Publications

Carlson, David, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, and Volkan Cevher. “Stochastic Spectral Descent for Discrete Graphical Models,” 2016.

Merel, Josh, David Carlson, Liam Paninski, and John P. Cunningham. “Neuroprosthetic decoder training as imitation learning.” PLoS Computational Biology 12 (2016).

Carlson, D. E., P. Stinson, A. Pakman, and L. Paninski. “Partition functions from rao-blackwellized tempered sampling.” In 33rd International Conference on Machine Learning Icml 2016, 6:4248–62, 2016.

Kaganovsky, Yan, Ikenna Odinaka, David Carlson, and Lawrence Carin. “Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization.” In Artificial Intelligence and Statistics, 1497–1505, 2016.

Song, Zhao, Ricardo Henao, David Carlson, and Lawrence Carin. “Learning sigmoid belief networks via Monte Carlo expectation maximization.” In Artificial Intelligence and Statistics, 1347–55, 2016.

Chen, Changyou, David Carlson, Zhe Gan, Chunyuan Li, and Lawrence Carin. “Bridging the gap between stochastic gradient MCMC and stochastic optimization.” In Artificial Intelligence and Statistics, 1051–60, 2016.

Li, Chunyuan, Changyou Chen, David Carlson, and Lawrence Carin. “Preconditioned stochastic gradient Langevin dynamics for deep neural networks.” In AAAI Conference on Artificial Intelligence, 2016.

Carlson, David E., Edo Collins, Ya-Ping Hsieh, Lawrence Carin, and Volkan Cevher. “Preconditioned spectral descent for deep learning.” In Advances in Neural Information Processing Systems, 2971–79, 2015.

Ulrich, Kyle R., David E. Carlson, Kafui Dzirasa, and Lawrence Carin. “GP kernels for cross-spectrum analysis.” In Advances in Neural Information Processing Systems, 1999–2007, 2015.

Gan, Zhe, Chunyuan Li, Ricardo Henao, David E. Carlson, and Lawrence Carin. “Deep Temporal Sigmoid Belief Networks for Sequence Modeling.” In NIPS, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2467–75, 2015.

Carlson, David. “Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series,” 2015.

Gan, Zhe, Ricardo Henao, David Carlson, and Lawrence Carin. “Learning deep sigmoid belief networks with data augmentation.” In Artificial Intelligence and Statistics, 268–76, 2015.

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

Gan, Zhe, Chunyuan Li, Ricardo Henao, David E. Carlson, and Lawrence Carin. “Deep temporal sigmoid belief networks for sequence modeling.” Advances in Neural Information Processing Systems, 2015, 2467–75.

Carlson, David, Volkan Cevher, and Lawrence Carin. “Stochastic spectral descent for restricted Boltzmann machines.” In Artificial Intelligence and Statistics, 111–19, 2015.

Carlson, David E., Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “On the relations of LFPs & Neural Spike Trains.” In NIPS, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger, 2060–68, 2014.

Carlson, David E., Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “On the Relationship Between LFP & Spiking Data.” In Advances in Neural Information Processing Systems, 2014.

Hu, Changwei, Eunsu Ryu, David Carlson, Yingjian Wang, and Lawrence Carin. “Latent Gaussian models for topic modeling.” In Artificial Intelligence and Statistics, 393–401, 2014.

Ulrich, Kyle, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “Analysis of Brain States from Multi-Region LFP Time-Series.” In Advances in Neural Information Processing Systems, 2014.

Wang, Liming, David Edwin Carlson, Miguel R. D. Rodrigues, Robert Calderbank, and Lawrence Carin. “A Bregman matrix and the gradient of mutual information for vector Poisson and Gaussian channels.” IEEE Transactions on Information Theory 60 (2014): 2611–29.

Wang, Liming, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, and Lawrence Carin. “Designed measurements for vector count data.” Advances in Neural Information Processing Systems, 2013, 1142–50.

Karumbaiah, Lohitash, Tarun Saxena, David Carlson, Ketki Patil, Radhika Patkar, Eric A. Gaupp, Martha Betancur, Garrett B. Stanley, Lawrence Carin, and Ravi V. Bellamkonda. “Relationship between intracortical electrode design and chronic recording function.” Biomaterials 34 (2013): 8061–74.

Carlson, David E., Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, and Lawrence Carin. “Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling.” IEEE Transactions on Biomedical Engineering 61 (2013): 41–54.

Carlson, David E., Vinayak Rao, Joshua T. Vogelstein, and Lawrence Carin. “Real-time inference for a gamma process model of neural spiking.” Advances in Neural Information Processing Systems, 2013, 2805–13.

Chen, Minhua, David Carlson, Aimee Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred Hero, Joseph Lucas, and Lawrence Carin. “Detection of viruses via statistical gene expression analysis.” IEEE Trans Biomed Eng 58, no. 3 (March 2011): 468–79. https://doi.org/10.1109/TBME.2010.2059702.