Richard Nock
43 papers · 2002–2024 · 9 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (18) π Interdisciplinary Bridge π Conference Polyglot (9)
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Conference Polyglot
(9)
π§
Keyword Pioneer
π
Interdisciplinary Bridge
π
Keyword Trendsetter Combo
(3)
π¬
Deep Specialist
(15)
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Keyword Champion
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Century Club
(43)
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Trend Setter
ποΈ
Keyword Collector
(50)
β‘
Prolific Year
(5)
π₯
Unstoppable
(11)
Conferences
ICML (16)
NIPS (15)
CVPR (4)
AISTATS (3)
AAAI (1)
ECCV (1)
ICCV (1)
IJCAI (1)
JMLR (1)
Top co-authors
Research topics
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