Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We formalize the concept of a minimal model set, which facilitates qualitative comparisons between different ap...

We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. Thi...

The speech signal is a combination of attributes that contain information of the speaker, channel and noise. Conventional speaker verification systems train a single generic model for all cases, and handle all variations from these attributes either by factor analysis, or by not considering the variations explicitly. We propose a new methodology to partition the data space according to these fa...

We publish first experiments on a new approach for training unigram prior probabilities of pronunciation variants using the Discriminative Model Combination (DMC) framework. A specific analysis of the approach is provided together with an error analysis before and after the DMC-training of the pronunciation priors.

Can we discover audio-visually consistent events from videos in a totally unsupervised manner? And, how to mine videos with different genres? In this paper we present our new results in automatically discovering audio-visual events. A new measure is proposed to select audio-visually consistent elements from the two dendrograms respectively representing hierarchical clustering results for the au...