ML Anthology

Nock, Richard

64 publications

NeurIPS 2024 Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar
TMLR 2024 For Robust Worst-Group Accuracy, Ignore Group Annotations Nathan Stromberg, Rohan Ayyagari, Monica Welfert, Sanmi Koyejo, Richard Nock, Lalitha Sankar
NeurIPS 2024 Generative Forests Richard Nock, Mathieu Guillame-Bert
NeurIPS 2024 How to Boost Any Loss Function Richard Nock, Yishay Mansour
NeurIPS 2024 Hyperbolic Embeddings of Supervised Models Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
AAAI 2024 Optimal Transport with Tempered Exponential Measures Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
NeurIPS 2023 Boosting with Tempered Exponential Measures Richard Nock, Ehsan Amid, Manfred Warmuth
AISTATS 2023 Clustering Above Exponential Families with Tempered Exponential Measures Ehsan Amid, Richard Nock, Manfred K. Warmuth
ICML 2023 Fair Densities via Boosting the Sufficient Statistics of Exponential Families Alexander Soen, Hisham Husain, Richard Nock
ICML 2023 LegendreTron: Uprising Proper Multiclass Loss Learning Kevin H Lam, Christian Walder, Spiridon Penev, Richard Nock
ICML 2023 Random Classification Noise Does Not Defeat All Convex Potential Boosters Irrespective of Model Choice Yishay Mansour, Richard Nock, Robert Williamson
AISTATS 2023 Smoothly Giving up: Robustness for Simple Models Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar
ICML 2022 Being Properly Improper Tyler Sypherd, Richard Nock, Lalitha Sankar
NeurIPS 2022 Fair Wrapping for Black-Box Predictions Alexander Soen, Ibrahim M Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
ICML 2022 Generative Trees: Adversarial and Copycat Richard Nock, Mathieu Guillame-Bert
CVPR 2022 Manifold Learning Benefits GANs Yao Ni, Piotr Koniusz, Richard Hartley, Richard Nock
ICML 2022 Neural Network Poisson Models for Behavioural and Neural Spike Train Data Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli
FnTML 2021 Advances and Open Problems in Federated Learning Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
ICML 2021 Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
ICML 2021 The Impact of Record Linkage on Learning from Feature Partitioned Data Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
NeurIPS 2020 All Your Loss Are Belong to Bayes Christian Walder, Richard Nock
AISTATS 2020 Local Differential Privacy for Sampling Hisham Husain, Borja Balle, Zac Cranko, Richard Nock
ECCV 2020 On Modulating the Gradient for Meta-Learning Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
ICML 2020 Supervised Learning: No Loss No Cry Richard Nock, Aditya Menon
NeurIPS 2019 A Primal-Dual Link Between GANs and Autoencoders Hisham Husain, Richard Nock, Robert C. Williamson
ICML 2019 Boosted Density Estimation Remastered Zac Cranko, Richard Nock
NeurIPS 2019 Disentangled Behavioural Representations Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
ICML 2019 Lossless or Quantized Boosting with Integer Arithmetic Richard Nock, Robert Williamson
ICML 2019 Monge Blunts Bayes: Hardness Results for Adversarial Training Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder
ECML-PKDD 2018 Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds Kelvin Hsu, Richard Nock, Fabio Ramos
NeurIPS 2018 Representation Learning of Compositional Data Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
ICML 2018 Variational Network Inference: Strong and Stable with Concrete Support Amir Dezfouli, Edwin Bonilla, Richard Nock
NeurIPS 2017 F-GANs in an Information Geometric Nutshell Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, Robert C. Williamson
CVPR 2017 Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
AAAI 2017 Tsallis Regularized Optimal Transport and Ecological Inference Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
NeurIPS 2016 A Scaled Bregman Theorem with Applications Richard Nock, Aditya Menon, Cheng Soon Ong
IJCAI 2016 Fast Learning from Distributed Datasets Without Entity Matching Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
ICML 2016 K-Variates++: More Pluses in the K-Means++ Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen
ICML 2016 Loss Factorization, Weakly Supervised Learning and Label Noise Robustness Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
NeurIPS 2016 On Regularizing Rademacher Observation Losses Richard Nock
ICML 2015 Rademacher Observations, Private Data, and Boosting Richard Nock, Giorgio Patrini, Arik Friedman
NeurIPS 2014 (Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
ECML-PKDD 2012 Boosting Nearest Neighbors for the Efficient Estimation of Posteriors Roberto D'Ambrosio, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen, Michel Barlaud
ICML 2011 On Tracking Portfolios with Certainty Equivalents on a Generalization of Markowitz Model: The Fool, the Wise and the Adaptive Richard Nock, Brice Magdalou, Eric Briys, Frank Nielsen
ECML-PKDD 2008 Mixed Bregman Clustering with Approximation Guarantees Richard Nock, Panu Luosto, Jyrki Kivinen
NeurIPS 2008 On the Efficient Minimization of Classification Calibrated Surrogates Richard Nock, Frank Nielsen
IJCAI 2007 Real Boosting a La Carte with an Application to Boosting Oblique Decision Tree Claudia Henry, Richard Nock, Frank Nielsen
ECML-PKDD 2005 Fitting the Smallest Enclosing Bregman Ball Richard Nock, Frank Nielsen
CVPR 2005 Interactive Pinpoint Image Object Removal Frank Nielsen, Richard Nock
ICML 2004 Boosting Grammatical Inference with Confidence Oracles Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier
CVPR 2004 Grouping with Bias Revisited Richard Nock, Frank Nielsen
CVPR 2003 On Region Merging: The Statistical Soundness of Fast Sorting, with Applications Frank Nielsen, Richard Nock
ECML-PKDD 2002 A Robust Boosting Algorithm Richard Nock, Patrice Lefaucheur
JAIR 2002 Inducing Interpretable Voting Classifiers Without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments Richard Nock
JMLR 2002 Stopping Criterion for Boosting-Based Data Reduction Techniques: From Binary to Multiclass Problem Marc Sebban, Richard Nock, Stéphane Lallich
ICML 2001 Boosting Neighborhood-Based Classifiers Marc Sebban, Richard Nock, Stéphane Lallich
CVPR 2001 Fast and Reliable Color Region Merging Inspired by Decision Tree Pruning Richard Nock
UAI 2000 Combining Feature and Example Pruning by Uncertainty Minimization Marc Sebban, Richard Nock
ICML 2000 Instance Pruning as an Information Preserving Problem Marc Sebban, Richard Nock
ALT 2000 Sharper Bounds for the Hardness of Prototype and Feature Selection Richard Nock, Marc Sebban
ALT 1999 Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any Richard Nock
ICML 1998 On the Power of Decision Lists Richard Nock, Pascal Jappy
ICML 1996 Negative Robust Learning Results from Horn Claus Programs Pascal Jappy, Richard Nock, Olivier Gascuel
ICML 1995 On Learning Decision Committees Richard Nock, Olivier Gascuel