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A Divergence Minimization Perspective on Imitation Learning Methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu
In many settings, it is desirable to learn decision-making and control policies through learning or bootstrapping from expert demonstrations. The most common approaches under this…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
Causal Modeling for Fairness in Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi +1
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamical…
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh +9
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of…
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled…
Understanding the Limitations of Conditional Generative Models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl +1
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a rob…