Eyke Hüllermeier

Artificial Intelligence · 2005–2023

A researcher in the Science corpus: 25 papers; trusts 263 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 230 citations into it; #5,228 of 35,345 by trust here, #5,359 by citations
Management Science and Operations Research — 68 citations into it; #2,528 of 11,946 by trust here, #2,728 by citations
Reinforcement Learning — 59 citations into it; #1,169 of 2,242 by trust here, #1,174 by citations
Statistics and Probability — 55 citations into it
Information Systems — 53 citations into it
Evaluation & Benchmarks — 41 citations into it
Computer Networks and Communications — 40 citations into it; #6,016 of 9,886 by trust here, #6,788 by citations
Computer Vision and Pattern Recognition — 35 citations into it

Whom Eyke Hüllermeier trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Eyke Hüllermeier overall

Papers from Eyke Hüllermeier's perspective

Ordered by how strongly the researchers Eyke Hüllermeier trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm (2014) — 73 citations
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm (2012) — 177 citations
Preference Learning (2010) — 315 citations
Learning to Rank Lexical Substitutions (2013) — 24 citations
ML-Plan: Automated machine learning via hierarchical planning (2018) — 182 citations
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning (2011) — 64 citations
Label ranking by learning pairwise preferences (2008) — 589 citations
On label dependence and loss minimization in multi-label classification (2012) — 330 citations
FURIA: an algorithm for unordered fuzzy rule induction (2009) — 467 citations
Preference-based Online Learning with Dueling Bandits: A Survey (2018) — 149 citations
A Survey of Preference-Based Online Learning with Bandit Algorithms (2014) — 50 citations
Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty (2014) — 188 citations
A Survey of Reinforcement Learning from Human Feedback (2023) — 338 citations
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019) — 2,133 citations
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction (2019) — 88 citations
ML-Plan for Unlimited-Length Machine Learning Pipelines (2018) — 23 citations
Open challenges for data stream mining research (2014) — 310 citations
Preference-based Evolutionary Direct Policy Search (2013) — 22 citations
Life Satisfaction, Household Income and Personality Traits (2012) — 535 citations
Bipartite Ranking through Minimization of Univariate Loss (2011) — 100 citations