8 citations · 23 across the 7 of their papers we have counts for
7 papers
Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents
Josefa Lia Stoisser, Marc Boubnovski Martell, Sidsel Boldsen +2
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, prod…
Measuring Black-Box Confidence via Reasoning Trajectories: Geometry, Coverage, and Verbalization
Marc Boubnovski Martell, Josefa Lia Stoisser, Kaspar Märtens +4
Reliable confidence estimation enables safe deployment of chain-of-thought (CoT) reasoning through text-only APIs. Yet the dominant black-box baseline, self-consistency over K samp…
Disentangling shared and private latent factors in multimodal Variational Autoencoders
Kaspar Märtens, Christopher Yau
Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or share…
Neural Decomposition: Functional ANOVA with Variational Autoencoders
Kaspar Märtens, Christopher Yau
Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded w…
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
Kaspar Märtens, Christopher Yau
Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. However, in application domains such as genomics where data sets…
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens, Kieran R. Campbell, Christopher Yau
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. Howeve…
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
Kaspar Märtens, Michalis K Titsias, Christopher Yau
Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effec…