David E. Carlson
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
18
Venues
7
Active years
2014–2025
Best venue rank
A*
Where they publish
Papers
18 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | IJCNLP | ClinStructor: AI-Powered Structuring of Unstructured Clinical Texts. | Karthikeyan K, Raghuveer Thirukovalluru, David E. Carlson |
| 2025 | KDD | MOTTO: A Mixture-of-Experts Framework for Multi-Treatment, Multi-Outcome Treatment Effect Estimation. | Yiling Liu, Wei Shi, Chen Fu, Ziyang Jiang, Zhigang Hua, David E. Carlson |
| 2023 | ICML | Estimating Causal Effects using a Multi-task Deep Ensemble. | Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson |
| 2022 | WACV | Learning to Weight Filter Groups for Robust Classification. | Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson |
| 2020 | AAAI | Dynamic Embedding on Textual Networks via a Gaussian Process. | Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David E. Carlson, Lawrence Carin |
| 2019 | AISTATS | On Target Shift in Adversarial Domain Adaptation. | Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson |
| 2019 | CVPR | StoryGAN: A Sequential Conditional GAN for Story Visualization. | Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, Jianfeng Gao |
| 2018 | AAAI | Video Generation From Text. | Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, Lawrence Carin |
| 2017 | ICML | Stochastic Bouncy Particle Sampler. | Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski |
| 2016 | AAAI | Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks. | Chunyuan Li, Changyou Chen, David E. Carlson, Lawrence Carin |
| 2016 | AISTATS | Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. | Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin |
| 2016 | AISTATS | Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. | Yan Kaganovsky, Ikenna Odinaka, David E. Carlson, Lawrence Carin |
| 2016 | AISTATS | Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. | Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin |
| 2016 | ICML | Partition Functions from Rao-Blackwellized Tempered Sampling. | David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski |
| 2015 | AISTATS | Stochastic Spectral Descent for Restricted Boltzmann Machines. | David E. Carlson, Volkan Cevher, Lawrence Carin |
| 2015 | AISTATS | Learning Deep Sigmoid Belief Networks with Data Augmentation. | Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin |
| 2015 | ICML | Scalable Deep Poisson Factor Analysis for Topic Modeling. | Zhe Gan, Changyou Chen, Ricardo Henao, David E. Carlson, Lawrence Carin |
| 2014 | AISTATS | Latent Gaussian Models for Topic Modeling. | Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin |