ICML 2019

Congratulations to the UCL researchers with papers at this year's ICML conference.

  • Ricardo Silva: "Making Decisions that Reduce Discriminatory Impacts"
  • Kevin Li, Dougal Sutherland and Arthur Gretton: "Learning deep kernels for exponential family densities"
  • Ryutaro Tanno and Daniel Alexander: "Adaptive Neural Trees"
  • Jun Wang: "BayesNAS: A Bayesian Approach for Neural Architecture Search"
  • Lea Duncker, Gergo Bohner and Maneesh Sahani: "Learning interpretable continuous-time models of latent stochastic dynamical systems"
  • Riccardo Grazzi and Massimiliano Pontil: "Learning-to-Learn Stochastic Gradient Descent with Biased Regularization"
  • Ed Grefenstette: "CompILE: Compositional Imitation Learning and Execution"
  • Luca Franceschi and Massimiliano Pontil: "Learning Discrete Structures for Graph Neural Networks"
  • Afroditi Papadaki and Miguel Rodrigues: "Adversarially Learned Representations for Information Obfuscation and Inference"
  • Giulia Luise, Dimitrios Stamos and Massimiliano Pontil: "Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction"