David E. Carlson

72 papers A* 10A 8Journal 40Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models. CoRR Jack Goffinet, Casey Hanks, David E. Carlson
2025	J	`jnl`	CARE: Turning LLMs Into Causal Reasoning Expert. CoRR Juncheng Dong, Yiling Liu, Ahmed Aloui, Vahid Tarokh, David E. Carlson
2025	—	`conf`	ClinStructor: AI-Powered Structuring of Unstructured Clinical Texts. IJCNLP-AACL (long papers) Karthikeyan K, Raghuveer Thirukovalluru, David E. Carlson
2025	J	`jnl`	Efficient few-shot medical image segmentation via self-supervised variational autoencoder. Medical Image Anal. Yanjie Zhou, Feng Zhou, Fengjun Xi, Yong Liu, Yun Peng, David E. Carlson, Liyun Tu
2025	—	`conf`	MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation. KDD (2) Yiling Liu, Wei Shi, Chen Fu, Ziyang Jiang, Zhigang Hua, David E. Carlson
2025	J	`jnl`	Pose Splatter: A 3D Gaussian Splatting Model for Quantifying Animal Pose and Appearance. CoRR Jack Goffinet, Youngjo Min, Carlo Tomasi, David E. Carlson
2025	J	`jnl`	Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data. Pattern Recognit. Hongwei Zhang, Ziyang Jiang, Song Zhang, Liyun Tu, David E. Carlson
2025	J	`jnl`	Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimation. CoRR Yiling Liu, Juncheng Dong, Chen Fu, Wei Shi, Ziyang Jiang, Zhigang Hua, David E. Carlson
2025	J	`jnl`	Understanding and Robustifying Sub-domain Alignment for Domain Adaptation. Trans. Mach. Learn. Res. Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson
2024	J	`jnl`	Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential. CoRR Ian Hough, Loïc Argentier, Ziyang Jiang, Tongshu Zheng, Mike Bergin, David E. Carlson, Jean-Luc Jaffrezo, Jocelyn Chanussot, Gaëlle Uzu
2024	J	`jnl`	Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation. CoRR Lei Duan, Ziyang Jiang, David E. Carlson
2024	J	`jnl`	Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments. CoRR Ziyang Jiang, Zach Calhoun, Yiling Liu, Lei Duan, David E. Carlson
2024	J	`jnl`	Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation. CoRR Feng Zhou, Yanjie Zhou, Longjie Wang, Yun Peng, David E. Carlson, Liyun Tu
2024	J	`jnl`	Generative Principal Component Regression via Variational Inference. CoRR Austin Talbot, Corey J. Keller, David E. Carlson, Alex V. Kotlar
2024	J	`jnl`	Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. Trans. Mach. Learn. Res. Ziyang Jiang, Tongshu Zheng, Yiling Liu, David E. Carlson
2023	J	`jnl`	Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators. CoRR Ziyang Jiang, Yiling Liu, Michael Hunter Klein, Ahmed Aloui, Yiman Ren, Keyu Li, Vahid Tarokh, David E. Carlson
2023	J	`jnl`	Domain Adaptation via Rebalanced Sub-domain Alignment. CoRR Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson
2023	A*	`conf`	Estimating Causal Effects using a Multi-task Deep Ensemble. ICML Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson
2023	J	`jnl`	Estimating Causal Effects using a Multi-task Deep Ensemble. CoRR Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson
2022	J	`jnl`	AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models. CoRR William E. Carson IV, Austin Talbot, David E. Carlson
2022	J	`jnl`	Estimating Potential Outcome Distributions with Collaborating Causal Networks. Trans. Mach. Learn. Res. Tianhui Zhou, William E. Carson IV, David E. Carlson
2022	J	`jnl`	Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. CoRR Ziyang Jiang, Tongshu Zheng, David E. Carlson
2022	A	`conf`	Learning to Weight Filter Groups for Robust Classification. WACV Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson
2022	J	`jnl`	Multiple Domain Causal Networks. CoRR Tianhui Zhou, William E. Carson IV, Michael Hunter Klein, David E. Carlson
2022	J	`jnl`	Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. IEEE Trans. Signal Process. Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson
2021	J	`jnl`	Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers. CoRR William E. Carson IV, Dmitry Yu. Isaev, Samantha Major, Guillermo Sapiro, Geraldine Dawson, David E. Carlson
2021	A*	`conf`	Directed Spectrum Measures Improve Latent Network Models Of Neural Populations. NeurIPS Neil Gallagher, Kafui Dzirasa, David E. Carlson
2021	J	`jnl`	Estimating Potential Outcome Distributions with Collaborating Causal Networks. CoRR Tianhui Zhou, David E. Carlson
2021	J	`jnl`	Estimating Uncertainty Intervals from Collaborating Networks. J. Mach. Learn. Res. Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson
2021	J	`jnl`	Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest-Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology. Remote. Sens. Tongshu Zheng, Michael Bergin, Guoyin Wang, David E. Carlson
2021	J	`jnl`	Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. CoRR Liyun Tu, Austin Talbot, Neil Gallagher, David E. Carlson
2020	—	`conf`	Attention-Based Network for Weak Labels in Neonatal Seizure Detection. MLHC Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martínez, Martín Bertrán, Guillermo Sapiro, David E. Carlson
2020	A*	`conf`	Dynamic Embedding on Textual Networks via a Gaussian Process. AAAI Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David E. Carlson, Lawrence Carin
2020	J	`jnl`	Estimating Uncertainty Intervals from Collaborating Networks. CoRR Tianhui Zhou, Yitong Li, Yuan Wu, David E. Carlson
2020	J	`jnl`	Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. CoRR Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson
2019	J	`jnl`	Gaussian-Process-Based Dynamic Embedding for Textual Networks. CoRR Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Cheng, David E. Carlson, Lawrence Carin
2019	A	`conf`	On Target Shift in Adversarial Domain Adaptation. AISTATS Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson
2019	J	`jnl`	On Target Shift in Adversarial Domain Adaptation. CoRR Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson
2019	A*	`conf`	StoryGAN: A Sequential Conditional GAN for Story Visualization. CVPR Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao
2018	A*	`conf`	Extracting Relationships by Multi-Domain Matching. NeurIPS Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson
2018	J	`jnl`	StoryGAN: A Sequential Conditional GAN for Story Visualization. CoRR Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao
2018	A*	`conf`	Video Generation From Text. AAAI Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin
2017	—	`conf`	Cross-Spectral Factor Analysis. NIPS Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson
2017	A*	`conf`	Stochastic Bouncy Particle Sampler. ICML Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski
2017	—	`conf`	Targeting EEG/LFP Synchrony with Neural Nets. NIPS Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson
2017	J	`jnl`	Video Generation From Text. CoRR Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin
2017	—	`conf`	YASS: Yet Another Spike Sorter. NIPS Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski
2016	A	`conf`	Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. AISTATS Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin
2016	A	`conf`	Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. AISTATS Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin
2016	J	`jnl`	Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2016	A	`conf`	Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. AISTATS Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin
2016	A*	`conf`	Partition Functions from Rao-Blackwellized Tempered Sampling. ICML David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
2016	A*	`conf`	Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks. AAAI Chunyuan Li, Changyou Chen, David E. Carlson, Lawrence Carin
2016	J	`jnl`	Stochastic Spectral Descent for Discrete Graphical Models. IEEE J. Sel. Top. Signal Process. David E. Carlson, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, Volkan Cevher
2015	J	`jnl`	Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. CoRR Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin
2015	—	`conf`	Deep Temporal Sigmoid Belief Networks for Sequence Modeling. NIPS Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
2015	J	`jnl`	Deep Temporal Sigmoid Belief Networks for Sequence Modeling. CoRR Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
2015	—	`conf`	GP Kernels for Cross-Spectrum Analysis. NIPS Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin
2015	A	`conf`	Learning Deep Sigmoid Belief Networks with Data Augmentation. AISTATS Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin
2015	J	`jnl`	Neuroprosthetic decoder training as imitation learning. CoRR Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2015	—	`conf`	Preconditioned Spectral Descent for Deep Learning. NIPS David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher
2015	A*	`conf`	Scalable Deep Poisson Factor Analysis for Topic Modeling. ICML Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin
2015	A	`conf`	Stochastic Spectral Descent for Restricted Boltzmann Machines. AISTATS David E. Carlson, Volkan Cevher, Lawrence Carin
2014	—	`conf`	Analysis of Brain States from Multi-Region LFP Time-Series. NIPS Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
2014	A	`conf`	Latent Gaussian Models for Topic Modeling. AISTATS Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin
2014	J	`jnl`	Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Trans. Biomed. Eng. David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, Lawrence Carin
2014	—	`conf`	On the relations of LFPs & Neural Spike Trains. NIPS David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
2013	—	`conf`	Designed Measurements for Vector Count Data. NIPS Liming Wang, David E. Carlson, Miguel R. D. Rodrigues, David Wilcox, A. Robert Calderbank, Lawrence Carin
2013	—	`conf`	Real-Time Inference for a Gamma Process Model of Neural Spiking. NIPS David E. Carlson, Vinayak A. Rao, Joshua T. Vogelstein, Lawrence Carin
2011	J	`jnl`	Detection of Viruses Via Statistical Gene Expression Analysis. IEEE Trans. Biomed. Eng. Minhua Chen, David E. Carlson, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred O. Hero III, Joseph E. Lucas, Lawrence Carin
2011	—	`conf`	On the Analysis of Multi-Channel Neural Spike Data. NIPS Bo Chen, David E. Carlson, Lawrence Carin
1980	J	`jnl`	Bit-Oriented Data Link Control Procedures. IEEE Trans. Commun. David E. Carlson