1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach
Yiwen Qiu, Francesco Montagna, Shimeng Huang +1
Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not representative of the underlyin…
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…
Demystifying amortized causal discovery with transformers
Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar +1
Supervised learning for causal discovery from observational data often achieves competitive performance despite seemingly avoiding the explicit assumptions that traditional methods…
Scalable Causal Discovery with Score Matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +2
This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveragi…
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +2
Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in o…