I am a PhD candidate in the Computer Science Department at the University of California, Irvine. Padhraic Smyth is my advisor. My research interests reside in both the theory and application of Bayesian models, with a emphasis on choosing priors and incorportating neural networks for inference. I've done research internships at Amazon, Microsoft Research / Bing, and Twitter Cortex. I graduated from Lehigh University (Bethlehem, PA) where I worked with Henry Baird. NEWS: I'll be joining the Cambridge Machine Learning group, as a postdoc, upon graduating this spring.
preprints / working papers
Eric Nalisnick and Padhraic Smyth. Learning Priors for Invariance. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Playa Blanca, Canary Islands, April 9-11 2018. Eric Nalisnick and Padhraic Smyth. Learning Approximately Objective Priors. In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), Sydney, Australia, August 11-15 2017. Eric Nalisnick and Padhraic Smyth. Stick-Breaking Variational Autoencoders. In Proceedings of the 5th International Conference on Learning Representations (ICLR), Toulon, France, April 24-26 2017. [Code] [Supplemental Materials] Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana. Improving Document Ranking with Dual Word Embeddings. In Proceedings of the 25th World Wide Web Conference (WWW), Short Paper, Montreal, Canada, April 11-15 2016. Eric T. Nalisnick and Henry S. Baird. Character-to-Character Sentiment Analysis in Shakespeare's Plays. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Short Paper, pages 479-83, Sofia, Bulgaria, August 4-9 2013. [Shakespeare Sentiment Explorer] Eric T. Nalisnick and Henry S. Baird. Extracting Sentiment Networks from Shakespeare's Plays. In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pages 758-762, Washington, USA, August 25-28, 2013.
Disi Ji, Eric Nalisnick, and Padhraic Smyth. Mondrian Processes for Flow Cytometry Analysis. Machine Learning for Health, Workshop at NIPS 2017, Long Beach, USA, December 8, 2017. Eric Nalisnick and Padhraic Smyth. Variational Inference with Stein Mixtures. Advances in Approximate Bayesian Inference, Workshop at NIPS 2017, Long Beach, USA, December 8, 2017. Eric Nalisnick and Padhraic Smyth. The Amortized Bootstrap. Implicit Models, Workshop at ICML 2017, Sydney, Australia, August 10, 2017. [Oral Presentation] Eric Nalisnick and Padhraic Smyth. Variational Reference Priors. Workshop Track, ICLR 2017, Toulon, France, April 24-26 2017. Eric Nalisnick, Lars Hertel, and Padhraic Smyth. Approximate Inference for Deep Latent Gaussian Mixtures. Bayesian Deep Learning, Workshop at NIPS 2016, Barcelona, Spain, December 19, 2016. Eric Nalisnick and Padhraic Smyth. Nonparametric Deep Generative Models with Stick-Breaking Priors. Data-Efficient Machine Learning, Workshop at ICML 2016, New York, USA, June 24, 2016. [Oral Presentation] Jihyun Park, Meg Blume-Kohout, Ralf Krestel, Eric Nalisnick, and Padhraic Smyth. Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Workshop at AAAI 2016, Phoenix, USA, February 12-13, 2016.
uci data science workshops
Predictive Modeling with Python. Advanced Predictive Modeling with Python.
notesStochastic Backprop through Mixture Densities. Stein Variational Gradient Descent. Generative Adversarial Networks (w/ Tensorflow basics). Operator Variational Inference. Mondrian Processes. The Beta Divergence. Learning Model Reparametrizations.