← Authors

Richard Nock

43 papers · 2002–2024 · 9 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (22) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (8) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (18) 🌍 Conference Polyglot (9) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (3) πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion πŸ’Ž Century Club (43) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (50) ⚑ Prolific Year (5) πŸ”₯ Unstoppable (11)

Conferences

ICML (16) NIPS (15) CVPR (4) AISTATS (3) AAAI (1) ECCV (1) ICCV (1) IJCAI (1) JMLR (1)

Top co-authors

Giorgio Patrini (6) Zac Cranko (5) Frank Nielsen (5) Mehrtash Harandi (4) Cheng Soon Ong (4) Ehsan Amid (4) Yishay Mansour (3) Lalitha Sankar (3) Manfred K. Warmuth (3) Aditya Menon (3)

Research topics

Differential Privacy (1)

Keywords

loss function (6) boosting algorithm (6) generative adversarial network (5) decision tree (5) convex optimization (4) supervised learning (4) exponential family (4) label noise (4) proper loss (4) bregman divergence (3) optimal transport (3) density estimation (3) variational inference (3) tempered exponential measure (3) information geometry (2) logistic loss (2) exponential families (2) algorithmic fairness (2) exponential loss (2) gaussian process (2)

Papers

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