155 citations · 945 across the 25 of their papers we have counts for
25 papers
Marginalizable Density Models
Dar Gilboa, Ari Pakman, Thibault Vatter
Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets. However, unlike kernel density estimators, modern…
Estimating the Unique Information of Continuous Variables
Ari Pakman, Amin Nejatbakhsh, Dar Gilboa +4
The integration and transfer of information from multiple sources to multiple targets is a core motive of neural systems. The emerging field of partial information decomposition (P…
Amortized Bayesian inference for clustering models
Ari Pakman, Liam Paninski
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models.…
Binary Bouncy Particle Sampler
Ari Pakman
The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to…
Stochastic Bouncy Particle Sampler
Ari Pakman, Dar Gilboa, David Carlson +1
We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The al…
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman +1
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex an…