(Georgia Institute of Technology, 2018-02-22)
Arora, Sanjeev
Generative Adversarial Nets (GANs) is a framework for training deep generative models,
due to Goodfellow et al'13. It involves a competition between a generator net that tries to produce
realistic images, and a discriminator that tries to distinguish the output from real images. The
framework has been applied to many settings, but it has been open to quantify how well it does,
though the images often look reasonable. In our paper in ICML'17 (joint with Ge, Liang, Ma, Zhang)
we give an analysis for the case of finite discriminators and generators. On the positive side, we can
show the existence of an equilibrium where generator succeeds in fooling the discriminator. On the
negative side, we show that in this equilibrium, generator produces a distribution of fairly low support.
This can be seen as a failure mode of the GANs framework. But in subsequent work in ICLR'18 (joint
with Risteski and Zhang) we show that this failure mode exists in popular GANs frameworks, which
we show learn distributions with fairly small support. We quantify this using our new "birthday
paradox" test.
(Georgia Institute of Technology, 2018-02-22)
Pineau, Joelle
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent
paradigm. A growing number of dialogue systems use conversation strategies that are learned from
large datasets. In this talk I will review several recent models and algorithms based on both
discriminative and generative models, and discuss new results on the proper performance measures
for such systems. Finally, I will highlight potential ethical issues that arise in dialogue systems
research, including: implicit biases, adversarial examples, privacy violations, and safety concerns.