A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

An important aspect of human perception is anticipation, which we use extensively in our
day-to-day activities when interacting with other humans as well as with our surroundings …

Many natural systems, such as neurons firing in the brain or basketball teams traversing a
court, give rise to time series data with complex, nonlinear dynamics. We can gain insight …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

We describe CST, an online algorithm for constructing skill trees from demonstration
trajectories. CST segments a demonstration trajectory into a chain of component skills …

Inspired from human cognition, machine learning systems are gradually revealing
advantages of sparser and more modular architectures. Recent work demonstrates that not …

Temporal segmentation of human motion into plausible motion primitives is central to
understanding and building computational models of human motion. Several issues …

We consider the problem of speaker diarization, the problem of segmenting an audio
recording of a meeting into temporal segments corresponding to individual speakers. The …