Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Dreaming of Federated Robustness: Inherent Barriers and Unavoidable Tradeoffs
      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
            Dreaming of Federated Robustness: Inherent Barriers and Unavoidable Tradeoffs
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Dreaming of Federated Robustness: Inherent Barriers and Unavoidable Tradeoffs

            Jul 23, 2021

            Sprecher:innen

            DP

            Dimitris Papailiopoulos

            Sprecher:in · 0 Follower:innen

            Organisator

            I2
            I2

            ICML 2021

            Konto · 1,1k Follower:innen

            Über ICML 2021

            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

            Gefällt euch das Format? Vertraut auf SlidesLive, um euer nächstes Event festzuhalten!

            Professionelle Aufzeichnung und Livestreaming – weltweit.

            Freigeben

            Empfohlene Videos

            Präsentationen, deren Thema, Kategorie oder Sprecher:in ähnlich sind

            Pure Exploration and Regret Minimization in Matching Bandits
            05:16

            Pure Exploration and Regret Minimization in Matching Bandits

            Flore Sentenac, …

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            Q&A for invited talks 5 and 6
            09:46

            Q&A for invited talks 5 and 6

            Lily Hu, …

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
            03:11

            Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition

            Benjamin Spetter-Goldstein, …

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            DAGs with No Curl: An Efficient DAG Structure Learning Approach
            05:15

            DAGs with No Curl: An Efficient DAG Structure Learning Approach

            Yue Yu, …

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            Opening
            02:48

            Opening

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
            05:20

            Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

            Botao Hao, …

            I2
            I2
            ICML 2021 4 years ago

            Ewigspeicher-Fortschrittswert: 0 = 0.0%

            Interessiert an Vorträgen wie diesem? ICML 2021 folgen