Next Up on 07.December '22: Luigi Gresele, Ph.D. Candidate @ MPI:IS with Causal Inference Through the Structural Causal Marginal Problem @ ICML 2022
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We get together with the authors of papers in Causality x AI/ML to discuss their papers in a lively group discussion. We meet weekly on Wednesday at 15:30 UTC / 16:30 CEST/CET in summer/winter / 10:30 EST / 23:30 JST.

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"If the discussions become too causal, then we might as well make it more casual." (warning: a joke that requires looking closely)

🌐 Everything to be found in our Slack Community:
We keep a long list of interesting papers (see list in
#general
channel at “List of Potential Next Paper Discussions”), from which we vote for our session papers (vote at
#vote
channel), and also recordings from our past sessions all self-enclosed in the Slack community (find in
#recordings
channel).

Past Sessions (see #recordings):

  • Session 30.11.2022 | Selecting Data Augmentation for Simulating Interventions | Discussant: Maximilian Ilse
  • Session 23.11.2022 | On Disentangled Representations Learned from Correlated Data | Discussant: Frederik Träuble
  • Session 16.11.2022 | Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Repr. Learning | Discussant: Sumedh Sontakke
  • Session 09.11.2022 | Causal Machine Learning: A Survey and Open Problems | Discussants: Jean Kaddour, Aengus Lynch
  • Session 02.11.2022 | A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models | Discussant: Severi Rissanen
  • Session 26.10.2022 | Nonlinear Invariant Risk Minimization: A Causal Approach | Discussant: Chaochao Lu
  • Session 19.10.2022 | CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models | Discussant: Mengyue Yang
  • Session 12.10.2022 | Weakly Supervised Causal Representation Learning | Discussant: Johann Brehmer
  • Session 05.10.2022 | Towards Causal Representation Learning | Discussant: Anirudh Goyal
  • Session 21.09.2022 | Selection Collider Bias in Large Language Models | Discussant: Emily McMilin
  • Session 14.09.2022 | The Causal-Neural Connection: Expressiveness, Learnability, and Inference | Discussants: Kai-Zhan Lee, Kevin Xia
  • Session 07.09.2022 | Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style | Discussant: Julius von Kügelgen
  • Session 31.08.2022 | Interventions, Where and How? Experimental Design for Causal Models at Scale | Discussants: Panagiotis Tigas and Yashas Annadani
  • Session 24.08.2022 | Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game | Discussant: Alexander Reisach
  • Session 17.08.2022 | Effect Identification in Cluster Causal Diagrams | Discussants: Tara Anand and Adèle Ribeiro
  • Session 10.08.2022 | Can Foundation Models Talk Causality? | Discussant: Moritz Willig
  • Session 03.08.2022 | Causal Conceptions of Fairness and their Consequences | Discussants: Hamed Nilforoshan and Johann Gaebler
  • Session 28.07.2022 | Bayesian Causal Discovery under Unknown Interventions | Discussant: Alexander Hägele
  • Session 20.07.2022 | Counterfactual Fairness | Discussant: Toon Vanderschueren

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