Róbert Istvan Busa-Fekete
Artificial Intelligence · 2012–2025
A researcher in the Science corpus: 9 papers; trusts 115 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 131 citations into it; #12,952 of 35,345 by trust here, #16,166 by citations
- Management Science and Operations Research — 57 citations into it; #5,079 of 11,946 by trust here, #5,316 by citations
- Reinforcement Learning — 50 citations into it; #1,255 of 2,242 by trust here, #1,545 by citations
- Computer Vision and Pattern Recognition — 32 citations into it
- Optimization & Training — 25 citations into it
- Computer Networks and Communications — 24 citations into it; #6,015 of 9,886 by trust here, #6,788 by citations
- Evaluation & Benchmarks — 24 citations into it
- Statistics and Probability — 19 citations into it
Whom Róbert Istvan Busa-Fekete trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yisong Yue
- Peter Auer
- Thorsten Joachims
- Nicolò Cesa-Bianchi
- Oded Maron
- Ronald J. Williams
- W. Hoeffding
- Csaba Szepesvari
- Yufan Zhao
- Andrew W. Moore
Who trusts Róbert Istvan Busa-Fekete overall
- Aadirupa Saha
- Akash Kumar Mohankumar
- Christian Wirth
- Yongsu Ahn
- Oren Melamud
- Aldo Pacchiano
- Chao Yu
- Aditya Gopalan
- Ruohan Zhang
- Mitesh M. Khapra
Papers from Róbert Istvan Busa-Fekete's perspective
Ordered by how strongly the researchers Róbert Istvan Busa-Fekete 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm (2014) — 73 citations
- Learning to Rank Lexical Substitutions (2013) — 24 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
- Distributed Stochastic Optimization via Adaptive SGD (2018) — 24 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Gemma 3 Technical Report (2025) — 1,621 citations
- Preference-based Evolutionary Direct Policy Search (2013) — 22 citations
- An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain (2012) — 41 citations