Álvaro Tejero-Cantero — accepted AI-conference papers

2024

Predictive, scalable and interpretable knowledge tracing on structured domains

Hanqi Zhou, Robert Bamler, Charley M Wu, Álvaro Tejero-Cantero

ICLR 2024spotlight

Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress ("knowledge tracing"; KT), and the prerequisite structure of the learning domain ("knowledge mapping"). While…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2022

GATSBI: Generative Adversarial Training for Simulation-Based Inference

Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Goncalves +1

ICLR 2022poster

Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and i…

Cited by 39 Source ↗PDF ↗code ↗Scholar ↗