6 papers
Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
Dario Rancati, Max Welling, Francesco Locatello
Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects, and feedback mediated by d…
Towards a holistic understanding of Selection Bias for Causal Effect Identification
Yiwen Qiu, Filip KovaÄeviÄ, Shimeng Huang +2
Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher soc…
Controlling Transient Amplification Improves Long-horizon Rollouts
Adeel Pervez, Francesco Locatello
Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when rolled out over long horizons…
The Rate-Distortion-Polysemanticity Tradeoff in SAEs
Tommaso Mencattini, Francesco Montagna, Francesco Locatello
Sparse Autoencoders (SAEs) that can accurately reconstruct their input (minimizing distortion) by making efficient use of few features (minimizing the rate) often fail to learn mon…
Causal Learning with the Invariance Principle
Francesco Montagna, Francesco Locatello
Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) to show that assuming that the…
Learning Discrete Diffusion of Graphs via Free-Energy Gradient Flows
Dario Rancati, Jan Maas, Francesco Locatello
Diffusion-based models on continuous spaces have seen substantial recent progress through the mathematical framework of gradient flows, leveraging the Wasserstein-2 () metri…