Michal Valko

Artificial Intelligence · 2013–2024

A researcher in the Science corpus: 12 papers; trusts 359 researchers and is trusted by 92 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 252 citations into it; #16,951 of 35,345 by trust here, #2,346 by citations
• Computer Vision and Pattern Recognition — 195 citations into it; #3,477 of 21,575 by trust here, #1,904 by citations
• Representation & Self-Supervised Learning — 127 citations into it; #3,012 of 3,145 by trust here, #337 by citations
• Reinforcement Learning — 55 citations into it
• Optimization & Training — 48 citations into it
• Management Science and Operations Research — 46 citations into it; #7,092 of 11,946 by trust here, #4,267 by citations
• Graph Learning — 25 citations into it
• Model Architectures — 25 citations into it

Whom Michal Valko trusts overall

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

1. Peter Auer
2. Jean-Yves Audibert
3. Lihong Li
4. Fuzhen Zhang
5. Geoffrey E. Hinton
6. Kaiming He
7. Sébastien Bubeck
8. J. Langford
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Michal Valko overall

1. Sayak Ray Chowdhury
2. Tor Lattimore
3. Lin F. Yang
4. Dongruo Zhou
5. Aniket Anand Deshmukh
6. Louis Faury
7. Aditya Gopalan
8. Lisha Li
9. Csaba Szepesvari
10. Mengdi Wang

Papers from Michal Valko's perspective

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

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

Their papers

• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
• BYOL works even without batch statistics (2020) — 131 citations
• Broaden Your Views for Self-Supervised Video Learning (2021) — 146 citations
• A General Theoretical Paradigm to Understand Learning from Human Preferences (2023) — 1,063 citations
• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces (2020) — 45 citations
• Simple regret for infinitely many armed bandits (2015) — 95 citations
• Large-Scale Representation Learning on Graphs via Bootstrapping (2021) — 329 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• Bootstrapped Representation Learning on Graphs (2021) — 315 citations
• Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited (2020) — 118 citations
• Regret Bounds for Kernel-Based Reinforcement Learning (2020) — 22 citations
• Finite-Time Analysis of Kernelised Contextual Bandits (2013) — 322 citations