Richard Nock

Adjunct Professor, the Australian National University, the University of Sydney & Senior Principal Researcher, Data61

email: (first name).(last name)@{anu.edu.au,data61.csiro.au}


Background and Research interests

I defended a PhD in computer science under the supervision of Olivier Gascuel and an accreditation to lead research (HDR) in computer science with Michel Habib. I graduated simultaneously in computer science and in agronomical engineering ("Ingénieur Agro", majors in statistics and industrial microbiology), after two years of CPGE ("Classes Préparatoires aux Grandes Ecoles").

My interests cover Machine Learning, Privacy, Information Geometry at large. I am Associate Editor of Springer's Information Geometry journal, and SPC for AISTATS 2019. I was a Local Chair of ICML 2017.


Recent invited talks

  • Confidential Computing - Federate Private Data Analysis
    NIPS 2016 PMPML workshop on Private Multi-Party Machine Learning
    [ slides | PMPMLw' page ]


Most recent publications [ Scholar | DBLP ]

  • Marta Avalos, Richard Nock, Cheng-Soon Ong, Julien Rouar and Ke Sun
    Representation Learning of Compositional Data
    NIPS 2018, Advances in Neural Information Processing Systems (accepted)
    [ paper, supplementary, code: soon ]
  • Amir Dezfouli, Bernard Balleine and Richard Nock
    Optimal Response Vigor and Choice under Non-Stationary Outcome Values
    Psychonomic Bulletin and Review 2018 (accepted)
    [ paper ]
  • Amir Dezfouli, Edwin Bonilla and Richard Nock
    Variational Network Inference: Strong and Stable with Concrete Support
    ICML 2018, International Conference on Machine Learning
    [ paper | supplementary | poster | code | slides | video (soon) ]
  • Kelvin Hsu, Richard Nock and Fabio Ramos
    Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
    ECML-PKDD 2018, European Conference on Machine Learning and KDD (Best Student ML Paper Award)
    [ paper + supplementary | poster (soon) | code | slides (soon) ]
  • Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu and Robert C. Williamson
    f-GANs in an Information Geometric Nutshell
    NIPS 2017, Advances in Neural Information Processing Systems (Spotlight)
    [ paper | supplementary | ArXiv (longer) | poster | code | slides | video ]
  • Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock and Lizhen Qu
    Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
    CVPR 2017, IEEE International Conference on Computer Vision and Pattern Recognition (Oral)
    [ paper | code | video | slides ]
  • Boris Muzellec, Richard Nock, Giorgio Patrini and Frank Nielsen
    Tsallis Regularized Optimal Transport and Ecological Inference
    AAAI 2017, AAAI Conference on Artificial Intelligence
    [ paper | supplementary | poster | code ]
  • Richard Nock
    On Regularizing Rademacher Observation Losses
    NIPS 2016, Advances in Neural Information Processing Systems
    [ paper | supplementary | poster | code | video ]
  • Richard Nock, Aditya Krishna Menon and Cheng-Soon Ong
    A scaled Bregman Theorem with Applications
    NIPS 2016, Advances in Neural Information Processing Systems
    [ paper | supplementary | poster | video]
  • Richard Nock, Raphael Canyasse, Roksana Boreli and Frank Nielsen
    k-variates++: more Pluses in the k-means++
    ICML 2016, International Conference on Machine Learning
    [ paper | supplementary | poster | slides ]
  • Giorgio Patrini, Frank Nielsen, Richard Nock and Marcello Carioni
    Loss Factorization, Weakly Supervised Learning and Label Noise Robustness
    ICML 2016, International Conference on Machine Learning
    [ paper | supplementary | poster | slides ]
  • Giorgio Patrini, Richard Nock, Stephen Hardy and Tiberio Caetano
    Fast Learning from Distributed Datasets without Entity Matching
    IJCAI 2016, International Joint Conference on Artificial Intelligence
    [ paper | ArXiv (longer) | poster | slides ]
  • Frank Nielsen, Boris Muzellec and Richard Nock
    Classification with Mixtures of Curved Mahalanobis Metrics
    ICIP 2016, International Conference on Image Processing (Oral)
    [ paper | supplementary | slides ]
  • Richard Nock, Frank Nielsen and Shun-ichi Amari
    On Conformal Divergences and their Population Minimizers
    IEEE Trans. on Information Theory 2016 (Vol 62), pp 527-538
    [ paper | ArXiv (paper + supplementary + additional results) ]

Recent media and wider audience outreach

  • Can I Trust my System ? Machine Learning at the Age of the Data Deluge
    AI for Government 2018 [ conference page ]
  • Leveraging AI and Automation in Cybersecurity in order to advance the Security Field -- the Good and the Bad
    SINET61 2018 [ conference page ]
  • DeepFakes
    ABC Weekend Sunrise News, April 2018 [ channel document ]


Students

    PhD program:

  • 2016: Giorgio Patrini (ANU)

    Interns:

  • 2018: Mona Buisson-Fenet (Ecole des Mines de Paris)
  • 2018: Olivia Torres (Ecole des Mines de Paris)
  • 2016: Boris Muzellec (Ecole Polytechnique)
  • 2015: Raphael Canyasse (Ecole Polytechnique)
  • 2015: Alexis Le Dantec (Ecole Polytechnique)