Mohit Rajpal

TFP optimizers should take an "additional_args" argument which is passed through to value_and_gradients_function

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Consider the following scenario (shamelessly copied from TFP tutorial) ``` import numpy as np import tensorflow as tf import tensorflow_probability as tfp np.random.seed(12345) @tf.function def quadratic(minimum, x): with tf.GradientTape() as...

Fixed broadcasting rules for gpflow.models.model.predict_y, partially resolves #1461.

1

**PR type:** new feature **Related issue(s)/PRs:#1461 ## Summary **Proposed changes** This proposed change fixes the broadcasting rules for predict_y to correctly handle full_cov/full_output_cov = True. The changes made rely on...

predict_y and predict_log_density support for full_cov or full_output_cov

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With latest version of GPFlow 2.0.2, calling predict_y with a SVGP model with full_cov = True results on a covariance matrix where the likelihood noise value is added to every...

When I proposed my solution to this problem, I was not sure whether the proof given by the TA did not result in a logical contradiction. Can it be upgraded...