Research
tinyboxes is a AI research lab that uses physical testbeds to study the behavior of AI systems and allow AI agents to interact with the physical world, learning from it. We actively publish research and are a team of researchers working on the intersection of AI and physics.
tinyboxes as a real-world physical testbed for AI methodology
Juan L. Gamella, Jonas Peters and Peter Bühlmann
Nature Machine IntelligenceContext is Key: A Benchmark for Forecasting with Essential Textual Information
Arjun Ashok, Andrew Robert Williams, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
NeurIPS 2024 Workshop on Time Series in the Age of Large ModelsAn Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models
Tong Xu, Armeen Taeb, Simge Kuccukyavuz, Ali Shojaie
arXiv preprint arXiv:2408.11977The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
arXiv preprint arXiv:2412.01953Sortability of Time Series Data
Christopher Lohse and Jonas Wahl
CI4TS Workshop @ UAI 2024Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann and Jianqing Fan
arXiv preprint arXiv:2405.04715Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi and Jörn-Henrik Jacobsen
arXiv preprint arXiv:2405.08719Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
arXiv preprint arXiv:2404.09962Sanity Checking Causal Representation Learning on a Simple Real-World System
tinyboxes team, Simon Bing, Jakob Runge
arXiv preprint arXiv:2502.20099