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Found 7 papers, 7 papers with code
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense.
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
However, generative networks only allow sampling from the parametrized distribution; for this reason, Ramesh et al. [2022] follows the common solution of adversarial training, where the generative network plays a min-max game against a "critic" network.
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
Score Matched Neural Exponential Families for Likelihood-Free Inference
Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations.
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England
A mathematical model for the COVID-19 pandemic spread in England is presented.
Applications Physics and Society Populations and Evolution
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption.
Computation Applications
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
