Contextual Bandits in a Non-Stationary Environment

Huazheng Wang
Department of Computer Science
University of Virginia

  • Date: Wednesday March 20th, 2019
  • Time: 1:00PM
  • Location: Rice 242

Abstract Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users’ preferences are dynamic. In this talk, I will introduce three works on non-stationary contextual bandit algorithms, including 1) detecting possible changes of environment, 2) finding a dynamic ensemble of admissible bandit models and 3) modeling user dependency for collaborative learning. Theoretical analysis and empirical evaluations on real-world recommendation datasets validate the effectiveness of the algorithms.

Building Fair Representations for Images and Text

Vicente Ordonez
Department of Computer Science
University of Virginia

  • Date: Wednesday March 6th, 2019
  • Time: 1:00PM
  • Location: Rice 242

Abstract There has been an increased amount of attention on the societal consequences of deploying machine learning models for decision making. Machine learning models often make disparate decisions for different segments of the input population that correlate with protected variables such as race, gender, and age. A more problematic scenario in current systems, is their reliance on generic representations learned from large amounts of data and re-used for many downstream applications, e.g. pre-trained CNNs for images, and pre-trained word-embeddings for text. In our group, we have been studying problems of representation, where the objective is to build generic representations for images and text that do not encode information about protected variables. I will discuss some of our current work in this area in the domain of both images and text.

Deep Learning for Genomics

Jack Lanchantin
Department of Computer Science
University of Virginia

  • Date: Wednesday February 27th, 2019
  • Time: 1:00PM
  • Location: Rice 242

Abstract Since the Human Genome project was completed in 2003, scientists have sought to discover the mechanisms by which DNA and its surrounding elements control biological processes. In this talk, I will introduce two essential biological processes related to the Genome and explain our work in using deep learning to predict and understand them. I will also discuss some of the open problems related to machine learning in Genomics.