World's Best Scientists 2026 revealed!

Overview

Kevin Swersky is affiliated with Google in the United States and focuses primarily on research within the field of Computer Science. Their work extensively covers areas such as Artificial Intelligence, Computer Vision and Pattern Recognition, and Management Science and Operations Research, among other subfields.

Their research contributions include numerous publications in recognized venues, with a significant number appearing on arXiv (Cornell University). Other publication venues include the 2022 IEEE International Conference on Data Mining (ICDM) and the Proceedings of the AAAI Conference on Artificial Intelligence.

Notable recent papers by Kevin Swersky include:

  • Big Self-Supervised Models are Strong Semi-Supervised Learners, 2020, arXiv (Cornell University)
  • Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks, 2022, 2022 IEEE International Conference on Data Mining (ICDM)
  • Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks, 2021, arXiv (Cornell University)
  • Pre-trained Gaussian Processes for Bayesian Optimization, 2021, arXiv (Cornell University)
  • Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent research collaborators include Milad Hashemi, Bernd Bohnet, Noah Fiedel, Aaron Parisi, and Azade Nova.

Kevin Swersky's work addresses a broad set of main topics such as:

  • Machine Learning and Data Classification
  • Topic Modeling
  • Advanced Neural Network Applications
  • Natural Language Processing Techniques
  • Gaussian Processes and Bayesian Inference
  • Mental Health via Writing
  • Advanced Bandit Algorithms Research

Best Publications

  • Taking the Human Out of the Loop: A Review of Bayesian Optimization

    Bobak Shahriari;Kevin Swersky;Ziyu Wang;Ryan P. Adams

  • Prototypical Networks for Few-shot Learning

    Jake Snell;Kevin Swersky;Richard S. Zemel

  • Big Self-Supervised Models are Strong Semi-Supervised Learners

    Ting Chen;Simon Kornblith;Kevin Swersky;Mohammad Norouzi

  • Learning Fair Representations

    Rich Zemel;Yu Wu;Kevin Swersky;Toni Pitassi

  • Scalable Bayesian Optimization Using Deep Neural Networks

    Jasper Snoek;Oren Rippel;Oren Rippel;Kevin Swersky;Ryan Kiros

  • Generative Moment Matching Networks

    Yujia Li;Kevin Swersky;Rich Zemel;Rich Zemel

  • Meta-Learning for Semi-Supervised Few-Shot Classification

    Eleni Triantafillou;Hugo Larochelle;Jake Snell;Josh Tenenbaum

  • Multi-Task Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan P Adams

  • Meta-Learning for Semi-Supervised Few-Shot Classification

    Mengye Ren;Eleni Triantafillou;Sachin Ravi;Jake Snell

  • The Variational Fair Autoencoder

    Christos Louizos;Kevin Swersky;Yujia Li;Max Welling;Max Welling;Max Welling

  • Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions

    Jimmy Lei Ba;Kevin Swersky;Sanja Fidler;Ruslan Salakhutdinov

  • Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

    Eleni Triantafillou;Tyler Zhu;Vincent Dumoulin;Pascal Lamblin

  • Your classifier is secretly an energy based model and you should treat it like one

    Will Grathwohl;Kuan-Chieh Wang;Joern-Henrik Jacobsen;David Duvenaud

  • Input Warping for Bayesian Optimization of Non-Stationary Functions

    Jasper Snoek;Kevin Swersky;Rich Zemel;Ryan Adams

  • Freeze-Thaw Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan Prescott Adams

  • Inductive Principles for Restricted Boltzmann Machine Learning

    Benjamin M. Marlin;Kevin Swersky;Bo Chen;Nando de Freitas

  • Flexibly Fair Representation Learning by Disentanglement

    Elliot Creager;David Madras;Jörn-Henrik Jacobsen;Marissa A. Weis

  • Learning Memory Access Patterns

    Milad Hashemi;Kevin Jordan Swersky;Jamie Alexander Smith;Grant Ayers

  • On Autoencoders and Score Matching for Energy Based Models

    Kevin Swersky;Marc'aurelio Ranzato;David Buchman;Nando D. Freitas

  • Graph Normalizing Flows

    Jenny Liu;Aviral Kumar;Jimmy Ba;Jamie Kiros

Frequent Co-Authors

Richard S. Zemel
Richard S. Zemel University of Toronto
Ryan P. Adams
Ryan P. Adams Princeton University
Parthasarathy Ranganathan
Parthasarathy Ranganathan Google (United States)
Jasper Snoek
Jasper Snoek Google (United States)
David Duvenaud
David Duvenaud University of Toronto
Max Welling
Max Welling University of Amsterdam
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Hugo Larochelle
Hugo Larochelle Google (United States)
Mohammad Norouzi
Mohammad Norouzi Google (United States)

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