Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v3-stream-013-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v3-stream-013-alpha.b-cdn.net
      • sl-yoda-v3-stream-013-beta.b-cdn.net
      • 1668715672.rsc.cdn77.org
      • 1420896597.rsc.cdn77.org
      • Subtitles
      • Off
      • English
      • Playback rate
      • Quality
      • Subtitles size
      • Large
      • Medium
      • Small
      • Mode
      • Video Slideshow
      • Audio Slideshow
      • Slideshow
      • Video
      My playlists
        Bookmarks
          00:00:00
            Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

            Dec 6, 2021

            Speakers

            YC

            Youngseog Chung

            Speaker · 0 followers

            WN

            Willie Neiswanger

            Speaker · 0 followers

            IC

            Ian Char

            Speaker · 0 followers

            About

            Among the many ways of quantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A model that predicts the true conditional quantiles for each input, at all quantile levels, presents a correct and efficient representation of the underlying uncertainty. To achieve this, many current quantile-based methods focus on optimizing the pinball loss. However, this loss restricts the scope of applicable…

            Organizer

            N2
            N2

            NeurIPS 2021

            Account · 1.9k followers

            About NeurIPS 2021

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

            Like the format? Trust SlidesLive to capture your next event!

            Professional recording and live streaming, delivered globally.

            Sharing

            Recommended Videos

            Presentations on similar topic, category or speaker

            Interested in talks like this? Follow NeurIPS 2021