On Learning Decision Committees. - Richard Nock; Olivier Gascuel (ICML, 1995)

Negative Robust Learning Results from Horn Claus Programs. - Pascal Jappy; Richard Nock; Olivier Gascuel (ICML, 1996)

Oracles and Assistants: Machine Learning Applied to Network Supervision. - Richard Nock; Babak Esfandiari (Canadian AI, 1998)

PAC Learning Conceptual Graphs. - Pascal Jappy; Richard Nock (ICCS, 1998)

On the Power of Decision Lists. - Richard Nock; Pascal Jappy (ICML, 1998)

Image segmentation using a generic, fast and non-parametric approach. - Christophe Fiorio; Richard Nock (ICTAI, 1998)

Function-Free Horn Clauses Are Hard to Approximate. - Richard Nock; Pascal Jappy (ILP, 1998)

Generalized Graph Colorability and Compressibility of Boolean Formulae. - Richard Nock; Pascal Jappy; Jean Sallantin (ISAAC, 1998)

Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods. - 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 003; et al. (Int. J. Pattern Recognit. Artif. Intell., 1998)

Complexity in the Case against Accuracy: When Building one Function-Free Horn Clause is as Hard as Any. - Richard Nock (ALT, 1999)

A "Top-Down and Prune" Induction Scheme for Constrained Decision Committees. - Richard Nock; Pascal Jappy (IDA, 1999)

Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme. - Richard Nock; Marc Sebban; Pascal Jappy (PKDD, 1999)

Contribution of Boosting in Wrapper Models. - Marc Sebban; Richard Nock (PKDD, 1999)

Decision tree based induction of decision lists. - Richard Nock; Pascal Jappy (Intell. Data Anal., 1999)

Identifying and Eliminating Irrelevant Instances Using Information Theory. - Marc Sebban; Richard Nock (AI, 2000)

Sharper Bounds for the Hardness of Prototype and Feature Selection. - Richard Nock; Marc Sebban (ALT, 2000)

A Concentration-Based Adaptive Approach to Region Merging of Optimal Time and Space Complexities. - Christophe Fiorio; Richard Nock (BMVC, 2000)

A Symmetric Nearest Neighbor Learning Rule. - Richard Nock; Marc Sebban; Pascal Jabby (EWCBR, 2000)

A Boosting-Based Prototype Weighting and Selection Scheme. - Richard Nock; Marc Sebban (FLAIRS, 2000)

Sorted Region Merging to Maximize Test Reliability. - Christophe Fiorio; Richard Nock (ICIP, 2000)

Instance Pruning as an Information Preserving Problem. - Marc Sebban; Richard Nock (ICML, 2000)

Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery. - Marc Sebban; Richard Nock (PKDD, 2000)

Combining Feature and Example Pruning by Uncertainty Minimization. - Marc Sebban; Richard Nock (UAI, 2000)

Impact of learning set quality and size on decision tree performances. - Marc Sebban; Richard Nock; Jean-Hugues Chauchat; Ricco Rakotomalala (Int. J. Comput. Syst. Signals, 2000)

Fast and Reliable Color Region Merging inspired by Decision Tree Pruning. - Richard Nock (CVPR (1), 2001)

Improvement of Nearest-Neighbor Classifiers via Support Vector Machines. - Marc Sebban; Richard Nock (FLAIRS, 2001)

Boosting Neighborhood-Based Classifiers. - Marc Sebban; Richard Nock; Stéphane Lallich (ICML, 2001)

Advances in Adaptive Prototype Weighting and Selection. - Richard Nock; Marc Sebban (Int. J. Artif. Intell. Tools, 2001)

An improved bound on the finite-sample risk of the nearest neighbor rule. - Richard Nock; Marc Sebban (Pattern Recognit. Lett., 2001)

A Bayesian boosting theorem. - Richard Nock; Marc Sebban (Pattern Recognit. Lett., 2001)

A Robust Boosting Algorithm. - Richard Nock; Patrice Lefaucheur (ECML, 2002)

Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments. - Richard Nock (J. Artif. Intell. Res., 2002)

Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. - Marc Sebban; Richard Nock; Stéphane Lallich (J. Mach. Learn. Res., 2002)

A hybrid filter/wrapper approach of feature selection using information theory. - Marc Sebban; Richard Nock (Pattern Recognit., 2002)

On Region Merging: The Statistical Soundness of Fast Sorting, with Applications. - Frank Nielsen; Richard Nock (CVPR (2), 2003)

A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting. - Richard Nock; Marc Sebban; Didier Bernard (Int. J. Pattern Recognit. Artif. Intell., 2003)

Reduced Error Pruning of branching programs cannot be approximated to within a logarithmic factor. - Richard Nock; Tapio Elomaa; Matti Kääriäinen (Inf. Process. Lett., 2003)

Complexity in the case against accuracy estimation. - Richard Nock (Theor. Comput. Sci., 2003)

Approximating smallest enclosing disks. - Frank Nielsen; Richard Nock (CCCG, 2004)

Grouping with Bias Revisited. - Richard Nock; Frank Nielsen (CVPR (2), 2004)

Approximating Smallest Enclosing Balls. - Frank Nielsen; Richard Nock (ICCSA (3), 2004)

Boosting grammatical inference with confidence oracles. - Jean-Christophe Janodet; Richard Nock; Marc Sebban; Henri-Maxime Suchier (ICML, 2004)

Improving Clustering Algorithms through Constrained Convex Optimization. - Richard Nock; Frank Nielsen (ICPR (4), 2004)

Grouping with Bias for Distribution-Free Mixture Model Estimation. - Richard Nock; Vincent Pagé (ICPR (2), 2004)

An Abstract Weighting Framework for Clustering Algorithms. - Richard Nock; Frank Nielsen (SDM, 2004)

Statistical Region Merging. - Richard Nock; Frank Nielsen (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

On domain-partitioning induction criteria: worst-case bounds for the worst-case based. - Richard Nock; Frank Nielsen (Theor. Comput. Sci., 2004)

On the estimation of frequent itemsets for data streams: theory and experiments. - Pierre-Alain Laur; Richard Nock; Jean-Emile Symphor; Pascal Poncelet (CIKM, 2005)

Interactive Pinpoint Image Object Removal. - Frank Nielsen; Richard Nock (CVPR (2), 2005)

Fitting the Smallest Enclosing Bregman Ball. - Richard Nock; Frank Nielsen (ECML, 2005)

Interactive Point-and-Click Segmentation for Object Removal in Digital Images. - Frank Nielsen; Richard Nock (ICCV-HCI, 2005)

Statistical Supports for Frequent Itemsets on Data Streams. - Pierre-Alain Laur; Jean-Emile Symphor; Richard Nock; Pascal Poncelet (MLDM, 2005)

ClickRemoval: interactive pinpoint image object removal. - Frank Nielsen; Richard Nock (ACM Multimedia, 2005)

On-Line Adaptive Filtering of Web Pages. - Richard Nock; Babak Esfandiari (PKDD, 2005)

Adaptive filtering of advertisements on web pages. - Babak Esfandiari; Richard Nock (WWW (Special interest tracks and posters), 2005)

A fast deterministic smallest enclosing disk approximation algorithm. - Frank Nielsen; Richard Nock (Inf. Process. Lett., 2005)

Semi-supervised statistical region refinement for color image segmentation. - Richard Nock; Frank Nielsen (Pattern Recognit., 2005)

Adaptation du boosting à l'inférence grammaticale via l'utilisation d'un oracle de confiance. - Jean-Christophe Janodet; Richard Nock; Marc Sebban; Henri-Maxime Suchier (Rev. d'Intelligence Artif., 2005)

Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization. - Svetlana Kiritchenko; Stan Matwin; Richard Nock; A. Fazel Famili (Canadian AI, 2006)

On approximating the smallest enclosing Bregman Balls. - Frank Nielsen; Richard Nock (SCG, 2006)

Soft Uncoupling of Markov Chains for Permeable Language Distinction: A New Algorithm. - Richard Nock; Pascal Vaillant; Frank Nielsen; Claudia Henry (ECAI, 2006)

Robust Multiclass Ensemble Classifiers via Symmetric Functions. - Patrice Lefaucheur; Richard Nock (ICPR (4), 2006)

Statistical Borders for Incremental Mining. - Richard Nock; Pierre-Alain Laur; Jean-Emile Symphor (ICPR (3), 2006)

Analyse spectrale des textes : détection automatique des frontières de langue et de discours. - Pascal Vaillant; Richard Nock; Claudia Henry (TALN (Posters), 2006)

On Weighting Clustering. - Richard Nock; Frank Nielsen (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

Visualizing bregman voronoi diagrams. - Frank Nielsen; Jean-Daniel Boissonnat; Richard Nock (SCG, 2007)

Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree. - Claudia Henry; Richard Nock; Frank Nielsen (IJCAI, 2007)

Fast Graph Segmentation Based on Statistical Aggregation Phenomena. - Frank Nielsen; Richard Nock (MVA, 2007)

A Real generalization of discrete AdaBoost. - Richard Nock; Frank Nielsen (Artif. Intell., 2007)

Bregman Voronoi Diagrams: Properties, Algorithms and Applications - Frank Nielsen; Jean-Daniel Boissonnat; Richard Nock (CoRR, 2007)

On the Centroids of Symmetrized Bregman Divergences - Frank Nielsen; Richard Nock (CoRR, 2007)

Statistical supports for mining sequential patterns and improving the incremental update process on data streams. - Pierre-Alain Laur; Jean-Emile Symphor; Richard Nock; Pascal Poncelet (Intell. Data Anal., 2007)

Mining evolving data streams for frequent patterns. - Pierre-Alain Laur; Richard Nock; Jean-Emile Symphor; Pascal Poncelet (Pattern Recognit., 2007)

Self-improved gaps almost everywhere for the agnostic approximation of monomials. - Richard Nock; Frank Nielsen (Theor. Comput. Sci., 2007)

Clustering Multivariate Normal Distributions. - Frank Nielsen; Richard Nock (ETVC, 2008)

Intrinsic Geometries in Learning. - Richard Nock; Frank Nielsen (ETVC, 2008)

Bregman sided and symmetrized centroids. - Frank Nielsen; Richard Nock (ICPR, 2008)

On the efficient minimization of convex surrogates in supervised learning. - Richard Nock; Frank Nielsen (ICPR, 2008)

Quantum Voronoi diagrams and Holevo channel capacity for 1-qubit quantum states. - Frank Nielsen; Richard Nock (ISIT, 2008)

On the Efficient Minimization of Classification Calibrated Surrogates. - Richard Nock; Frank Nielsen (NIPS, 2008)

Mixed Bregman Clustering with Approximation Guarantees. - Richard Nock; Panu Luosto; Jyrki Kivinen (ECML/PKDD (2), 2008)

Staring at Economic Aggregators through Information Lenses - Richard Nock; Nicolas Sanz; Fred Celimene; Frank Nielsen (CoRR, 2008)

On the smallest enclosing information disk. - Frank Nielsen; Richard Nock (Inf. Process. Lett., 2008)

Levels of Details for Gaussian Mixture Models. - Vincent Garcia; Frank Nielsen; Richard Nock (ACCV (2), 2009)

Simplifying Gaussian mixture models via entropic quantization. - Frank Nielsen; Vincent Garcia; Richard Nock (EUSIPCO, 2009)

The Dual Voronoi Diagrams with Respect to Representational Bregman Divergences. - Frank Nielsen; Richard Nock (ISVD, 2009)

Information geometries and Microeconomic Theories - Richard Nock; Brice Magdalou; Nicolas Sanz; Eric Briys; Fred Celimene; Frank Nielsen (CoRR, 2009)

Approximating Smallest Enclosing Balls with Applications to Machine Learning. - Frank Nielsen; Richard Nock (Int. J. Comput. Geom. Appl., 2009)

Bregman Divergences and Surrogates for Learning. - Richard Nock; Frank Nielsen (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Soft memberships for spectral clustering, with application to permeable language distinction. - Richard Nock; Pascal Vaillant; Claudia Henry; Frank Nielsen (Pattern Recognit., 2009)

Sided and symmetrized Bregman centroids. - Frank Nielsen; Richard Nock (IEEE Trans. Inf. Theory, 2009)

Multi-class Leveraged κ-NN for Image Classification. - Paolo Piro; Richard Nock; Frank Nielsen; Michel Barlaud (ACCV (3), 2010)

Hierarchical Gaussian Mixture Model. - Vincent Garcia; Frank Nielsen; Richard Nock (ICASSP, 2010)

Hyperbolic Voronoi Diagrams Made Easy. - Frank Nielsen; Richard Nock (ICCSA Workshops, 2010)

Entropies and cross-entropies of exponential families. - Frank Nielsen; Richard Nock (ICIP, 2010)

Boosting Bayesian MAP Classification. - Paolo Piro; Richard Nock; Frank Nielsen; Michel Barlaud (ICPR, 2010)

Jensen-Bregman Voronoi Diagrams and Centroidal Tessellations. - Frank Nielsen; Richard Nock (ISVD, 2010)

K-NN boosting prototype learning for object classification. - Paolo Piro; Michel Barlaud; Richard Nock; Frank Nielsen (WIAMIS, 2010)

Boosting k-NN for categorization of natural scenes - Paolo Piro; Richard Nock; Frank Nielsen; Michel Barlaud (CoRR, 2010)

Bregman Voronoi Diagrams. - Jean-Daniel Boissonnat; Frank Nielsen; Richard Nock (Discret. Comput. Geom., 2010)

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 (ICML, 2011)

On Rényi and Tsallis entropies and divergences for exponential families - Frank Nielsen; Richard Nock (CoRR, 2011)

Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms - Richard Nock (CoRR, 2011)

A closed-form expression for the Sharma-Mittal entropy of exponential families - Frank Nielsen; Richard Nock (CoRR, 2011)

Income distributions and decomposable divergence measures. - Brice Magdalou; Richard Nock (J. Econ. Theory, 2011)

Skew Jensen-Bregman Voronoi Diagrams. - Frank Nielsen; Richard Nock (Trans. Comput. Sci., 2011)

Classification of biological cells using bio-inspired descriptors. - Wafa Bel Haj Ali; Dario Giampaglia; Michel Barlaud; Paolo Piro; Richard Nock; Thierry Pourcher (ICPR, 2012)

Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability. - Roberto D'Ambrosio; Wafa Bel Haj Ali; Richard Nock; Paolo Soda; Frank Nielsen; Michel Barlaud (MLMI, 2012)

Boosting Nearest Neighbors for the Efficient Estimation of Posteriors. - Roberto D'Ambrosio; Richard Nock; Wafa Bel Haj Ali; Frank Nielsen; Michel Barlaud (ECML/PKDD (1), 2012)

The hyperbolic Voronoi diagram in arbitrary dimension - Frank Nielsen; Richard Nock (CoRR, 2012)

Boosting k-NN for Categorization of Natural Scenes. - Richard Nock; Paolo Piro; Frank Nielsen; Wafa Bel Haj Ali; Michel Barlaud (Int. J. Comput. Vis., 2012)

Leveraging k-NN for generic classification boosting. - Paolo Piro; Richard Nock; Frank Nielsen; Michel Barlaud (Neurocomputing, 2012)

Consensus Region Merging for Image Segmentation. - Frank Nielsen; Richard Nock (ACPR, 2013)

Demonstrator of a Tourist Recommendation System. - Erol Elisabeth; Richard Nock; Fred Celimene (BDA, 2013)

Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries. - Richard Nock; Frank Nielsen; Eric Briys (JCDL, 2013)

Information-geometric lenses for multiple foci+contexts interfaces. - Richard Nock; Frank Nielsen (SIGGRAPH ASIA Technical Briefs, 2013)

Total Jensen divergences: Definition, Properties and k-Means++ Clustering. - Frank Nielsen; Richard Nock (CoRR, 2013)

Combining Feature and Prototype Pruning by Uncertainty Minimization - Marc Sebban; Richard Nock (CoRR, 2013)

Boosting k-Nearest Neighbors Classification. - Paolo Piro; Richard Nock; Wafa Bel Haj Ali (Advanced Topics in Computer Vision, 2013)

Visualizing hyperbolic Voronoi diagrams. - Frank Nielsen; Richard Nock (SoCG, 2014)

Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification. - Wafa Bel Haj Ali; Richard Nock; Michel Barlaud (ICPR, 2014)

(Almost) No Label No Cry. - Giorgio Patrini; Richard Nock; Tibério S. Caetano; Paul Rivera (NIPS, 2014)

A note on the optimal scalar Bregman k-means clustering with an application to learning best statistical mixtures. - Frank Nielsen; Richard Nock (CoRR, 2014)

Further heuristics for $k$-means: The merge-and-split heuristic and the $(k, l)$-means. - Frank Nielsen; Richard Nock (CoRR, 2014)

Further results on the hyperbolic Voronoi diagrams. - Frank Nielsen; Richard Nock (CoRR, 2014)

On Clustering Histograms with k-Means by Using Mixed α-Divergences. - Frank Nielsen; Richard Nock; Shun-ichi Amari (Entropy, 2014)

On the Chi Square and Higher-Order Chi Distances for Approximating $f$ -Divergences. - Frank Nielsen; Richard Nock (IEEE Signal Process. Lett., 2014)

Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning. - Frank Nielsen; Richard Nock (IEEE Signal Process. Lett., 2014)

Total Jensen divergences: Definition, properties and clustering. - Frank Nielsen; Richard Nock (ICASSP, 2015)

Rademacher Observations, Private Data, and Boosting. - Richard Nock; Giorgio Patrini; Arik Friedman (ICML, 2015)

Learning Games and Rademacher Observations Losses. - Richard Nock (CoRR, 2015)

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana. - Meriam Bayoudh; Emmanuel Roux; Gilles Richard; Richard Nock (Comput. Geosci., 2015)

Gentle Nearest Neighbors Boosting over Proper Scoring Rules. - Richard Nock; Wafa Bel Haj Ali; Roberto D'Ambrosio; Frank Nielsen; Michel Barlaud (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Classification with mixtures of curved mahalanobis metrics. - Frank Nielsen; Boris Muzellec; Richard Nock (ICIP, 2016)

k-variates++: more pluses in the k-means++. - Richard Nock; Raphaël Canyasse; Roksana Boreli; Frank Nielsen (ICML, 2016)

Loss factorization, weakly supervised learning and label noise robustness. - Giorgio Patrini; Frank Nielsen; Richard Nock; Marcello Carioni (ICML, 2016)

Fast Learning from Distributed Datasets without Entity Matching. - Giorgio Patrini; Richard Nock; Stephen Hardy 002; Tibério S. Caetano (IJCAI, 2016)

On Regularizing Rademacher Observation Losses. - Richard Nock (NIPS, 2016)

A scaled Bregman theorem with applications. - Richard Nock; Aditya Krishna Menon; Cheng Soon Ong (NIPS, 2016)

Patch Matching with Polynomial Exponential Families and Projective Divergences. - Frank Nielsen; Richard Nock (SISAP, 2016)

Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances. - Frank Nielsen; Boris Muzellec; Richard Nock (CoRR, 2016)

Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices. - Frank Nielsen; Richard Nock (CoRR, 2016)

A series of maximum entropy upper bounds of the differential entropy. - Frank Nielsen; Richard Nock (CoRR, 2016)

The Crossover Process: Learnability meets Protection from Inference Attacks. - Richard Nock; Giorgio Patrini; Finnian Lattimore; Tibério S. Caetano (CoRR, 2016)

Making Neural Networks Robust to Label Noise: a Loss Correction Approach. - Giorgio Patrini; Alessandro Rozza; Aditya Krishna Menon; Richard Nock; Lizhen Qu (CoRR, 2016)

On Conformal Divergences and Their Population Minimizers. - Richard Nock; Frank Nielsen; Shun-ichi Amari (IEEE Trans. Inf. Theory, 2016)

Tsallis Regularized Optimal Transport and Ecological Inference. - Boris Muzellec; Richard Nock; Giorgio Patrini; Frank Nielsen (AAAI, 2017)

Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. - Giorgio Patrini; Alessandro Rozza; Aditya Krishna Menon; Richard Nock; Lizhen Qu (CVPR, 2017)

Bregman Divergences from Comparative Convexity. - Frank Nielsen; Richard Nock (GSI, 2017)

f-GANs in an Information Geometric Nutshell. - Richard Nock; Zac Cranko; Aditya Krishna Menon; Lizhen Qu; Robert C. Williamson (NIPS, 2017)

Semi-parametric Network Structure Discovery Models. - Amir Dezfouli; Edwin V. Bonilla; Richard Nock (CoRR, 2017)

Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means. - Frank Nielsen; Richard Nock (CoRR, 2017)

Distribution-free Evolvability of Vector Spaces: All it takes is a Generating Set. - Richard Nock; Frank Nielsen (CoRR, 2017)

On w-mixtures: Finite convex combinations of prescribed component distributions. - Frank Nielsen; Richard Nock (CoRR, 2017)

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. - Stephen Hardy 002; Wilko Henecka; Hamish Ivey-Law; Richard Nock; Giorgio Patrini; Guillaume Smith; Brian Thorne (CoRR, 2017)

MaxEnt Upper Bounds for the Differential Entropy of Univariate Continuous Distributions. - Frank Nielsen; Richard Nock (IEEE Signal Process. Lett., 2017)

Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity. - Frank Nielsen; Richard Nock (IEEE Signal Process. Lett., 2017)

On the Geometry of Mixtures of Prescribed Distributions. - Frank Nielsen; Richard Nock (ICASSP, 2018)

Variational Network Inference: Strong and Stable with Concrete Support. - Amir Dezfouli; Edwin V. Bonilla; Richard Nock (ICML, 2018)

Representation Learning of Compositional Data. - Marta Avalos; Richard Nock; Cheng Soon Ong; Julien Rouar; Ke Sun 001 (NeurIPS, 2018)

Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds. - Kelvin Hsu; Richard Nock; Fabio Ramos 001 (ECML/PKDD (2), 2018)

Entity Resolution and Federated Learning get a Federated Resolution. - Richard Nock; Stephen Hardy 002; Wilko Henecka; Hamish Ivey-Law; Giorgio Patrini; Guillaume Smith; Brian Thorne (CoRR, 2018)

Monge beats Bayes: Hardness Results for Adversarial Training. - Zac Cranko; Aditya Krishna Menon; Richard Nock; Cheng Soon Ong; Zhan Shi; Christian J. Walder (CoRR, 2018)

Integral Privacy for Density Estimation with Approximation Guarantees. - Hisham Husain; Zac Cranko; Richard Nock (CoRR, 2018)

Private Text Classification. - Leif W. Hanlen; Richard Nock; Hanna Suominen; Neil Bacon (CoRR, 2018)

D-PAGE: Diverse Paraphrase Generation. - Qiongkai Xu; Juyan Zhang; Lizhen Qu; Lexing Xie; Richard Nock (CoRR, 2018)

Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds. - Kelvin Hsu; Richard Nock; Fabio Ramos 001 (CoRR, 2018)

Lipschitz Networks and Distributional Robustness. - Zac Cranko; Simon Kornblith; Zhan Shi; Richard Nock (CoRR, 2018)

Min-Max Statistical Alignment for Transfer Learning. - Samitha Herath; Mehrtash Harandi; Basura Fernando; Richard Nock (CVPR, 2019)

The Bregman Chord Divergence. - Frank Nielsen; Richard Nock (GSI, 2019)

Siamese Networks: The Tale of Two Manifolds. - Soumava Kumar Roy; Mehrtash Harandi; Richard Nock; Richard I. Hartley (ICCV, 2019)

Monge blunts Bayes: Hardness Results for Adversarial Training. - Zac Cranko; Aditya Krishna Menon; Richard Nock; Cheng Soon Ong; Zhan Shi; Christian J. Walder (ICML, 2019)

Boosted Density Estimation Remastered. - Zac Cranko; Richard Nock (ICML, 2019)

Lossless or Quantized Boosting with Integer Arithmetic. - Richard Nock; Robert C. Williamson (ICML, 2019)

Disentangled behavioural representations. - Amir Dezfouli; Hassan Ashtiani; Omar Ghattas; Richard Nock; Peter Dayan; Cheng Soon Ong (NeurIPS, 2019)

A Primal-Dual link between GANs and Autoencoders. - Hisham Husain; Richard Nock; Robert C. Williamson (NeurIPS, 2019)

Non-Euclidean Embeddings for Graph Analytics and Visualisation. - Daniel Filonik; Tian Feng 001; Ke Sun 001; Richard Nock; Alex Collins; Tomasz Bednarz (SIGGRAPH Asia Posters, 2019)

New Tricks for Estimating Gradients of Expectations. - Christian J. Walder; Richard Nock; Cheng Soon Ong; Masashi Sugiyama (CoRR, 2019)

Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. - Hisham Husain; Richard Nock; Robert C. Williamson (CoRR, 2019)

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; Salim El Rouayheb; et al. (CoRR, 2019)

Statistical Computational Learning. - Antoine Cornuéjols; Frédéric Koriche; Richard Nock (A Guided Tour of Artificial Intelligence Research (1) (I), 2020)

Local Differential Privacy for Sampling. - Hisham Husain; Borja Balle; Zac Cranko; Richard Nock (AISTATS, 2020)

Adaptive Subspaces for Few-Shot Learning. - Christian Simon; Piotr Koniusz; Richard Nock; Mehrtash Harandi (CVPR, 2020)

On Modulating the Gradient for Meta-learning. - Christian Simon; Piotr Koniusz; Richard Nock; Mehrtash Harandi (ECCV (8), 2020)

Supervised learning: no loss no cry. - Richard Nock; Aditya Krishna Menon (ICML, 2020)

All your loss are belong to Bayes. - Christian J. Walder; Richard Nock (NeurIPS, 2020)

Boosted and Differentially Private Ensembles of Decision Trees. - Richard Nock; Wilko Henecka (CoRR, 2020)

Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family. - Frank Nielsen; Richard Nock (CoRR, 2020)

Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers. - Alexander Soen; Hisham Husain; Richard Nock (CoRR, 2020)

SMINT: Toward Interpretable and Robust Model Sharing for Deep Neural Networks. - Huijun Wu 001; Chen Wang 008; Richard Nock; Wei Wang 011; Jie Yin 001; Kai Lu 001; Liming Zhu 001 (ACM Trans. Web, 2020)

Computing Statistical Divergences with Sigma Points. - Frank Nielsen; Richard Nock (GSI, 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 002; Wilko Henecka; Hamish Ivey-Law; Jakub Nabaglo; Giorgio Patrini; Guillaume Smith; Brian Thorne (ICML, 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; et al. (Found. Trends Mach. Learn., 2021)

Manifold Learning Benefits GANs. - Yao Ni; Piotr Koniusz; Richard I. Hartley; Richard Nock (CVPR, 2022)

Neural Network Poisson Models for Behavioural and Neural Spike Train Data. - Moein Khajehnejad; Forough Habibollahi; Richard Nock; Ehsan Arabzadeh; Peter Dayan; Amir Dezfouli (ICML, 2022)

Generative Trees: Adversarial and Copycat. - Richard Nock; Mathieu Guillame-Bert (ICML, 2022)

Being Properly Improper. - Tyler Sypherd; Richard Nock; Lalitha Sankar (ICML, 2022)

Fair Wrapping for Black-box Predictions. - Alexander Soen; Ibrahim M. Alabdulmohsin; Sanmi Koyejo; Yishay Mansour; Nyalleng Moorosi; Richard Nock; Ke Sun 001; Lexing Xie (NeurIPS, 2022)

What killed the Convex Booster ? - Yishay Mansour; Richard Nock; Robert C. Williamson (CoRR, 2022)

Clustering above Exponential Families with Tempered Exponential Measures. - Ehsan Amid; Richard Nock; Manfred K. Warmuth (AISTATS, 2023)

Smoothly Giving up: Robustness for Simple Models. - Tyler Sypherd; Nathaniel Stromberg 001; Richard Nock; Visar Berisha; Lalitha Sankar (AISTATS, 2023)

LegendreTron: Uprising Proper Multiclass Loss Learning. - Kevin H. Lam; Christian J. Walder; Spiridon I. Penev; Richard Nock (ICML, 2023)

Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. - Yishay Mansour; Richard Nock; Robert C. Williamson (ICML, 2023)

Fair Densities via Boosting the Sufficient Statistics of Exponential Families. - Alexander Soen; Hisham Husain; Richard Nock (ICML, 2023)

Boosting with Tempered Exponential Measures. - Richard Nock; Ehsan Amid; Manfred K. Warmuth (NeurIPS, 2023)

The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (CoRR, 2023)

Optimal Transport with Tempered Exponential Measures. - Ehsan Amid; Frank Nielsen; Richard Nock; Manfred K. Warmuth (AAAI, 2024)

Hyperbolic Embeddings of Supervised Models. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (NeurIPS, 2024)

Generative Forests. - Richard Nock; Mathieu Guillame-Bert (NeurIPS, 2024)

How to Boost Any Loss Function. - Richard Nock; Yishay Mansour (NeurIPS, 2024)

Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections. - Nathan Stromberg; Rohan Ayyagari; Sanmi Koyejo; Richard Nock; Lalitha Sankar (NeurIPS, 2024)

Tempered Calculus for ML: Application to Hyperbolic Model Embedding. - Richard Nock; Ehsan Amid; Frank Nielsen; Alexander Soen; Manfred K. Warmuth (CoRR, 2024)

Boosting gets full Attention for Relational Learning. - Mathieu Guillame-Bert; Richard Nock (CoRR, 2024)

Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading. - Nathan Stromberg; Rohan Ayyagari; Sanmi Koyejo; Richard Nock; Lalitha Sankar (CoRR, 2024)

For Robust Worst-Group Accuracy, Ignore Group Annotations. - Nathan Stromberg; Rohan Ayyagari; Monica Welfert; Sanmi Koyejo; Richard Nock; Lalitha Sankar (Trans. Mach. Learn. Res., 2024)

Principled Foundations for Preference Optimization. - Wenxuan Zhou; Shujian Zhang; Brice Magdalou; John Lambert; Ehsan Amid; Richard Nock; Andrew Hard (CoRR, 2025)