Background, Research interests and more
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 (MSc) and in
agronomical engineering (MSc "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 an External Advisor to the Australian
Artificial Intelligence Institute, Associate Editor for IEEE
Transactions on Information Theory and Area Chair for ICLR 2024. I was Area
Chair for ICML 2022, 2021, 2020, 2019, ICLR 2022, 2021, AAAI 2022, 2021, AISTATS 2019 and NeurIPS 2023, 2022, 2021, 2020, 2019, and
a Local Chair of ICML
2017.
Some invited talks material
-
Confidential Computing - Federate Private Data Analysis
NIPS 2016 PMPML workshop on Private Multi-Party Machine Learning
[ slides |
PMPMLw' page ]
Some recent publications [ Scholar | DBLP ]
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Ehsan Amid, Frank Nielsen, Richard Nock and Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures
AAAI 2024, AAAI Conference on Artificial
Intelligence
[ paper, supplementary, poster, slides, code, video: soon | ArXiv ]
-
Richard Nock, Ehsan Amid and Manfred K. Warmuth
Boosting with Tempered Exponential Measures
NeurIPS 2023, Advances in Neural Information Processing
Systems
[ paper + supplementary | poster | code ]
-
Yishay Mansour, Richard Nock and Robert C. Williamson
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
ICML 2023, International Conference on Machine Learning (Oral)
[ paper + supplementary | poster | slides | video ]
-
Kevin H. Lam, Christian Walder, Spiridon Penev and Richard Nock
LegendreTron: Uprising Proper Multiclass Loss Learning
ICML 2023, International Conference on Machine Learning
[ paper + supplementary | poster | code ]
-
Alexander Soen, Hisham Husain and Richard Nock
Fair Densities via Boosting the Sufficient Statistics of Exponential Families
ICML 2023, International Conference on Machine Learning
[ paper + supplementary | poster | code ]
-
Ehsan Amid, Richard Nock and Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures
AISTATS 2023, International Conference on Artificial
Intelligence and Statistics
[ paper + supplementary | poster | slides | code | video ]
-
Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha and Lalitha Sankar
Smoothly Giving up: Robustness for Simple Models
AISTATS 2023, International Conference on Artificial
Intelligence and Statistics
[ paper + supplementary | code ]
-
Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour,
Nyalleng Moorosi, Richard Nock, Ke Sun and Lexing Xie
Fair Wrapping for Black-Box Predictions
NeurIPS 2022, Advances in Neural Information Processing
Systems
[ paper | supplementary | poster | code | video ]
-
Richard Nock and Mathieu Guillame-Bert
Generative Trees: Adversarial and Copycat
ICML 2022, International Conference on Machine Learning (Long presentation)
[ paper + supplementary | poster | code | slides | video ]
-
Tyler Sypherd, Richard Nock and Lalitha Sankar
Being Properly Improper
ICML 2022, International Conference on Machine Learning
[ paper + supplementary | poster | code | slides | video ]
-
Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan and Amir Dezfouli
Neural Network Poisson Models for Behavioural and Neural Spike Train Data
ICML 2022, International Conference on Machine Learning
[ paper + supplementary | poster | code | slides | video ]
-
Yao Ni, Piotr Koniusz, Richard Hartley and Richard Nock
Manifold Learning Benefits GANs
CVPR 2022, IEEE International Conference on Computer
Vision and Pattern Recognition
[ paper | supplementary | poster | code | slides ]
-
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock and Simon Kornblith
Generalized Lipschitz Regularisation Equals
Distributional Robustness
ICML 2021, International Conference on Machine Learning
[ paper | supplementary | slides | video ]
-
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub
Nabaglo, Giorgio Patrini, Guillaume Smith and Brian Thorne
The Impact of Record Linkage on Learning from Feature
Partitioned Data
ICML 2021, International Conference on Machine Learning
[ paper | supplementary | poster | slides | video | FLOW seminar ]
-
Amir Dezfouli, Richard Nock and Peter Dayan
Adversarial Vulnerabilities of Human Decision-Making
Proceedings of the National Academy of Sciences USA,
2020 (Vol 117), pp 29221–29228
[ paper (open access) | supplementary | data | code ]
-
Richard Nock and Frank Nielsen
The Phylogenetic Tree of Boosting has a Bushy Carriage but a Single Trunk
Proceedings of the National Academy of Sciences USA,
2020 (Vol 117), pp 8692-8693
[ paper (open access) ]
-
Christian Walder and Richard Nock
All your Loss are Belong to Bayes
NeurIPS 2020, Advances in Neural Information Processing
Systems
[ paper | supplementary | poster | code | video ]
-
Richard Nock and Aditya Krishna Menon
Supervised Learning: No Loss No Cry
ICML 2020, International Conference on Machine Learning
[ paper | supplementary | video ]
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Christian Simon, Piotr Koniusz, Richard Nock and Mehrtash Harandi
Adaptive Subspaces for Few-Shot Learning
CVPR 2020, IEEE International Conference on Computer
Vision and Pattern Recognition
[ paper | code ]
-
Christian Simon, Piotr Koniusz, Richard Nock and Mehrtash Harandi
On Learning to Modulate the Gradient for Fast Adaptation of Neural Networks
ECCV 2020, European Conference on Computer Vision
[ paper | code ]
-
Hisham Husain, Borja Balle, Zac Cranko and Richard Nock
Local Differential Privacy for Sampling
AISTATS 2020, International Conference on Artificial
Intelligence and Statistics
[ paper | supplementary | code ]
-
Richard Nock, Natalia Polouliakh, Frank Nielsen, Keiko Oga, Carlin R. Connell, Cedric Heimhofer, Kazuhiro Shibanai, Samik Ghosh, Ken-ichi Aisaki, Satoshi Kitajima, Jun Kanno, Taketo Akama and Hiroaki Kitano
A Geometric Clustering Tool (AGCT) to Robustly Unravel the Inner Structure of Time-Series Gene Expressions
PLoS ONE, 2020 15(7): e0233755
[ paper | AGCT
access ]
-
Hisham Husain, Richard Nock and Robert C. Williamson
A Primal-Dual link between GANs and Autoencoders
NeurIPS 2019, Advances in Neural Information Processing
Systems
[ paper | supplementary | poster ]
-
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter
Dayan and Cheng Soon Ong
Disentangled Behavioral Representations
NeurIPS 2019, Advances in Neural Information Processing
Systems
[ paper | supplementary | poster | BioRxiv version ]
-
Soumava Kumar Roy, Mehrtash Harandi, Richard Hartley and Richard Nock
Siamese Networks: the Tale of Two Manifolds
ICCV 2019, International Conference on Computer Vision (Oral)
[ paper | supplementary | video | code ]
-
Richard Nock and Robert C. Williamson
Lossless or Quantized Boosting with Integer Arithmetic
ICML 2019, International Conference on Machine Learning
[ paper | supplementary | poster | code | slides ]
-
Zac Cranko and Richard Nock
Boosted Density Estimation Remastered
ICML 2019, International Conference on Machine Learning
[ paper | supplementary | poster | code | slides ]
-
Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan
Shi and Christian Walder
Monge blunts Bayes: Hardness Results for Adversarial Training
ICML 2019, International Conference on Machine Learning
[ paper | supplementary | ArXiv | poster | code | slides ]
-
Samitha Herath, Basura Fernando, Mehrtash Harandi and Richard Nock
Min-Max Statistical Alignment for Transfer Learning
CVPR 2019, IEEE International Conference on Computer
Vision and Pattern Recognition
[ paper | supplementary | poster | code ]
-
Amir Dezfouli, Bernard Balleine and Richard Nock
Optimal Response Vigor and Choice under
Non-Stationary Outcome Values
Psychonomic Bulletin and Review 2019 (Vol 26), pp
182-204
[ paper ]
-
Marta Avalos-Fernandez, Richard Nock, Cheng Soon Ong, Julien Rouar and Ke Sun
Representation Learning of Compositional Data
NeurIPS/NIPS 2018, Advances in Neural Information Processing
Systems
[ paper + supplementary | poster | code | video ]
-
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 ]
-
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 | code | slides | video ]
-
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 ]
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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 | slides | video ]
-
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) ]
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