Paper-Conference

Infinite Neural Operators: Gaussian Processes on Functions
A variety of infinitely wide neural architectures (e.g., dense NNs, CNNs, and transformers) induce Gaussian process (GP) priors over …
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Parameter Efficient Fine-tuning via Explained Variance Adaptation
Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned for a specific downstream task. The most common …
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Guaranteed Prediction Sets for Functional Surrogate models
We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models …
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Calibrated Physics-Informed Uncertainty Quantification
Neural PDEs have emerged as inexpensive surrogate models for numerical PDE solvers. While they offer efficient approximations, they …
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