Pakman, Ari

13 publications

AISTATS 2025 Bayesian Circular Regression with Von Mises Quasi-Processes Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

ICML 2025 Clustering via Self-Supervised Diffusion Roy Uziel, Irit Chelly, Oren Freifeld, Ari Pakman

AISTATS 2025 Consistent Amortized Clustering via Generative Flow Networks Irit Chelly, Roy Uziel, Oren Freifeld, Ari Pakman

ICMLW 2024 Amortized Probabilistic Detection of Communities in Graphs Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman

ICMLW 2024 Von Mises Quasi-Processes for Bayesian Circular Regression Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

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

ICML 2020 Neural Clustering Processes Ari Pakman, Yueqi Wang, Catalin Mitelut, Jinhyung Lee, Liam Paninski

NeurIPSW 2019 Spike Sorting Using the Neural Clustering Process Yueqi Wang, Ari Pakman, Catalin Mitelut, JinHyung Lee, Liam Paninski

ICML 2017 Stochastic Bouncy Particle Sampler Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski

ICML 2016 Partition Functions from Rao-Blackwellized Tempered Sampling David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

UAI 2016 Taming the Noise in Reinforcement Learning via Soft Updates Roy Fox, Ari Pakman, Naftali Tishby

NeurIPS 2013 Auxiliary-Variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski

NeurIPS 2013 Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski