Richard Nock

Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections NeurIPS 2024 Generative Forests NeurIPS 2024 How to Boost Any Loss Function NeurIPS 2024 Hyperbolic Embeddings of Supervised Models NeurIPS 2024 Optimal Transport with Tempered Exponential Measures AAAI 2024 Boosting with Tempered Exponential Measures NeurIPS 2023 Clustering above Exponential Families with Tempered Exponential Measures AISTATS 2023 Fair Densities via Boosting the Sufficient Statistics of Exponential Families ICML 2023 LegendreTron: Uprising Proper Multiclass Loss Learning ICML 2023 Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice ICML 2023 Smoothly Giving up: Robustness for Simple Models AISTATS 2023 Being Properly Improper ICML 2022 Fair Wrapping for Black-box Predictions NeurIPS 2022 Generative Trees: Adversarial and Copycat ICML 2022 Manifold Learning Benefits GANs CVPR 2022 Neural Network Poisson Models for Behavioural and Neural Spike Train Data ICML 2022 Generalised Lipschitz Regularisation Equals Distributional Robustness ICML 2021 The Impact of Record Linkage on Learning from Feature Partitioned Data ICML 2021 Adaptive Subspaces for Few-Shot Learning CVPR 2020 All your loss are belong to Bayes NeurIPS 2020 Local Differential Privacy for Sampling AISTATS 2020 On Modulating the Gradient for Meta-Learning ECCV 2020 Supervised learning: no loss no cry ICML 2020 A Primal-Dual link between GANs and Autoencoders NeurIPS 2019 Boosted Density Estimation Remastered ICML 2019 Disentangled behavioural representations NeurIPS 2019 Lossless or Quantized Boosting with Integer Arithmetic ICML 2019 Min-Max Statistical Alignment for Transfer Learning CVPR 2019 Monge blunts Bayes: Hardness Results for Adversarial Training ICML 2019 Siamese Networks: The Tale of Two Manifolds ICCV 2019 Representation Learning of Compositional Data NeurIPS 2018 Variational Network Inference: Strong and Stable with Concrete Support ICML 2018 f-GANs in an Information Geometric Nutshell NeurIPS 2017 Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach CVPR 2017 A scaled Bregman theorem with applications NeurIPS 2016 Fast Learning from Distributed Datasets without Entity Matching IJCAI 2016 k-variates++: more pluses in the k-means++ ICML 2016 Loss factorization, weakly supervised learning and label noise robustness ICML 2016 On Regularizing Rademacher Observation Losses NeurIPS 2016 Rademacher Observations, Private Data, and Boosting ICML 2015 (Almost) No Label No Cry NeurIPS 2014 On the Efficient Minimization of Classification Calibrated Surrogates NeurIPS 2008 Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem JMLR 2002