Search Results for author:
Found 6 papers, 1 papers with code
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
Particle filters flexibly represent multiple posterior modes nonparametrically, via a collection of weighted samples, but have classically been applied to tracking problems with known dynamics and observation likelihoods.
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
We investigate how fairness relaxations scale to flexible classifiers like deep neural networks for images and text.
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
We propose a dynamic Bayesian network that includes the on-screen goal position as part of its latent state, and thus allows the person’s intended angle of movement to be aggregated over a much longer history of neural activity.
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
Bayesian nonparametric hidden Markov models are typically learned via fixed truncations of the infinite state space or local Monte Carlo proposals that make small changes to the state space.
Memoized Online Variational Inference for Dirichlet Process Mixture Models
Variational inference algorithms provide the most effective framework for large-scale training of Bayesian nonparametric models.
Efficient Online Inference for Bayesian Nonparametric Relational Models
In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size.
