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Workshop

Causal Representation Learning

Sara Magliacane ⋅ Atalanti Mastakouri ⋅ Yuki Asano ⋅ Claudia Shi ⋅ Cian Eastwood ⋅ Sébastien Lachapelle ⋅ Bernhard Schölkopf ⋅ Caroline Uhler
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Abstract

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Schedule

Timezone: America/Los_Angeles
8:30 AM
Poster session
3:20 PM
Closing remarks
Learning Object Motion and Appearance Dynamics with Object-Centric Representations
Yeon-Ji Song ⋅ Hyunseo Kim ⋅ Suhyung Choi ⋅ Jin-Hwa Kim ⋅ Byoung-Tak Zhang
Attention for Causal Relationship Discovery from Biological Neural Dynamics
Ziyu Lu ⋅ Anika Tabassum ⋅ Shruti Kulkarni ⋅ Lu Mi ⋅ Nathan Kutz ⋅ Eric Shea-Brown ⋅ Seung-Hwan Lim
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Neehal Tumma ⋅ Mathias Lechner ⋅ Noel Loo ⋅ Ramin Hasani ⋅ Daniela Rus
Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models
Sean Kulinski ⋅ Zeyu Zhou ⋅ Ruqi Bai ⋅ Murat Kocaoglu ⋅ David Inouye
Inverted-Attention Transformers can Learn Object Representations: Insights from Slot Attention
Yi-Fu Wu ⋅ Klaus Greff ⋅ Gamaleldin Elsayed ⋅ Michael Mozer ⋅ Thomas Kipf ⋅ Sjoerd van Steenkiste
Triangular Monotonic Generative Models Can Perform Causal Discovery
Quanhan (Johnny) Xi ⋅ Sebastian Gonzalez ⋅ Benjamin Bloem-Reddy
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Jacqueline Maasch ⋅ Weishen Pan ⋅ Shantanu Gupta ⋅ Volodymyr Kuleshov ⋅ Kyra Gan ⋅ Fei Wang
Exploiting Causal Representations in Reinforcement Learning: A Posterior Sampling Approach
Mirco Mutti ⋅ Riccardo De Santi ⋅ Marcello Restelli ⋅ Alexander Marx ⋅ Giorgia Ramponi
Identifying Effects of Disease on Single-Cells with Domain-Invariant Generative Modeling
Abdul Moeed ⋅ Martin Rohbeck ⋅ Pavlo Lutsik ⋅ Kai Ueltzhoeffer ⋅ Marc Jan Bonder ⋅ Oliver Stegle
Causal Regressions For Unstructured Data
Amandeep Singh ⋅ Bolong Zheng
Expediting Reinforcement Learning by Incorporating Temporal Causal Information
Jan Corazza ⋅ Daniel Neider ⋅ Zhe Xu ⋅ Hadi Partovi Aria
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu ⋅ Dingling Yao ⋅ Sébastien Lachapelle ⋅ Perouz Taslakian ⋅ Julius von Kügelgen ⋅ Francesco Locatello ⋅ Sara Magliacane
Hierarchical Causal Representation Learning
Angelos Nalmpantis ⋅ Phillip Lippe ⋅ Sara Magliacane
The Linear Representation Hypothesis in Language Models
Kiho Park ⋅ Yo Joong Choe ⋅ Victor Veitch
Debiasing Multimodal Models via Causal Information Minimization
Vaidehi Patil ⋅ Adyasha Maharana ⋅ Mohit Bansal
Unfairness Detection within Power Systems through Transfer Counterfactual Learning
Song Wei ⋅ Xiangrui Kong ⋅ Sarah Huestis-Mitchell ⋅ Yao Xie ⋅ Shixiang Zhu ⋅ Alinson Xavier ⋅ Feng Qiu
Independent Mechanism Analysis and the Manifold Hypothesis: Identifiability and Genericity
Shubhangi Ghosh ⋅ Luigi Gresele ⋅ Julius von Kügelgen ⋅ Michel Besserve ⋅ Bernhard Schölkopf
Score-based Causal Representation Learning from Interventions: Nonparametric Identifiability
Burak Varıcı ⋅ Emre Acartürk ⋅ Karthikeyan Shanmugam ⋅ Ali Tajer
Counterfactual Generative Models for Time-Varying Treatments
Shenghao Wu ⋅ Wenbin Zhou ⋅ Minshuo Chen ⋅ Shixiang Zhu
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood ⋅ Julius von Kügelgen ⋅ Linus Ericsson ⋅ Diane Bouchacourt ⋅ Pascal Vincent ⋅ Mark Ibrahim ⋅ Bernhard Schölkopf
Object-Centric Semantic Vector Quantization
Yi-Fu Wu ⋅ Minseung Lee ⋅ Sungjin Ahn
Towards the Reusability and Compositionality of Causal Representations
Davide Talon ⋅ Phillip Lippe ⋅ Stuart James ⋅ Alessio Del Bue ⋅ Sara Magliacane
Object-centric architectures enable efficient causal representation learning
Amin Mansouri ⋅ Jason Hartford ⋅ Yan Zhang ⋅ Yoshua Bengio
Putting Causal Identification to the Test: Falsification using Multi-Environment Data
Rickard Karlsson ⋅ Ștefan Creastă ⋅ Jesse Krijthe
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao ⋅ Danru Xu ⋅ Sébastien Lachapelle ⋅ Sara Magliacane ⋅ Perouz Taslakian ⋅ Georg Martius ⋅ Julius von Kügelgen ⋅ Francesco Locatello
Invariance & Causal Representation Learning: Prospects and Limitations
Simon Bing ⋅ Jonas Wahl ⋅ Urmi Ninad ⋅ Jakob Runge
Curvature and Causal Inference in Network Data
Amirhossein Farzam ⋅ Allen Tannenbaum ⋅ Guillermo Sapiro
Causal Modeling with Stationary Diffusions
Lars Lorch ⋅ Andreas Krause ⋅ Bernhard Schölkopf
Causal Markov Blanket Representations for Domain Generalization Prediction
Naiyu Yin ⋅ Hanjing Wang ⋅ Tian Gao ⋅ Amit Dhurandhar ⋅ Qiang Ji
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam ⋅ Elan Rosenfeld ⋅ Pradeep Ravikumar ⋅ Niklas Pfister ⋅ Jonas Peters
Learning Causally-Aware Representations of Multi-Agent Interactions
Yuejiang Liu ⋅ Ahmad Rahimi ⋅ Po-Chien Luan ⋅ Frano Rajič ⋅ Alexandre Alahi
A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori ⋅ Pedro Sanchez ⋅ Konstantinos Vilouras ⋅ Ben Glocker ⋅ Sotirios Tsaftaris
Learning Macro Variables with Auto-encoders
Dhanya Sridhar ⋅ Eric Elmoznino ⋅ Maitreyi Swaroop