Row 66143

Row ID: 66143 | Dataset Entry | Axioma AXP Content Repository

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So, I used to have the same understanding as you wrote above.

Though, doesn't this paper \[1\] show the opposite? That for Sparse GP models, FITC (minimizes fKL) UNDERESTIMATES the predictive variance and VFE (minimizes rKL) OVERESTIMATES it (see section 3.1). This result is what started to confuse me really and what made me interested in the question. Might just be something specific to sparse GPs though...

\[1\] Bauer, M., Van der Wilk, M., & Rasmussen, C. E. (2016). Understanding probabilistic sparse Gaussian process approximations. *Advances in neural information processing systems*, *29*

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text So, I used to have the same understanding as you wrote above. Though, doesn't this paper \[1\] show the opposite? That for Sparse GP models, FITC (minimizes fKL) UNDERESTIMATES the predictive variance and VFE (minimizes rKL) OVERESTIMATES it (see section 3.1). This result is what started to confuse me really and what made me interested in the question. Might just be something specific to sparse GPs though... \[1\] Bauer, M., Van der Wilk, M., & Rasmussen, C. E. (2016). Understanding probabi…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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  "text": "So, I used to have the same understanding as you wrote above.\n\nThough, doesn't this paper \\[1\\] show the opposite? That for Sparse GP models, FITC (minimizes fKL) UNDERESTIMATES the predictive variance and VFE (minimizes rKL) OVERESTIMATES it (see section 3.1). This result is what started to confuse me really and what made me interested in the question. Might just be something specific to sparse GPs though...\n\n  \n\\[1\\] Bauer, M., Van der Wilk, M., & Rasmussen, C. E. (2016). Understanding probabilistic sparse Gaussian process approximations. *Advances in neural information processing systems*, *29*",
  "label": "r/machinelearning",
  "dataType": "comment",
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  "datetime": "2024-05-23",
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