Bayesian spike inference from calcium imaging data. - Eftychios A. Pnevmatikakis; Josh Merel; Ari Pakman; Liam Paninski (ACSSC, 2013)

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. - Ari Pakman; Liam Paninski (NIPS, 2013)

Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. - Benjamin Shababo; Brooks Paige; Ari Pakman; Liam Paninski (NIPS, 2013)

Fast state-space methods for inferring dendritic synaptic connectivity. - Ari Pakman; Jonathan Hunter Huggins; Carl Smith; Liam Paninski (J. Comput. Neurosci., 2014)

G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates. - Roy Fox; Ari Pakman; Naftali Tishby (CoRR, 2015)

Partition Functions from Rao-Blackwellized Tempered Sampling. - David E. Carlson; Patrick Stinson; Ari Pakman; Liam Paninski (ICML, 2016)

Taming the Noise in Reinforcement Learning via Soft Updates. - Roy Fox; Ari Pakman; Naftali Tishby (UAI, 2016)

Stochastic Bouncy Particle Sampler. - Ari Pakman; Dar Gilboa; David E. Carlson; Liam Paninski (ICML, 2017)

Amortized Bayesian inference for clustering models. - Ari Pakman; Liam Paninski (CoRR, 2018)

Neural Permutation Processes. - Ari Pakman; Yueqi Wang; Liam Paninski (AABI, 2019)

Discrete Neural Processes. - Ari Pakman; Liam Paninski (CoRR, 2019)

Neural Clustering Processes. - Ari Pakman; Yueqi Wang; Catalin Mitelut; Jin Hyung Lee; Liam Paninski (ICML, 2020)

Attentive Clustering Processes. - Ari Pakman; Yueqi Wang; Yoonho Lee 001; Pallab Basu; Juho Lee 001; Yee Whye Teh; Liam Paninski (CoRR, 2020)

Estimating the Unique Information of Continuous Variables. - Ari Pakman; Amin Nejatbakhsh; Dar Gilboa; Abdullah Makkeh; Luca Mazzucato; Michael Wibral; Elad Schneidman (NeurIPS, 2021)

Estimating the Unique Information of Continuous Variables. - Ari Pakman; Dar Gilboa; Elad Schneidman (CoRR, 2021)

Marginalizable Density Models. - Dar Gilboa; Ari Pakman; Thibault Vatter (CoRR, 2021)

von Mises Quasi-Processes for Bayesian Circular Regression. - Yarden Cohen; Alexandre K. W. Navarro; Jes Frellsen; Richard E. Turner; Raziel Riemer; Ari Pakman (CoRR, 2024)

Consistent Amortized Clustering via Generative Flow Networks. - Irit Chelly; Roy Uziel; Oren Freifeld; Ari Pakman (AISTATS, 2025)

Bayesian Circular Regression with von Mises Quasi-Processes. - Yarden Cohen; Alexandre K. W. Navarro; Jes Frellsen; Richard E. Turner; Raziel Riemer; Ari Pakman (AISTATS, 2025)

Super-efficient exact Hamiltonian Monte Carlo for the von Mises distribution. - Ari Pakman (Appl. Math. Lett., 2025)

Clustering via Self-Supervised Diffusion. - Roy Uziel; Irit Chelly; Oren Freifeld; Ari Pakman (CoRR, 2025)