Carlson, David

13 publications

ICML 2025 Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series Zachary C. Brown, David Carlson

NeurIPS 2025 Pose Splatter: A 3D Gaussian Splatting Model for Quantifying Animal Pose and Appearance Jack Goffinet, Youngjo Min, Carlo Tomasi, David Carlson

TMLR 2025 Understanding and Robustifying Sub-Domain Alignment for Domain Adaptation Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David Carlson

TMLR 2024 Incorporating Prior Knowledge into Neural Networks Through an Implicit Composite Kernel Ziyang Jiang, Tongshu Zheng, Yiling Liu, David Carlson

ICML 2023 Estimating Causal Effects Using a Multi-Task Deep Ensemble Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David Carlson

TMLR 2022 Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou, William E Carson Iv, David Carlson

WACV 2022 Learning to Weight Filter Groups for Robust Classification Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David Carlson

JMLR 2021 Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

MLHC 2020 Attention-Based Network for Weak Labels in Neonatal Seizure Detection Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martinez, Martin Bertran, Guillermo Sapiro, David Carlson

AISTATS 2019 On Target Shift in Adversarial Domain Adaptation Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David Carlson

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

ICML 2015 Scalable Deep Poisson Factor Analysis for Topic Modeling Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin