Machine Learning for Large Documents, Social Science and Language-Based Games

Register here.

Wednesday, June 29, 2016 - 6:00 PM to 8:00 PM

University of Colorado Denver - 1201 Larimer Street Room ACAD 1600, Denver, CO 

NOTE: For folks unable to attend in person register and we will email you a live-stream link prior to event.

Agenda:

6:00 - 6:20 Schmooze - Food shall be served

6:20 - 6:30 Announcements

6:30 - 7:30 Opening up the Black Box: Interactive Machine Learning for Understanding Large Document Collections, Characterizing Social Science, and Language-Based Games by Jordan Boyd-Graber

7:30 - 8:00 Networking

Opening up the Black Box: Interactive Machine Learning for Understanding Large Document Collections, Characterizing Social Science, and Language-Based Games - Abstract

Machine learning is ubiquitous, but most users treat it as a black box: a handy tool that suggests purchases, flags spam, or autocompletes text. I present qualities that ubiquitous machine learning should have to allow for a future filled with fruitful, natural interactions with humans: interpretability, interactivity, and an understanding of human qualities. After introducing these properties, I present machine learning applications that begin to fulfill these desirable properties. I begin with a traditional information processing task---making sense and categorizing large document collections---and show that machine learning methods can provide interpretable, efficient techniques to categorize large document collections with a human in the loop. From there, I turn to techniques to help computers understand and detect when texts reveal their writer's ideology or duplicity. Finally, I end with a setting combining all of these properties: language-based games and simultaneous machine translation.

Jordan Boyd-Graber - Bio

Jordan Boyd-Graber is an assistant professor in the University of Colorado Boulder's Computer Science Department, formerly serving as an assistant professor at the University of Maryland. He is a 2010 graduate of Princeton University, with a PhD thesis on "Linguistic Extensions of Topic Models" with David Blei. Jordan's research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process. This research often leads him to use tools such as large-scale inference for probabilistic methods, natural language processing, multilingual corpus understanding, and human computation. He is a recipient of the 2015 Karen Spärk Jones award and has won "best of" awards at NIPS, NAACL, and CoNLL.