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Feat: Implement Full-Rank VI #720
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Commits on Aug 14, 2024
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Commits on Aug 16, 2024
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Fix: cholesky factor as flattened PyTree
Define `chol_params` as a flattened Cholesky factor PyTree that consists of diagonal elements followed by the off-diagonal elements in row-major order for n = dim * (dim + 1) / 2 elements. The diagonal (first dim elements) are passed through a softplus function to ensure positivity, crucial to maintain a valid covariance matrix This parameterization allows for unconstrained optimization while ensuring the resulting covariance matrix Sigma = CC^T is symmetric and positive definite. The `chol_params` are then reshaped into a lower triangular matrix `chol_factor` using `jnp.tril` and `jnp.diag` functions.
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Commits on Aug 19, 2024
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Fix: Non-jitted full-rank VI works
Fix testing bug, add docstrings, and change softmax to exponential when converting `chol_params` to `chol_factor` in `_unflatten_cholesky`.
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Fix: Full-rank VI compatible with JIT compilation
Refactor `_unflatten_cholesky()` function to take `dim` argument instead of infering it (dynamically) from the `chol_params` input vector. This avoids JIT compilation issues. Also update docstrings.
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Commits on Sep 11, 2024
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Tests: Check full-rank covariance matrix
Add assert statements that verify full-rank VI recovers the true, full-rank covariance matrix.
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