Snoek, Jasper

Practical Bayesian Optimization of Machine Learning Algorithms

Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv

Advanced Bandit Algorithms ResearchMachine Learning and Data Classification

Cited by 6,691

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 1,048

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., & Snoek, J. (2019) · arXiv

Adversarial Robustness in Machine LearningGaussian Processes and Bayesian Inference

Cited by 989

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 537

Second opinion needed: Communicating uncertainty in medical machine learning

Kompa, B., Snoek, J., & Beam, A. L. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 514

Hyperparameter ensembles for robustness and uncertainty quantification

Wenzel, F., Snoek, J., Tran, D., & Jenatton, R. (2020) · arXiv

Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning

Cited by 122

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

Riquelme, C., Tucker, G., & Snoek, J. (2018) · arXiv

Advanced Bandit Algorithms ResearchReinforcement Learning in Robotics

Cited by 72

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Combining Ensembles and Data Augmentation can Harm your Calibration

Wen, Y., Jerfel, G., Muller, R., Dusenberry, M. W., Snoek, J., Lakshminarayanan, B., & Tran, D. (2020) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 27

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10