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Eleni Triantafillou

Research Scientist at Google Brain.
Ph.D from the University of Toronto (2021), advised by Raquel Urtasun and Richard Zemel

[etriantafillou at google dot com]

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News

  • I am co-leading the organization of the first NeurIPS competition on unlearning [website].
  • Our paper "In Search for a Generalizable Method for Source Free Domain Adaptation" was accepted to ICML 2023 [paper] [blog post].
  • [New preprint] Towarded Unbounded Machine Unlearning [paper].
  • The recording of my NeurIPS tutorial about the role of meta-learning in few-shot learning is now available here.
  • I am co-organizing the NeurIPS 2022 workshop on meta-learning.
  • I gave an invited talk at the NeurIPS 2021 workshop on meta-learning.
  • I defended my PhD thesis in the summer of 2021 and have started working as a Research Scientist in Google Brain, based in London UK.
  • About me

    I'm a research scientist at Google Brain, based in London UK. My main research interest is around creating methods that allow efficient and effective adaptation of deep neural networks to cope with distribution shifts, introduction of new concepts, or removal of outdated or harmful knowledge. My research falls in the areas of few-shot learning, meta-learning, domain adaptation and machine unlearning.


    In my free time, I enjoy food blogging, dance (especially hip hop and commercial), piano playing, singing and song writing.

    Conference publications


    In Search for a Generalizable Method for Source Free Domain Adaptation
    Malik Boudiaf, Tom Denton, Bart van Merriƫnboer, Vincent Dumoulin*, Eleni Triantafillou*. ICML 2023. [paper].


    Learning a Universal Template for Few-shot Dataset Generalization.
    Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin. ICML 2021. [paper].


    Meta-dataset: A dataset of datasets for learning to learn from few examples.
    Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle. ICLR 2020. [paper].


    Meta Learning for Semi-Supervised Few-Shot Classification.
    Mengye Ren, Eleni Triantafillou*, Sachin Ravi*, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard Zemel. ICLR 2018. [paper]


    Few-Shot Learning Through an Information Retrieval Lens.
    Eleni Triantafillou, Richard Zemel and Raquel Urtasun. NeurIPS 2017. [paper]


    Non-Deterministic Planning with Temporally Extended Goals: LTL over finite and infinite traces.
    Alberto Camacho, Eleni Triantafillou, Christian Muise, Jorge Baier, and Sheila McIlraith. AAAI, 2017. [paper]

    Workshop papers and preprints


    Towards Unbounded Machine Unlearning
    Meghdad Kurmanji, Peter Triantafillou, Eleni Triantafillou. 2023. [paper].


    In Search for a Generalizable Method for Source Free Domain Adaptation
    Malik Boudiaf, Tom Denton, Bart van Merriƫnboer, Vincent Dumoulin*, Eleni Triantafillou*. 2023. [paper].


    Flexible Few-Shot Learning with Contextual Similarity.
    Mengye Ren*, Eleni Triantafillou*, Kuan-Chieh Wang*, James Lucas*, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel. 2020. [paper].


    Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training.
    Eleni Triantafillou, Vincent Dumoulin, Hugo Larochelle, Richard Zemel. Meta-Learning workshop at NeurIPS 2020. [paper].


    Few-shot Out-of-Distribution Detection.
    Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, Richard Zemel. UDL workshop at ICML 2020 (spotlight). [paper].


    Few-shot Learning for Free by Modelling Global Class Structure.
    Xuechen Li*, Will Grathwohl*, Eleni Triantafillou*, David Duvenaud, Richard Zemel. Meta-learning workshop at NeurIPS 2018. [paper].


    Towards Generalizable Sentence Embeddings
    Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel.
    1st Workshop on Representation Learning for NLP at ACL 2016. [paper]


    A Unifying Framework for Planning with LTL and Regular Expressions
    Eleni Triantafillou, Jorge A. Baier, Sheila A. McIlraith.
    MOCHAP workshop at ICAPS 2015. [paper]

    Community Service

    • Action Editor for TMLR.
    • Area chair for AutoML 2022 and 2023.
    • Reviewer for NeurIPS: 2018 (top 10%), 2019, 2020 (top 10%), 2021 (top 8%), 2022.
    • Reviewer for ICML: 2019 (top 5%), 2020, 2021 (expert reviewer), 2022.
    • Reviewer for ICLR: 2019, 2020, 2021 (outstanding reviewer), 2022, 2023
    • Reviewer for CVPR: 2021
    • Reviewer for UAI: 2018
    • Reviewer for IROS: 2021
    • Reviewer for TMLR: 2022 onwards
    • Program committee member for workshops: S2D-OLAD at ICLR 2021, Meta-Learning at NeurIPS: 2020 (senior reviewer), 2018 and 2017, AMTL at ICML 2019, LLD at ICLR 2019, LLD at NeurIPS 2017, WiML at NeurIPS 2017, WiCV at CVPR 2018 and 2021