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It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence.
The direction of time , volume 65
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Causality in thought
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Distinguishing cause from effect using observational data: methods and benchmarks
Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf · 2016
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
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Relational inductive biases, deep learning, and graph networks
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Towards formal definitions of blameworthiness, intention, and moral responsibility
Joseph Halpern and Max Kleiman-Weiner · 2018
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Causality from a distributional robustness point of view
Nicolai Meinshausen · 2018
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Causal inference via kernel deviance measures
Jovana Mitrovic, Dino Sejdinovic, and Yee Whye Teh · 2018
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Theoretical impediments to machine learning with seven sparks from the causal revolution
Judea Pearl · 2018
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Causally regularized learning with agnostic data selection bias
Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, and Peixuan Chen · 2018
Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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Selection, ignorability and challenges with causal fairness
Jake Fawkes, Robin Evans, and Dino Sejdinovic · 2022
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Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 2022
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Emergent world representations: Exploring a sequence model trained on a synthetic task
Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2022
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Acquisition of chess knowledge in AlphaZero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Martin Wattenberg, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik · 2022
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Current structure predictors are not learning the physics of protein folding
Carlos Outeiral, Daniel A Nissley, and Charlotte M Deane · 2022
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Promises and challenges of causality for ethical machine learning
Aida Rahmattalabi and Alice Xiang · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Counterfactual harm
Jonathan Richens, Rory Beard, and Daniel H Thompson · 2022
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Goal misgeneralization: Why correct specifications aren’t enough for correct goals
Rohin Shah, Vikrant Varma, Ramana Kumar, Mary Phuong, Victoria Krakovna, Jonathan Uesato, and Zac Kenton · 2022
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D’ya like dags? a survey on structure learning and causal discovery
Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden · 2022
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu · 2022
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Context is environment, 2023
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, and Kartik Ahuja · 2023
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Language models represent space and time
Wes Gurnee and Max Tegmark · 2023
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Discovering agents
Zachary Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan Richens, Matt MacDermott, and Tom Everitt · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
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A survey of zero-shot generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2023
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Actually, othello-gpt has a linear emergent world model, Mar 2023
Neel Nanda · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Defining deception in structural causal games
Francis Rhys Ward, Francesca Toni, and Francesco Belardinelli · 2023
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Fixing the good regulator theorem
John Wentworth · 2023
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Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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The reasons that agents act: Intention and instrumental goals
Francis Rhys Ward, Matt MacDermott, Francesco Belardinelli, Francesca Toni, and Tom Everitt · 2024
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