Richard Nock - RankMe

249 papers A* 45A 9B 16Misc 8Journal 116Unranked 53

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Seasoning Generative Models for a Generalization Aftertaste. CoRR Hisham Husain, Valentin De Bortoli, Richard Nock
2025	J	`jnl`	Principled Foundations for Preference Optimization. CoRR Wenxuan Zhou, Shujian Zhang, Brice Magdalou, John Lambert, Ehsan Amid, Richard Nock, Andrew Hard
2024	J	`jnl`	Boosting gets full Attention for Relational Learning. CoRR Mathieu Guillame-Bert, Richard Nock
2024	A*	`conf`	Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections. NeurIPS Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar
2024	J	`jnl`	For Robust Worst-Group Accuracy, Ignore Group Annotations. Trans. Mach. Learn. Res. Nathan Stromberg, Rohan Ayyagari, Monica Welfert, Sanmi Koyejo, Richard Nock, Lalitha Sankar
2024	A*	`conf`	Generative Forests. NeurIPS Richard Nock, Mathieu Guillame-Bert
2024	A*	`conf`	How to Boost Any Loss Function. NeurIPS Richard Nock, Yishay Mansour
2024	J	`jnl`	How to Boost Any Loss Function. CoRR Richard Nock, Yishay Mansour
2024	A*	`conf`	Hyperbolic Embeddings of Supervised Models. NeurIPS Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
2024	J	`jnl`	Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading. CoRR Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar
2024	A*	`conf`	Optimal Transport with Tempered Exponential Measures. AAAI Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
2024	J	`jnl`	Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
2023	A*	`conf`	Boosting with Tempered Exponential Measures. NeurIPS Richard Nock, Ehsan Amid, Manfred K. Warmuth
2023	J	`jnl`	Boosting with Tempered Exponential Measures. CoRR Richard Nock, Ehsan Amid, Manfred K. Warmuth
2023	A	`conf`	Clustering above Exponential Families with Tempered Exponential Measures. AISTATS Ehsan Amid, Richard Nock, Manfred K. Warmuth
2023	A*	`conf`	Fair Densities via Boosting the Sufficient Statistics of Exponential Families. ICML Alexander Soen, Hisham Husain, Richard Nock
2023	J	`jnl`	Generative Forests. CoRR Richard Nock, Mathieu Guillame-Bert
2023	A*	`conf`	LegendreTron: Uprising Proper Multiclass Loss Learning. ICML Kevin H. Lam, Christian J. Walder, Spiridon I. Penev, Richard Nock
2023	J	`jnl`	LegendreTron: Uprising Proper Multiclass Loss Learning. CoRR Kevin H. Lam, Christian J. Walder, Spiridon I. Penev, Richard Nock
2023	J	`jnl`	Optimal Transport with Tempered Exponential Measures. CoRR Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
2023	A*	`conf`	Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. ICML Yishay Mansour, Richard Nock, Robert C. Williamson
2023	A	`conf`	Smoothly Giving up: Robustness for Simple Models. AISTATS Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar
2023	J	`jnl`	Smoothly Giving up: Robustness for Simple Models. CoRR Tyler Sypherd, Nathan Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar
2023	J	`jnl`	The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
2022	A*	`conf`	Being Properly Improper. ICML Tyler Sypherd, Richard Nock, Lalitha Sankar
2022	J	`jnl`	Clustering above Exponential Families with Tempered Exponential Measures. CoRR Ehsan Amid, Richard Nock, Manfred K. Warmuth
2022	A*	`conf`	Fair Wrapping for Black-box Predictions. NeurIPS Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
2022	J	`jnl`	Fair Wrapping for Black-box Predictions. CoRR Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
2022	A*	`conf`	Generative Trees: Adversarial and Copycat. ICML Richard Nock, Mathieu Guillame-Bert
2022	J	`jnl`	Generative Trees: Adversarial and Copycat. CoRR Richard Nock, Mathieu Guillame-Bert
2022	A*	`conf`	Manifold Learning Benefits GANs. CVPR Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock
2022	A*	`conf`	Neural Network Poisson Models for Behavioural and Neural Spike Train Data. ICML Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli
2022	J	`jnl`	What killed the Convex Booster ? CoRR Yishay Mansour, Richard Nock, Robert C. Williamson
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	J	`jnl`	Being Properly Improper. CoRR Richard Nock, Tyler Sypherd, Lalitha Sankar
2021	—	`conf`	Computing Statistical Divergences with Sigma Points. GSI Frank Nielsen, Richard Nock
2021	A*	`conf`	Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
2021	J	`jnl`	Manifold Learning Benefits GANs. CoRR Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock
2021	A*	`conf`	The Impact of Record Linkage on Learning from Feature Partitioned Data. ICML Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
2020	A*	`conf`	Adaptive Subspaces for Few-Shot Learning. CVPR Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
2020	A*	`conf`	All your loss are belong to Bayes. NeurIPS Christian J. Walder, Richard Nock
2020	J	`jnl`	All your loss are belong to Bayes. CoRR Christian J. Walder, Richard Nock
2020	J	`jnl`	Boosted and Differentially Private Ensembles of Decision Trees. CoRR Richard Nock, Wilko Henecka
2020	J	`jnl`	Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family. CoRR Frank Nielsen, Richard Nock
2020	J	`jnl`	Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers. CoRR Alexander Soen, Hisham Husain, Richard Nock
2020	J	`jnl`	Generalised Lipschitz Regularisation Equals Distributional Robustness. CoRR Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
2020	A	`conf`	Local Differential Privacy for Sampling. AISTATS Hisham Husain, Borja Balle, Zac Cranko, Richard Nock
2020	—	`conf`	On Modulating the Gradient for Meta-learning. ECCV (8) Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
2020	J	`jnl`	SMINT: Toward Interpretable and Robust Model Sharing for Deep Neural Networks. ACM Trans. Web Huijun Wu, Chen Wang, Richard Nock, Wei Wang, Jie Yin, Kai Lu, Liming Zhu
2020	—	`ch.`	Statistical Computational Learning. A Guided Tour of Artificial Intelligence Research (1) (I) Antoine Cornuéjols, Frédéric Koriche, Richard Nock
2020	J	`jnl`	Supervised Learning: No Loss No Cry. CoRR Richard Nock, Aditya Krishna Menon
2020	A*	`conf`	Supervised learning: no loss no cry. ICML Richard Nock, Aditya Krishna Menon
2019	A*	`conf`	A Primal-Dual link between GANs and Autoencoders. NeurIPS Hisham Husain, Richard Nock, Robert C. Williamson
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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, 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, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, 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
2019	J	`jnl`	Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. CoRR Hisham Husain, Richard Nock, Robert C. Williamson
2019	A*	`conf`	Boosted Density Estimation Remastered. ICML Zac Cranko, Richard Nock
2019	A*	`conf`	Disentangled behavioural representations. NeurIPS Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
2019	A*	`conf`	Lossless or Quantized Boosting with Integer Arithmetic. ICML Richard Nock, Robert C. Williamson
2019	A*	`conf`	Min-Max Statistical Alignment for Transfer Learning. CVPR Samitha Herath, Mehrtash Harandi, Basura Fernando, Richard Nock
2019	A*	`conf`	Monge blunts Bayes: Hardness Results for Adversarial Training. ICML Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder
2019	J	`jnl`	New Tricks for Estimating Gradients of Expectations. CoRR Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama
2019	—	`conf`	Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia Posters Daniel Filonik, Tian Feng, Ke Sun, Richard Nock, Alex Collins, Tomasz Bednarz
2019	A*	`conf`	Siamese Networks: The Tale of Two Manifolds. ICCV Soumava Kumar Roy, Mehrtash Harandi, Richard Nock, Richard I. Hartley
2019	—	`conf`	The Bregman Chord Divergence. GSI Frank Nielsen, Richard Nock
2018	J	`jnl`	Boosted Density Estimation Remastered. CoRR Zac Cranko, Richard Nock
2018	J	`jnl`	D-PAGE: Diverse Paraphrase Generation. CoRR Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock
2018	J	`jnl`	Entity Resolution and Federated Learning get a Federated Resolution. CoRR Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne
2018	—	`conf`	Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds. ECML/PKDD (2) Kelvin Hsu, Richard Nock, Fabio Ramos
2018	J	`jnl`	Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds. CoRR Kelvin Hsu, Richard Nock, Fabio Ramos
2018	J	`jnl`	Integral Privacy for Density Estimation with Approximation Guarantees. CoRR Hisham Husain, Zac Cranko, Richard Nock
2018	J	`jnl`	Lipschitz Networks and Distributional Robustness. CoRR Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock
2018	J	`jnl`	Monge beats Bayes: Hardness Results for Adversarial Training. CoRR Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder
2018	Misc	`conf`	On the Geometry of Mixtures of Prescribed Distributions. ICASSP Frank Nielsen, Richard Nock
2018	J	`jnl`	Private Text Classification. CoRR Leif W. Hanlen, Richard Nock, Hanna Suominen, Neil Bacon
2018	A*	`conf`	Representation Learning of Compositional Data. NeurIPS Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
2018	J	`jnl`	The Bregman chord divergence. CoRR Frank Nielsen, Richard Nock
2018	A*	`conf`	Variational Network Inference: Strong and Stable with Concrete Support. ICML Amir Dezfouli, Edwin V. Bonilla, Richard Nock
2017	—	`conf`	Bregman Divergences from Comparative Convexity. GSI Frank Nielsen, Richard Nock
2017	J	`jnl`	Distribution-free Evolvability of Vector Spaces: All it takes is a Generating Set. CoRR Richard Nock, Frank Nielsen
2017	J	`jnl`	Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means. CoRR Frank Nielsen, Richard Nock
2017	J	`jnl`	Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity. IEEE Signal Process. Lett. Frank Nielsen, Richard Nock
2017	A*	`conf`	Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. CVPR Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
2017	J	`jnl`	MaxEnt Upper Bounds for the Differential Entropy of Univariate Continuous Distributions. IEEE Signal Process. Lett. Frank Nielsen, Richard Nock
2017	J	`jnl`	On w-mixtures: Finite convex combinations of prescribed component distributions. CoRR Frank Nielsen, Richard Nock
2017	J	`jnl`	Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne
2017	J	`jnl`	Semi-parametric Network Structure Discovery Models. CoRR Amir Dezfouli, Edwin V. Bonilla, Richard Nock
2017	A*	`conf`	Tsallis Regularized Optimal Transport and Ecological Inference. AAAI Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
2017	—	`conf`	f-GANs in an Information Geometric Nutshell. NIPS Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
2017	J	`jnl`	f-GANs in an Information Geometric Nutshell. CoRR Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
2016	—	`conf`	A scaled Bregman theorem with applications. NIPS Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
2016	J	`jnl`	A scaled Bregman theorem with applications. CoRR Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
2016	J	`jnl`	A series of maximum entropy upper bounds of the differential entropy. CoRR Frank Nielsen, Richard Nock
2016	B	`conf`	Classification with mixtures of curved mahalanobis metrics. ICIP Frank Nielsen, Boris Muzellec, Richard Nock
2016	J	`jnl`	Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices. CoRR Frank Nielsen, Richard Nock
2016	A*	`conf`	Fast Learning from Distributed Datasets without Entity Matching. IJCAI Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
2016	J	`jnl`	Fast Learning from Distributed Datasets without Entity Matching. CoRR Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
2016	J	`jnl`	Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances. CoRR Frank Nielsen, Boris Muzellec, Richard Nock
2016	A*	`conf`	Loss factorization, weakly supervised learning and label noise robustness. ICML Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
2016	J	`jnl`	Loss factorization, weakly supervised learning and label noise robustness. CoRR Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
2016	J	`jnl`	Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
2016	J	`jnl`	On Conformal Divergences and Their Population Minimizers. IEEE Trans. Inf. Theory Richard Nock, Frank Nielsen, Shun-ichi Amari
2016	—	`conf`	On Regularizing Rademacher Observation Losses. NIPS Richard Nock
2016	B	`conf`	Patch Matching with Polynomial Exponential Families and Projective Divergences. SISAP Frank Nielsen, Richard Nock
2016	J	`jnl`	The Crossover Process: Learnability meets Protection from Inference Attacks. CoRR Richard Nock, Giorgio Patrini, Finnian Lattimore, Tibério S. Caetano
2016	J	`jnl`	Tsallis Regularized Optimal Transport and Ecological Inference. CoRR Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
2016	A*	`conf`	k-variates++: more pluses in the k-means++. ICML Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen
2016	J	`jnl`	k-variates++: more pluses in the k-means++. CoRR Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen
2015	J	`jnl`	Gentle Nearest Neighbors Boosting over Proper Scoring Rules. IEEE Trans. Pattern Anal. Mach. Intell. Richard Nock, Wafa Bel Haj Ali, Roberto D'Ambrosio, Frank Nielsen, Michel Barlaud
2015	J	`jnl`	Learning Games and Rademacher Observations Losses. CoRR Richard Nock
2015	A*	`conf`	Rademacher Observations, Private Data, and Boosting. ICML Richard Nock, Giorgio Patrini, Arik Friedman
2015	J	`jnl`	Rademacher Observations, Private Data, and Boosting. CoRR Richard Nock, Giorgio Patrini, Arik Friedman
2015	J	`jnl`	Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana. Comput. Geosci. Meriam Bayoudh, Emmanuel Roux, Gilles Richard, Richard Nock
2015	Misc	`conf`	Total Jensen divergences: Definition, properties and clustering. ICASSP Frank Nielsen, Richard Nock
2014	—	`conf`	(Almost) No Label No Cry. NIPS Giorgio Patrini, Richard Nock, Tibério S. Caetano, Paul Rivera
2014	J	`jnl`	A note on the optimal scalar Bregman k-means clustering with an application to learning best statistical mixtures. CoRR Frank Nielsen, Richard Nock
2014	B	`conf`	Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification. ICPR Wafa Bel Haj Ali, Richard Nock, Michel Barlaud
2014	J	`jnl`	Further heuristics for $k$-means: The merge-and-split heuristic and the $(k, l)$-means. CoRR Frank Nielsen, Richard Nock
2014	J	`jnl`	Further results on the hyperbolic Voronoi diagrams. CoRR Frank Nielsen, Richard Nock
2014	J	`jnl`	On Clustering Histograms with Entropy Frank Nielsen, Richard Nock, Shun-ichi Amari
2014	J	`jnl`	On the Chi Square and Higher-Order Chi Distances for Approximating $f$ -Divergences. IEEE Signal Process. Lett. Frank Nielsen, Richard Nock
2014	J	`jnl`	Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning. IEEE Signal Process. Lett. Frank Nielsen, Richard Nock
2014	A*	`conf`	Visualizing hyperbolic Voronoi diagrams. SoCG Frank Nielsen, Richard Nock
2013	—	`ch.`	Boosting Advanced Topics in Computer Vision Paolo Piro, Richard Nock, Wafa Bel Haj Ali
2013	J	`jnl`	Combining Feature and Prototype Pruning by Uncertainty Minimization CoRR Marc Sebban, Richard Nock
2013	—	`conf`	Consensus Region Merging for Image Segmentation. ACPR Frank Nielsen, Richard Nock
2013	—	`conf`	Demonstrator of a Tourist Recommendation System. BDA Erol Elisabeth, Richard Nock, Fred Celimene
2013	—	`conf`	Information-geometric lenses for multiple foci+contexts interfaces. SIGGRAPH ASIA Technical Briefs Richard Nock, Frank Nielsen
2013	—	`conf`	Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries. JCDL Richard Nock, Frank Nielsen, Eric Briys
2013	J	`jnl`	On conformal divergences and their population minimizers. CoRR Richard Nock, Frank Nielsen, Shun-ichi Amari
2013	J	`jnl`	On the Chi square and higher-order Chi distances for approximating f-divergences. CoRR Frank Nielsen, Richard Nock
2013	J	`jnl`	Total Jensen divergences: Definition, Properties and k-Means++ Clustering. CoRR Frank Nielsen, Richard Nock
2012	—	`conf`	Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability. MLMI Roberto D'Ambrosio, Wafa Bel Haj Ali, Richard Nock, Paolo Soda, Frank Nielsen, Michel Barlaud
2012	—	`conf`	Boosting Nearest Neighbors for the Efficient Estimation of Posteriors. ECML/PKDD (1) Roberto D'Ambrosio, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen, Michel Barlaud
2012	J	`jnl`	Boosting k-NN for Categorization of Natural Scenes. Int. J. Comput. Vis. Richard Nock, Paolo Piro, Frank Nielsen, Wafa Bel Haj Ali, Michel Barlaud
2012	B	`conf`	Classification of biological cells using bio-inspired descriptors. ICPR Wafa Bel Haj Ali, Dario Giampaglia, Michel Barlaud, Paolo Piro, Richard Nock, Thierry Pourcher
2012	J	`jnl`	Leveraging k-NN for generic classification boosting. Neurocomputing Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud
2012	J	`jnl`	The hyperbolic Voronoi diagram in arbitrary dimension CoRR Frank Nielsen, Richard Nock
2011	J	`jnl`	A closed-form expression for the Sharma-Mittal entropy of exponential families CoRR Frank Nielsen, Richard Nock
2011	J	`jnl`	Income distributions and decomposable divergence measures. J. Econ. Theory Brice Magdalou, Richard Nock
2011	J	`jnl`	Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms CoRR Richard Nock
2011	J	`jnl`	On Rényi and Tsallis entropies and divergences for exponential families CoRR Frank Nielsen, Richard Nock
2011	A*	`conf`	On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive. ICML Richard Nock, Brice Magdalou, Eric Briys, Frank Nielsen
2011	J	`jnl`	Skew Jensen-Bregman Voronoi Diagrams. Trans. Comput. Sci. Frank Nielsen, Richard Nock
2010	B	`conf`	Boosting Bayesian MAP Classification. ICPR Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud
2010	J	`jnl`	Boosting k-NN for categorization of natural scenes CoRR Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud
2010	J	`jnl`	Bregman Voronoi Diagrams. Discret. Comput. Geom. Jean-Daniel Boissonnat, Frank Nielsen, Richard Nock
2010	B	`conf`	Entropies and cross-entropies of exponential families. ICIP Frank Nielsen, Richard Nock
2010	Misc	`conf`	Hierarchical Gaussian Mixture Model. ICASSP Vincent Garcia, Frank Nielsen, Richard Nock
2010	—	`conf`	Hyperbolic Voronoi Diagrams Made Easy. ICCSA Workshops Frank Nielsen, Richard Nock
2010	—	`conf`	Jensen-Bregman Voronoi Diagrams and Centroidal Tessellations. ISVD Frank Nielsen, Richard Nock
2010	—	`conf`	K-NN boosting prototype learning for object classification. WIAMIS Paolo Piro, Michel Barlaud, Richard Nock, Frank Nielsen
2010	—	`conf`	Multi-class Leveraged ACCV (3) Paolo Piro, Richard Nock, Frank Nielsen, Michel Barlaud
2009	J	`jnl`	Approximating Smallest Enclosing Balls with Applications to Machine Learning. Int. J. Comput. Geom. Appl. Frank Nielsen, Richard Nock
2009	J	`jnl`	Bregman Divergences and Surrogates for Learning. IEEE Trans. Pattern Anal. Mach. Intell. Richard Nock, Frank Nielsen
2009	J	`jnl`	Hyperbolic Voronoi diagrams made easy CoRR Frank Nielsen, Richard Nock
2009	J	`jnl`	Information geometries and Microeconomic Theories CoRR Richard Nock, Brice Magdalou, Nicolas Sanz, Eric Briys, Fred Celimene, Frank Nielsen
2009	—	`conf`	Levels of Details for Gaussian Mixture Models. ACCV (2) Vincent Garcia, Frank Nielsen, Richard Nock
2009	J	`jnl`	Sided and symmetrized Bregman centroids. IEEE Trans. Inf. Theory Frank Nielsen, Richard Nock
2009	—	`conf`	Simplifying Gaussian mixture models via entropic quantization. EUSIPCO Frank Nielsen, Vincent Garcia, Richard Nock
2009	J	`jnl`	Soft memberships for spectral clustering, with application to permeable language distinction. Pattern Recognit. Richard Nock, Pascal Vaillant, Claudia Henry, Frank Nielsen
2009	—	`conf`	The Dual Voronoi Diagrams with Respect to Representational Bregman Divergences. ISVD Frank Nielsen, Richard Nock
2008	J	`jnl`	Analyse spectrale des textes: détection automatique des frontières de langue et de discours CoRR Pascal Vaillant, Richard Nock, Claudia Henry
2008	B	`conf`	Bregman sided and symmetrized centroids. ICPR Frank Nielsen, Richard Nock
2008	—	`conf`	Clustering Multivariate Normal Distributions. ETVC Frank Nielsen, Richard Nock
2008	—	`conf`	Intrinsic Geometries in Learning. ETVC Richard Nock, Frank Nielsen
2008	—	`conf`	Mixed Bregman Clustering with Approximation Guarantees. ECML/PKDD (2) Richard Nock, Panu Luosto, Jyrki Kivinen
2008	—	`conf`	On the Efficient Minimization of Classification Calibrated Surrogates. NIPS Richard Nock, Frank Nielsen
2008	B	`conf`	On the efficient minimization of convex surrogates in supervised learning. ICPR Richard Nock, Frank Nielsen
2008	J	`jnl`	On the smallest enclosing information disk. Inf. Process. Lett. Frank Nielsen, Richard Nock
2008	B	`conf`	Quantum Voronoi diagrams and Holevo channel capacity for 1-qubit quantum states. ISIT Frank Nielsen, Richard Nock
2008	J	`jnl`	Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm CoRR Richard Nock, Pascal Vaillant, Frank Nielsen, Claudia Henry
2008	J	`jnl`	Staring at Economic Aggregators through Information Lenses CoRR Richard Nock, Nicolas Sanz, Fred Celimene, Frank Nielsen
2007	J	`jnl`	A Real generalization of discrete AdaBoost. Artif. Intell. Richard Nock, Frank Nielsen
2007	J	`jnl`	Bregman Voronoi Diagrams: Properties, Algorithms and Applications CoRR Frank Nielsen, Jean-Daniel Boissonnat, Richard Nock
2007	Misc	`conf`	Fast Graph Segmentation Based on Statistical Aggregation Phenomena. MVA Frank Nielsen, Richard Nock
2007	J	`jnl`	Mining evolving data streams for frequent patterns. Pattern Recognit. Pierre-Alain Laur, Richard Nock, Jean-Emile Symphor, Pascal Poncelet
2007	A*	`conf`	On Bregman Voronoi diagrams. SODA Frank Nielsen, Jean-Daniel Boissonnat, Richard Nock
2007	J	`jnl`	On the Centroids of Symmetrized Bregman Divergences CoRR Frank Nielsen, Richard Nock
2007	A*	`conf`	Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree. IJCAI Claudia Henry, Richard Nock, Frank Nielsen
2007	J	`jnl`	Self-improved gaps almost everywhere for the agnostic approximation of monomials. Theor. Comput. Sci. Richard Nock, Frank Nielsen
2007	J	`jnl`	Statistical supports for mining sequential patterns and improving the incremental update process on data streams. Intell. Data Anal. Pierre-Alain Laur, Jean-Emile Symphor, Richard Nock, Pascal Poncelet
2007	—	`conf`	Visualizing bregman voronoi diagrams. SCG Frank Nielsen, Jean-Daniel Boissonnat, Richard Nock
2006	A	`conf`	A Real Generalization of Discrete AdaBoost. ECAI Richard Nock, Frank Nielsen
2006	—	`conf`	Analyse spectrale des textes : détection automatique des frontières de langue et de discours. TALN (Posters) Pascal Vaillant, Richard Nock, Claudia Henry
2006	—	`conf`	Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization. Canadian AI Svetlana Kiritchenko, Stan Matwin, Richard Nock, A. Fazel Famili
2006	J	`jnl`	On Weighting Clustering. IEEE Trans. Pattern Anal. Mach. Intell. Richard Nock, Frank Nielsen
2006	—	`conf`	On approximating the smallest enclosing Bregman Balls. SCG Frank Nielsen, Richard Nock
2006	—	`conf`	On the Smallest Enclosing Information Disk. CCCG Frank Nielsen, Richard Nock
2006	—	`conf`	Robust Multiclass Ensemble Classifiers via Symmetric Functions. ICPR (4) Patrice Lefaucheur, Richard Nock
2006	A	`conf`	Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm. ECAI Richard Nock, Pascal Vaillant, Frank Nielsen, Claudia Henry
2006	—	`conf`	Statistical Borders for Incremental Mining. ICPR (3) Richard Nock, Pierre-Alain Laur, Jean-Emile Symphor
2005	J	`jnl`	A fast deterministic smallest enclosing disk approximation algorithm. Inf. Process. Lett. Frank Nielsen, Richard Nock
2005	J	`jnl`	Adaptation du boosting à l'inférence grammaticale Rev. d'Intelligence Artif. Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier
2005	—	`conf`	Adaptive filtering of advertisements on web pages. WWW (Special interest tracks and posters) Babak Esfandiari, Richard Nock
2005	A*	`conf`	ClickRemoval: interactive pinpoint image object removal. ACM Multimedia Frank Nielsen, Richard Nock
2005	—	`conf`	Fitting the Smallest Enclosing Bregman Ball. ECML Richard Nock, Frank Nielsen
2005	—	`conf`	Interactive Pinpoint Image Object Removal. CVPR (2) Frank Nielsen, Richard Nock
2005	—	`conf`	Interactive Point-and-Click Segmentation for Object Removal in Digital Images. ICCV-HCI Frank Nielsen, Richard Nock
2005	A	`conf`	On the estimation of frequent itemsets for data streams: theory and experiments. CIKM Pierre-Alain Laur, Richard Nock, Jean-Emile Symphor, Pascal Poncelet
2005	—	`conf`	On-Line Adaptive Filtering of Web Pages. PKDD Richard Nock, Babak Esfandiari
2005	J	`jnl`	Semi-supervised statistical region refinement for color image segmentation. Pattern Recognit. Richard Nock, Frank Nielsen
2005	—	`conf`	Statistical Supports for Frequent Itemsets on Data Streams. MLDM Pierre-Alain Laur, Jean-Emile Symphor, Richard Nock, Pascal Poncelet
2004	A	`conf`	An Abstract Weighting Framework for Clustering Algorithms. SDM Richard Nock, Frank Nielsen
2004	—	`conf`	Approximating Smallest Enclosing Balls. ICCSA (3) Frank Nielsen, Richard Nock
2004	—	`conf`	Approximating smallest enclosing disks. CCCG Frank Nielsen, Richard Nock
2004	A*	`conf`	Boosting grammatical inference with confidence oracles. ICML Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier
2004	—	`conf`	Grouping with Bias Revisited. CVPR (2) Richard Nock, Frank Nielsen
2004	—	`conf`	Grouping with Bias for Distribution-Free Mixture Model Estimation. ICPR (2) Richard Nock, Vincent Pagé
2004	—	`conf`	Improving Clustering Algorithms through Constrained Convex Optimization. ICPR (4) Richard Nock, Frank Nielsen
2004	J	`jnl`	On domain-partitioning induction criteria: worst-case bounds for the worst-case based. Theor. Comput. Sci. Richard Nock, Frank Nielsen
2004	J	`jnl`	Statistical Region Merging. IEEE Trans. Pattern Anal. Mach. Intell. Richard Nock, Frank Nielsen
2003	J	`jnl`	A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting. Int. J. Pattern Recognit. Artif. Intell. Richard Nock, Marc Sebban, Didier Bernard
2003	J	`jnl`	Complexity in the case against accuracy estimation. Theor. Comput. Sci. Richard Nock
2003	—	`conf`	On Region Merging: The Statistical Soundness of Fast Sorting, with Applications. CVPR (2) Frank Nielsen, Richard Nock
2003	J	`jnl`	Reduced Error Pruning of branching programs cannot be approximated to within a logarithmic factor. Inf. Process. Lett. Richard Nock, Tapio Elomaa, Matti Kääriäinen
2002	—	`conf`	A Robust Boosting Algorithm. ECML Richard Nock, Patrice Lefaucheur
2002	J	`jnl`	A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recognit. Marc Sebban, Richard Nock
2002	J	`jnl`	Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments. J. Artif. Intell. Res. Richard Nock
2002	J	`jnl`	Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. J. Mach. Learn. Res. Marc Sebban, Richard Nock, Stéphane Lallich
2001	J	`jnl`	A Bayesian boosting theorem. Pattern Recognit. Lett. Richard Nock, Marc Sebban
2001	J	`jnl`	Advances in Adaptive Prototype Weighting and Selection. Int. J. Artif. Intell. Tools Richard Nock, Marc Sebban
2001	J	`jnl`	An improved bound on the finite-sample risk of the nearest neighbor rule. Pattern Recognit. Lett. Richard Nock, Marc Sebban
2001	A*	`conf`	Boosting Neighborhood-Based Classifiers. ICML Marc Sebban, Richard Nock, Stéphane Lallich
2001	—	`conf`	Fast and Reliable Color Region Merging inspired by Decision Tree Pruning. CVPR (1) Richard Nock
2001	Misc	`conf`	Improvement of Nearest-Neighbor Classifiers via Support Vector Machines. FLAIRS Marc Sebban, Richard Nock
2000	Misc	`conf`	A Boosting-Based Prototype Weighting and Selection Scheme. FLAIRS Richard Nock, Marc Sebban
2000	A	`conf`	A Concentration-Based Adaptive Approach to Region Merging of Optimal Time and Space Complexities. BMVC Christophe Fiorio, Richard Nock
2000	—	`conf`	A Symmetric Nearest Neighbor Learning Rule. EWCBR Richard Nock, Marc Sebban, Pascal Jabby
2000	A	`conf`	Combining Feature and Example Pruning by Uncertainty Minimization. UAI Marc Sebban, Richard Nock
2000	—	`conf`	Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery. PKDD Marc Sebban, Richard Nock
2000	Misc	`conf`	Identifying and Eliminating Irrelevant Instances Using Information Theory. AI Marc Sebban, Richard Nock
2000	J	`jnl`	Impact of learning set quality and size on decision tree performances. Int. J. Comput. Syst. Signals Marc Sebban, Richard Nock, Jean-Hugues Chauchat, Ricco Rakotomalala
2000	A*	`conf`	Instance Pruning as an Information Preserving Problem. ICML Marc Sebban, Richard Nock
2000	B	`conf`	Sharper Bounds for the Hardness of Prototype and Feature Selection. ALT Richard Nock, Marc Sebban
2000	B	`conf`	Sorted Region Merging to Maximize Test Reliability. ICIP Christophe Fiorio, Richard Nock
1999	B	`conf`	A "Top-Down and Prune" Induction Scheme for Constrained Decision Committees. IDA Richard Nock, Pascal Jappy
1999	B	`conf`	Complexity in the Case against Accuracy: When Building one Function-Free Horn Clause is as Hard as Any. ALT Richard Nock
1999	—	`conf`	Contribution of Boosting in Wrapper Models. PKDD Marc Sebban, Richard Nock
1999	J	`jnl`	Decision tree based induction of decision lists. Intell. Data Anal. Richard Nock, Pascal Jappy
1999	—	`conf`	Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme. PKDD Richard Nock, Marc Sebban, Pascal Jappy
1998	B	`conf`	Function-Free Horn Clauses Are Hard to Approximate. ILP Richard Nock, Pascal Jappy
1998	B	`conf`	Generalized Graph Colorability and Compressibility of Boolean Formulae. ISAAC Richard Nock, Pascal Jappy, Jean Sallantin
1998	B	`conf`	Image segmentation using a generic, fast and non-parametric approach. ICTAI Christophe Fiorio, Richard Nock
1998	A*	`conf`	On the Power of Decision Lists. ICML Richard Nock, Pascal Jappy
1998	—	`conf`	Oracles and Assistants: Machine Learning Applied to Network Supervision. Canadian AI Richard Nock, Babak Esfandiari
1998	Misc	`conf`	PAC Learning Conceptual Graphs. ICCS Pascal Jappy, Richard Nock
1998	J	`jnl`	Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods. Int. J. Pattern Recognit. Artif. Intell. Olivier Gascuel, Bernadette Bouchon-Meunier, Gilles Caraux, Patrick Gallinari, Alain Guénoche, Yann Guermeur, Yves Lechevallier, Christophe Marsala, Laurent Miclet, Jacques Nicolas, Richard Nock, Mohammed Ramdani, Michèle Sebag, Basavanneppa Tallur, Gilles Venturini, Patrick Vitte
1996	A*	`conf`	Negative Robust Learning Results from Horn Claus Programs. ICML Pascal Jappy, Richard Nock, Olivier Gascuel
1995	A*	`conf`	On Learning Decision Committees. ICML Richard Nock, Olivier Gascuel