Richard Nock

Richard Nock, Christophe Fiorio, R. Nock, 1998, Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294).

The Dual Voronoi Diagrams with Respect to Representational Bregman Divergences

Frank Nielsen, Richard Nock, R. Nock, 2009, 2009 Sixth International Symposium on Voronoi Diagrams.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions

Samik Ghosh, Hiroaki Kitano, Frank Nielsen, 2020, PloS one.

Boosting k-NN for Categorization of Natural Scenes

Frank Nielsen, Richard Nock, Michel Barlaud, 2010, International Journal of Computer Vision.

Boosting Neighborhood-Based Classifiers

Richard Nock, Marc Sebban, Stéphane Lallich, 2001, ICML.

Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

Richard Nock, Alessandro Rozza, Lizhen Qu, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Lipschitz Networks and Distributional Robustness pdf

Richard Nock, Zhan Shi, Simon Kornblith, 2018, ArXiv.

D-PAGE: Diverse Paraphrase Generation pdf

Richard Nock, Qiongkai Xu, Lizhen Qu, 2018, ArXiv.

The hyperbolic Voronoi diagram in arbitrary dimension pdf

Frank Nielsen, Richard Nock, R. Nock, 2012, ArXiv.

Fitting the Smallest Enclosing Bregman Ball

Frank Nielsen, Richard Nock, R. Nock, 2005, ECML.

Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means pdf

Frank Nielsen, Richard Nock, 2017, ArXiv.

Learning Games and Rademacher Observations Losses pdf

Richard Nock, R. Nock, 2015, ArXiv.

Non-Euclidean Embeddings for Graph Analytics and Visualisation

Richard Nock, Tomasz Bednarz, Ke Sun, 2019, SIGGRAPH Asia Posters.

Statistical Computational Learning

Richard Nock, Antoine Cornuéjols, Frédéric Koriche, 2020, A Guided Tour of Artificial Intelligence Research.

Variational Network Inference: Strong and Stable with Concrete Support

Richard Nock, Edwin V. Bonilla, Amir Dezfouli, 2018, ICML.

Statistical region merging

Frank Nielsen, Richard Nock, R. Nock, 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Reduced Error Pruning of branching programs cannot be approximated to within a logarithmic factor

Richard Nock, Tapio Elomaa, Matti Kääriäinen, 2003, Inf. Process. Lett..

Supervised Learning: No Loss No Cry

Richard Nock, Aditya Krishna Menon, 2020, ICML.

Integral Privacy for Sampling from Mollifier Densities with Approximation Guarantees

Richard Nock, Hisham Husain, Zac Cranko, 2018 .

A scaled Bregman theorem with applications

Richard Nock, Cheng Soon Ong, Aditya Krishna Menon, 2016, NIPS.

On weighting clustering

Frank Nielsen, Richard Nock, R. Nock, 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

f-GANs in an Information Geometric Nutshell

Richard Nock, Lizhen Qu, Aditya Krishna Menon, 2017, NIPS.

A hybrid filter/wrapper approach of feature selection using information theory

Richard Nock, Marc Sebban, R. Nock, 2002, Pattern Recognit..

A "Top-Down and Prune" Induction Scheme for Constrained Decision Committees

Richard Nock, Pascal Jappy, R. Nock, 1999, IDA.

Boosting Nearest Neighbors for the Efficient Estimation of Posteriors

Frank Nielsen, Richard Nock, Michel Barlaud, 2012, ECML/PKDD.

Skew Jensen-Bregman Voronoi Diagrams

Frank Nielsen, Richard Nock, Frank Nielsen, 2011, Trans. Comput. Sci..

Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem

Richard Nock, Marc Sebban, Stéphane Lallich, 2003, J. Mach. Learn. Res..

Integral Privacy for Density Estimation with Approximation Guarantees pdf

Richard Nock, Hisham Husain, Zac Cranko, 2018, ArXiv.

Bregman Divergences from Comparative Convexity

Frank Nielsen, Richard Nock, 2017, GSI.

Automatic learning of structural knowledge from geographic information for updating land cover maps

Richard Nock, Gilles Richard, Emmanuel Roux, 2012 .

Classification with mixtures of curved mahalanobis metrics

Frank Nielsen, Richard Nock, Boris Muzellec, 2016, 2016 IEEE International Conference on Image Processing (ICIP).

Total Jensen divergences: Definition, Properties and k-Means++ Clustering pdf

Frank Nielsen, Richard Nock, 2013, ArXiv.

Boosting k-Nearest Neighbors Classification

Richard Nock, Paolo Piro, Wafa Bel Haj Ali, 2013, Advanced Topics in Computer Vision.

Representation Learning of Compositional Data

Richard Nock, Cheng Soon Ong, Ke Sun, 2018, NeurIPS.

Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme

Richard Nock, Marc Sebban, Pascal Jappy, 1999, PKDD.

Sharper Bounds for the Hardness of Prototype and Feature Selection

Richard Nock, Marc Sebban, R. Nock, 2000, ALT.

Approximating smallest enclosing disks

Frank Nielsen, Richard Nock, 2004, CCCG.

Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms pdf

Richard Nock, R. Nock, 2002, J. Artif. Intell. Res..

Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family pdf

Frank Nielsen, Richard Nock, 2020, ArXiv.

On the Centroids of Symmetrized Bregman Divergences pdf

Frank Nielsen, Richard Nock, 2007, ArXiv.

A Boosting-Based Prototype Weighting and Selection Scheme

Richard Nock, Marc Sebban, R. Nock, 2000, FLAIRS.

2014 IEEE Aerospace Conference

Richard Nock, Naim Dahnoun, Gerhard Ehret, 2014 .

Multi-class Leveraged κ-NN for Image Classification

Frank Nielsen, Richard Nock, Michel Barlaud, 2010, ACCV.

Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization

Stan Matwin, Richard Nock, Svetlana Kiritchenko, 2006, Canadian AI.

Analyse spectrale des textes: détection automatique des frontières de langue et de discours pdf

Richard Nock, Claudia Henry, Pascal Vaillant, 2008, ArXiv.

Sorted region merging to maximize test reliability

Richard Nock, Christophe Fiorio, 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

Self-improved gaps almost everywhere for the agnostic approximation of monomials

Frank Nielsen, Richard Nock, 2007, Theor. Comput. Sci..

On Clustering Histograms with k-Means by Using Mixed α-Divergences

Frank Nielsen, Richard Nock, Shun-ichi Amari, 2014, Entropy.

Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability

Frank Nielsen, Richard Nock, Michel Barlaud, 2012, MLMI.

Statistical Supports for Frequent Itemsets on Data Streams

Richard Nock, Pascal Poncelet, Jean-Emile Symphor, 2005, MLDM.

Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity

Frank Nielsen, Richard Nock, R. Nock, 2017, IEEE Signal Processing Letters.

Bregman sided and symmetrized centroids

Frank Nielsen, Richard Nock, 2008, 2008 19th International Conference on Pattern Recognition.

A closed-form expression for the Sharma–Mittal entropy of exponential families pdf

Frank Nielsen, Richard Nock, R. Nock, 2011, ArXiv.

MaxEnt Upper Bounds for the Differential Entropy of Univariate Continuous Distributions

Frank Nielsen, Richard Nock, R. Nock, 2017, IEEE Signal Processing Letters.

On the Smallest Enclosing Information Disk

Frank Nielsen, Richard Nock, R. Nock, 2008, CCCG.

On the estimation of frequent itemsets for data streams: theory and experiments

Richard Nock, Pascal Poncelet, Jean-Emile Symphor, 2005, CIKM '05.

Gentle Nearest Neighbors Boosting over Proper Scoring Rules

Frank Nielsen, Richard Nock, Michel Barlaud, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

On the Efficient Minimization of Classification Calibrated Surrogates

Frank Nielsen, Richard Nock, R. Nock, 2008, NIPS.

Lossless or Quantized Boosting with Integer Arithmetic

Richard Nock, Robert C. Williamson, R. C. Williamson, 2019, ICML.

On Regularizing Rademacher Observation Losses

Richard Nock, 2016, NIPS.

Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning

Frank Nielsen, Richard Nock, R. Nock, 2014, IEEE Signal Processing Letters.

Statistical supports for mining sequential patterns and improving the incremental update process on data streams

Richard Nock, Pascal Poncelet, Jean-Emile Symphor, 2007, Intell. Data Anal..

On the chi square and higher-order chi distances for approximating f-divergences

Frank Nielsen, Richard Nock, R. Nock, 2013, IEEE Signal Processing Letters.

Fast Learning from Distributed Datasets without Entity Matching

Richard Nock, Tibério S. Caetano, Stephen Hardy, 2016, IJCAI.

ClickRemoval: interactive pinpoint image object removal

Frank Nielsen, Richard Nock, 2005, MULTIMEDIA '05.

A Bayesian boosting theorem

Richard Nock, Marc Sebban, 2001, Pattern Recognit. Lett..

(Almost) No Label No Cry

Richard Nock, Tibério S. Caetano, Giorgio Patrini, 2014, NIPS.

Income distributions and decomposable divergence measures

Richard Nock, Brice Magdalou, R. Nock, 2010, J. Econ. Theory.

Decision tree based induction of decision lists

Richard Nock, Pascal Jappy, R. Nock, 1999, Intell. Data Anal..

On region merging: the statistical soundness of fast sorting, with applications

Frank Nielsen, Richard Nock, R. Nock, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

The Crossover Process: Learnability and Data Protection from Inference Attacks

Richard Nock, Giorgio Patrini, Finnian Lattimore, 2016 .

PAC Learning Conceptual Graphs

Richard Nock, Pascal Jappy, 1998, ICCS.

Leveraging Κ-nn for generic classification boosting

Frank Nielsen, Richard Nock, Michel Barlaud, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances pdf

Frank Nielsen, Richard Nock, Boris Muzellec, 2016, ArXiv.

On the Power of Decision Lists

Richard Nock, Pascal Jappy, 1998, ICML.

Improving clustering algorithms through constrained convex optimization

Frank Nielsen, Richard Nock, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Chronicle Learning and Agent oriented techniques for network management and supervision

Richard Nock, Joël Quinqueton, Babak Esfandiari, 1997 .

Further results on the hyperbolic Voronoi diagrams pdf

Frank Nielsen, Richard Nock, 2014, ArXiv.

Loss factorization, weakly supervised learning and label noise robustness

Frank Nielsen, Richard Nock, Giorgio Patrini, 2016, ICML.

On w-mixtures: Finite convex combinations of prescribed component distributions pdf

Frank Nielsen, Richard Nock, 2017, ArXiv.

Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices pdf

Frank Nielsen, Richard Nock, R. Nock, 2016, ArXiv.

Grouping with bias revisited

Frank Nielsen, Richard Nock, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification

Richard Nock, Michel Barlaud, Wafa Bel Haj Ali, 2014, 2014 22nd International Conference on Pattern Recognition.

Entropies and cross-entropies of exponential families

Frank Nielsen, Richard Nock, R. Nock, 2010, 2010 IEEE International Conference on Image Processing.

Levels of Details for Gaussian Mixture Models

Frank Nielsen, Richard Nock, Vincent Garcia, 2009, ACCV.

Boosting grammatical inference with confidence oracles

Richard Nock, Marc Sebban, Jean-Christophe Janodet, 2004, ICML '04.

Adaptive filtering of advertisements on web pages

Richard Nock, Babak Esfandiari, R. Nock, 2005, WWW '05.

Further heuristics for $k$-means: The merge-and-split heuristic and the $(k, l)$-means pdf

Frank Nielsen, Richard Nock, R. Nock, 2014, ArXiv.

Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries

Frank Nielsen, Richard Nock, Eric Briys, 2013, JCDL '13.

A Symmetric Nearest Neighbor Learning Rule

Richard Nock, Marc Sebban, Pascal Jabby, 2000, EWCBR.

Consensus Region Merging for Image Segmentation

Frank Nielsen, Richard Nock, 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

A Concentration-Based Adaptive Approach to Region Merging of Optimal Time and Space Complexities

Richard Nock, Christophe Fiorio, 2000, BMVC.

Total Jensen divergences: Definition, properties and clustering

Frank Nielsen, Richard Nock, R. Nock, 2013, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Fast Graph Segmentation Based on Statistical Aggregation Phenomena

Frank Nielsen, Richard Nock, 2007, MVA.

Function-Free Horn Clauses Are Hard to Approximate

Richard Nock, Pascal Jappy, 1998, ILP.

Intrinsic Geometries in Learning

Frank Nielsen, Richard Nock, R. Nock, 2009, ETVC.

An improved bound on the finite-sample risk of the nearest neighbor rule

Richard Nock, Marc Sebban, 2001, Pattern Recognit. Lett..

Boosted and Differentially Private Ensembles of Decision Trees pdf

Richard Nock, Wilko Henecka, 2020, ArXiv.

Proper-Composite Loss Functions in Arbitrary Dimensions pdf

Richard Nock, Robert C. Williamson, Zac Cranko, 2019, ArXiv.

Interactive Pinpoint Image Object Removal

Frank Nielsen, Richard Nock, R. Nock, 2005, CVPR.

Jensen-Bregman Voronoi Diagrams and Centroidal Tessellations

Frank Nielsen, Richard Nock, R. Nock, 2010, 2010 International Symposium on Voronoi Diagrams in Science and Engineering.

A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting

Richard Nock, Marc Sebban, Didier Bernard, 2003, Int. J. Pattern Recognit. Artif. Intell..

Tsallis Regularized Optimal Transport and Ecological Inference pdf

Frank Nielsen, Richard Nock, Giorgio Patrini, 2016, AAAI.

Contribution of Boosting in Wrapper Models

Richard Nock, Marc Sebban, R. Nock, 1999, PKDD.

The Crossover Process: Learnability meets Protection from Inference Attacks pdf

Richard Nock, Tibério S. Caetano, Giorgio Patrini, 2016, ArXiv.

Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods

Richard Nock, Bernadette Bouchon-Meunier, Christophe Marsala, 1998, Int. J. Pattern Recognit. Artif. Intell..

Entity Resolution and Federated Learning get a Federated Resolution pdf

Richard Nock, Brian Thorne, Wilko Henecka, 2018, ArXiv.

Bregman Divergences and Surrogates for Learning

Frank Nielsen, Richard Nock, R. Nock, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

On Bregman Voronoi diagrams

Richard Nock, Jean-Daniel Boissonnat, Frank Nielsen, 2007, SODA '07.

On the Geometry of Mixtures of Prescribed Distributions

Frank Nielsen, Richard Nock, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Complexity in the case against accuracy estimation

Richard Nock, 2003, Theor. Comput. Sci..

A series of maximum entropy upper bounds of the differential entropy pdf

Frank Nielsen, Richard Nock, R. Nock, 2016, ArXiv.

Robust Multiclass Ensemble Classifiers via Symmetric Functions

Richard Nock, Patrice Lefaucheur, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

Distribution-free Evolvability of Vector Spaces: All it takes is a Generating Set pdf

Frank Nielsen, Richard Nock, R. Nock, 2017, ArXiv.

Adaptation du boosting à l'inférence grammaticale via l'utilisation d'un oracle de confiance

Richard Nock, Marc Sebban, Jean-Christophe Janodet, 2005, Rev. d'Intelligence Artif..

Combining Feature and Example Pruning by Uncertainty Minimization

Richard Nock, Marc Sebban, R. Nock, 2000, UAI.

Approximating Smallest Enclosing Balls with Applications to Machine Learning

Frank Nielsen, Richard Nock, R. Nock, 2009, Int. J. Comput. Geom. Appl..

Boosting Bayesian MAP Classification

Frank Nielsen, Richard Nock, Michel Barlaud, 2010, 2010 20th International Conference on Pattern Recognition.

Fast and reliable color region merging inspired by decision tree pruning

Richard Nock, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Approximating Smallest Enclosing Balls

Frank Nielsen, Richard Nock, R. Nock, 2004, ICCSA.

Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds

Richard Nock, Fabio Tozeto Ramos, Kelvin Hsu, 2018, ECML/PKDD.

On Learning Decision Committees

Richard Nock, Olivier Gascuel, 1995, ICML.

Combining Feature and Prototype Pruning by Uncertainty Minimization pdf

Richard Nock, Marc Sebban, R. Nock, 2013, ArXiv.

Semi-parametric Network Structure Discovery Models pdf

Richard Nock, Edwin V. Bonilla, Amir Dezfouli, 2017, ArXiv.

Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm

Frank Nielsen, Richard Nock, Claudia Henry, 2006, ECAI.

Bregman Voronoi Diagrams pdf

Frank Nielsen, Richard Nock, Jean-Daniel Boissonnat, 2007, Discret. Comput. Geom..

Semi-supervised statistical region refinement for color image segmentation

Frank Nielsen, Richard Nock, 2005, Pattern Recognit..

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption pdf

Richard Nock, Brian Thorne, Wilko Henecka, 2017, ArXiv.

Boosted Density Estimation Remastered

Richard Nock, Zac Cranko, R. Nock, 2018, ICML.

Advances and Open Problems in Federated Learning

Richard Nock, Zaïd Harchaoui, Mehdi Bennis, 2021, Found. Trends Mach. Learn..

Manifold Learning Benefits GANs pdf

Richard Nock, Piotr Koniusz, Richard Hartley, 2021, ArXiv.

Being Properly Improper pdf

Richard Nock, Lalitha Sankar, Tyler Sypherd, 2021, ArXiv.

Computing Statistical Divergences with Sigma Points

Richard Nock, Frank Nielsen, R. Nock, 2021, GSI.

Generative Trees: Adversarial and Copycat

Richard Nock, Mathieu Guillame-Bert, R. Nock, 2022, ArXiv.

The Impact of Record Linkage on Learning from Feature Partitioned Data

Richard Nock, Wilko Henecka, Giorgio Patrini, 2021, ICML.

Advances in Adaptive Prototype Weighting and Selection

Richard Nock, Marc Sebban, 2001, Int. J. Artif. Intell. Tools.

( Almost ) No Label No Cry-Supplementary Material

Richard Nock, Giorgio Patrini, Paul Rivera, 2015 .

Machines that Learn with Limited or No Supervision: A Survey on Deep Learning Based Techniques

Richard Nock, Fatih Porikli, Samitha Herath, 2017 .

Fitting the smallest enclosing Bregman balls

Frank Nielsen, Richard Nock, R. Nock, 2005 .

Deep Subspace Networks For Few-Shot Learning

Richard Nock, Mehrtash Harandi, Piotr Koniusz, 2019 .

On the Centroids of Symmetrized Bregman Divergences |Extended Abstract|

Frank Nielsen, Richard Nock, R. Nock, 2007 .

Mining Matrix Data with Bregman Matrix Divergences for Portfolio Selection

Frank Nielsen, Richard Nock, Brice Magdalou, 2013 .