Dingling Yao
@Dingling_Yao
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Joined May 2022
💫 new preprint 💫 Wanna see causal representation learning algorithms successfully applied to real-world dynamical systems? Joint work with CarolineMuller and @FrancescoLocat8 Paper:
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RT @NriaArmengol2: Tired of causally confused agents when learning from offline datasets? We propose 🚣🏼♀️CAIAC🚣🏼♀️, a method for counterf…
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🤩Check out our poster tomorrow morning at #ICML2024
☀️ ICML2024 ☀️ Tuesday 11:30-13:00 #2201 ❓Do you want to identify high-level causal variables from perceptual data under partial observable setup? 💡Here we focus on learning from unpaired observations from a dataset with an instance-dependent partial observability pattern.
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RT @riccardocadeii: ✨ New preprint ✨ 🪜 Are you aiming to use deep learning to reach causality in mean (e.g., treatment effect estimation)?…
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🦥🦥🦥🦥🦥🦥 Our **mechanistic identifier** also successfully answers causal downstream questions, showing the global warming effect that is surprisingly in line with existing literature on climate change.
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More details at #ICLR When: Friday 10, May 10:45-12:45 Where: Hall B — No. 194
✨ ICLR2024 Spotlight ✨ Still want to identify causal variables in a partially observed setup? 🦥 We present a unified framework for studying the identifiability of representations learned from simultaneously, partially observed views, such as different data modalities. 🍍
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Joint work with a great team: @Danrunnnn @seblachap @saramagliacane @PerouzT @GMartius @JKugelgen @FrancescoLocat8 🦥 paper: 🦥 code:
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