[1/3] Excited to announce “Semantic uncertainty intervals for disentangled latent spaces”. Poster Session 6 at #Neurips2022!
We show how to construct uncertainty intervals with meaning, eg. on a person’s hair color, or the amount they are smiling! @trustworthy_ml
We show how to construct uncertainty intervals with meaning, eg. on a person’s hair color, or the amount they are smiling! @trustworthy_ml
[2/3] We use conformal prediction, quantile regression (QR), and GANs to go beyond pixel-level uncertainty, getting rigorous uncertainty quantification for image generation!
The idea is to perform QR on disentangled GAN latents, then calibrate it with conformal risk control.
The idea is to perform QR on disentangled GAN latents, then calibrate it with conformal risk control.
[3 / 3] This was joint work with @ml_angelopoulos, @stats_stephen, @yaniv_romano, and @phillip_isola. If you want to come by talk about rigorous statistics for generative models, please visit our poster at Poster Session 6 or DM me to chat!
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