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Found 9 papers, 4 papers with code
Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models
As we train our predictive models jointly with a conformer decoder, the new latent embeddings can be mapped to their corresponding inputs, which we call \textit{MoleCLUEs}, or (molecular) counterfactual latent uncertainty explanations \citep{antoran2020getting}.
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems.
Uncertainty Surrogates for Deep Learning
We show how our approach can be used for estimating uncertainty in prediction and out-of-distribution detection.
Deep Smoothing of the Implied Volatility Surface
Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments.
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure.
Generative Models for Simulating Mobility Trajectories
We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.
Single-Model Uncertainties for Deep Learning
To estimate epistemic uncertainty, we propose Orthonormal Certificates (OCs), a collection of diverse non-constant functions that map all training samples to zero.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
