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06/20/2023
MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty
Structure-based models in the molecular sciences can be highly sensitive...
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06/01/2023
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Many scientific and industrial applications require joint optimization o...
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02/24/2023
Retrospective Uncertainties for Deep Models using Vine Copulas
Despite the major progress of deep models as learning machines, uncertai...
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11/07/2022
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Latent variable models such as the Variational Auto-Encoder (VAE) have b...
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10/19/2022
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based ...
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10/18/2022
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Artificial intelligence (AI) is revolutionizing many areas of our lives,...
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04/16/2021
Uncertainty Surrogates for Deep Learning
In this paper we introduce a novel way of estimating prediction uncertai...
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06/12/2019
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
We propose a vine copula autoencoder to construct flexible generative mo...
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06/12/2019
Deep Smoothing of the Implied Volatility Surface
We present an artificial neural network (ANN) approach to value financia...
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11/30/2018
Generative Models for Simulating Mobility Trajectories
Mobility datasets are fundamental for evaluating algorithms pertaining t...
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11/02/2018
Frequentist uncertainty estimates for deep learning
We provide frequentist estimates of aleatoric and epistemic uncertainty ...
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01/31/2018