Related Issues (20)
- test_full_hess_curvlinops_vs_asdl of "regression" loss failed in Python 3.11 HOT 2
- Laplace Transform and Inverse laplace Transform for 2D images HOT 2
- Remove default `loss` for `optimize_prior_precision`
- Make try-except statement in `_gridsearch` more specific HOT 1
- Serialization: `torch.load(..., map_location="cpu")` doesn't map all tensors in `Laplace` instance HOT 2
- Make `torchmetrics` metrics the default in cross validation
- Efficient LLLA predictive samples
- Support for `torchtune`'s models and datasets
- Conform to PyTorch convention in the loss HOT 1
- Cache features in LLLA with MC predictive HOT 1
- Generalized dict-input mechanism
- Add example & doc for reward modeling functionality
- Handle feature reduction properly in LLLA
- Adding prior precision in `KronLaplace` is broken
- Support for structured Kronecker factors HOT 1
- Fast `KronLLLaplace` joint predictive
- Handle logits with more than 2 axes in `torch.func` diag and full backends
- Fix all linting issues and format all files with `ruff`
- Prior precision and prior mean of `LLLaplace` are hardcoded HOT 2
- `torch.func` jacobian OOM HOT 2
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from laplace.