Bobak Shahriari
Artificial Intelligence · 2014–2025
A researcher in the Science corpus: 10 papers; trusts 119 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 331 citations into it; #7,494 of 35,345 by trust here, #3,155 by citations
- Reinforcement Learning — 180 citations into it
- Management Science and Operations Research — 87 citations into it; #1,945 of 11,946 by trust here, #476 by citations
- Computer Vision and Pattern Recognition — 73 citations into it
- Computational Theory and Mathematics — 69 citations into it; #1,900 of 12,182 by trust here, #380 by citations
- Evaluation & Benchmarks — 54 citations into it
- Optimization & Training — 50 citations into it; #1,163 of 2,601 by trust here, #1,248 by citations
- Probabilistic & Bayesian Methods — 30 citations into it; #599 of 1,525 by trust here, #212 by citations
Whom Bobak Shahriari trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nando de Freitas
- Frank Hutter
- Daniel J. Lizotte
- Eric Brochu
- Geoffrey E. Hinton
- Yoshua Bengio
- Jasper Snoek
- Richard S. Sutton
- Diederik P. Kingma
- Radford M. Neal
Who trusts Bobak Shahriari overall
- Frank Hutter
- Gang Luo
- Marc-André Zöller
- Aaron Klein
- Bowen Baker
- Stefan Falkner
- Hangyu Zhu
- Matthias Feurer
- Yutian Chen
- Tonmoy Saikia
Papers from Bobak Shahriari's perspective
Ordered by how strongly the researchers Bobak Shahriari 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
- Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016) — 5,660 citations
- Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
- Critic Regularized Regression (2020) — 389 citations
- An Entropy Search Portfolio for Bayesian Optimization (2014) — 62 citations
- Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
- Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 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
- Making Efficient Use of Demonstrations to Solve Hard Exploration Problems (2019) — 97 citations
- On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014) — 134 citations