Navigate: Transfer & Meta-Learning | (Deep) Probabilistic & Generative Models | Information fusion | Reinforcement learning | Other



Selected Research by Categories



I have actively pursued research in the categories listed below along with a small selection of representative sources/publications. For a complete list of publications please see my publications page.




Transfer and meta-learning



Selected sources:

  • Fast adaptation via linearized neural networks [pdf] (2019)
  • Variational mutual information distillation for transfer learning [pdf] (2019)
  • Empirical Bayes transdutive meta-learning with Synthetic Gradients [pdf] (2019)
  • Transfer learning blog post [medium] (2019)
  • Xfer: Open-source deep transfer learning in Python [Github]
  • Transferring Knowledge across learning processes [pdf] (2018)

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(Deep) Probabilistic & Generative models



Selected publications:

  • Deep Gaussian processes [Thesis, pdf]
  • Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa [pdf] (2018)
  • Deep Gaussian Processes with Convolutional Kernels [pdf] (2018)
  • beta-BNN: A Rate-Distortion Perspective on Bayesian Neural Networks. (2018)
  • Variational auto-encoded Deep Gaussian processes [pdf] (2016)
  • Recurrent Gaussian processes [pdf] (2016)
  • Introductory tutorial on deep learning (probabilistic perspective) with code [online]
  • Introductory tutorial on Gaussian processes with code [online]

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Information fusion & Muti-view learning



Selected publications:

  • Deep Gaussian processes for multi-fidelity modeling [pdf] (2018)
  • Non-linear information fusion algorithms for robust multi-fidelity modeling [pdf] (2017)
  • Manifold Alignment Determination: finding correspondences across different data views [ pdf] (2017)
  • Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis [pdf] (2016)
  • Probabilistic Consolidation of Grasp Experience [pdf] (2016)

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Reinforcement Learning



Selected publications:

  • ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems [pdf] (2019)
  • Inverse Reinforcement Learning via Deep Gaussian Process [pdf] (2017)
  • Online Constrained Model-based Reinforcement Learning [pdf] (2017)

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Other Application areas



Other application areas where I have published:

  • Bayesian Optimization & Active Learning
  • Dynamical Systems
  • Robotics
  • Variational inference
  • Topic models

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