POMDP model.

Nonparametric Object and Parts Modeling with Lie Group Dynamics

Articulated motion analysis methods often rely on strong prior knowledge, e.g. as applied to human motion. However, the world contains a variety of articulating objects - mammals, insects, mechanized structures - with parts varying number and configuration. We develop a Bayesian nonparametric model applicable to such objects combined with coupled rigid-body motion dynamics. We derive an efficient Gibbs sampling procedure applicable to differing observation sequence types without need for markers or learned body models.

A Robust Approach to Sequential Information Theoretic Planning

Mutual information (MI) is often used a reward in information-driven Bayesian optimal experiment design (BOED). MI generally lacks a closed form necessitating estimators. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Robust M-estimators yield bounds on absolute deviation of MI estimates. We propose an integrated inference-and-planning algorithm leveraging sample-reuse at each stage. Empirical results show improvement over recent methods for inference of gene-regulatory networks.

A Video Representation Using Temporal Superpixels

Presents a generative probabilistic model of temporally consistent superpixels where, in contrast to other methods, a temporal superpixel in one frame tracks the same part of an underlying object in subsequent frames. We use a bilateral Gaussian process model of flow between frames to propagate superpixels in an online fashion. We present four new metrics to measure performance of a temporal superpixel representation and find that our method outperforms supervoxel methods.