Me, Prasanna Balaprakash (https://www.linkedin.com/in/prasannaprakash/) and Jayaraman J. Thiagarajan (https://www.linkedin.com/in/jjayaram7/) are organizing a minisymposium on “Robust and Efficient Probabilistic Deep Learning for Scientific Data and Beyond” at the SIAM Conference on Uncertainty Quantification (https://lnkd.in/eVQ-hVK5 ), that is taking place from April 12-15. We will be highlighting the latest advances in probabilistic deep learning, both from the Bayesian and frequentist perspective with focus on efficiency and robustness.
We have three sessions https://lnkd.in/ekJFuS4E https://lnkd.in/eA9eEmUr https://lnkd.in/eUS6hGP7
We will be looking forward for your attendance. The recorded sessions will also be available for registered participants
Our awesome speaker lineup is as follows: 1. Andrew Wilson (https://lnkd.in/e_PCBaKy) talking about “How Do We Build Models That Learn and Generalize?” 2. Shrijita Bhattacharya talking on “Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details” 3. Ian Fischer (https://lnkd.in/egCNRUyn) talking on “Information Theoretic Objectives, Generalization, and Robustness” 4. Bo Li (https://lnkd.in/e6Z2-Zyt) talking on “Trustworthy Machine Learning via Logic Inference” 5. Shubhendu Trivedi talking on “Locally Valid and Discriminative Prediction Intervals for Deep Learning Models” 6. I`m giving a talk on “Information-Preserving Bayesian Models for Efficient and Robust Learning” 7. Rushil Anirudh talking on “Delta-UQ: Accurate Uncertainty Quantification via Anchor Marginalization” 8. Salman Habib giving a talk on “Machine Learning, Systematics, and Statistics: A Cosmological Perspective” 9. Mohamed Aziz Bhouri (https://lnkd.in/enqfTNPV) taking on “Gaussian Processes Meet Neural ODEs: A Bayesian Framework for Learning the Dynamics of Partially Observed Systems from Scarce and Noisy Data” 10. Prasanna Balaprakash giving a talk on “Autodeuq: Automated Deep Ensemble with Uncertainty Quantification”
