6 papers
Adaptive Training of INRs via Pruning and Densification
Diana Aldana, João Paulo Lima, Daniel Csillag +4
Encoding input coordinates with sinusoidal functions into multilayer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of low-dimensional signals,…
Differentially Private E-Values
Daniel Csillag, Diego Mesquita
E-values have gained prominence as flexible tools for statistical inference and risk control, enabling anytime- and post-hoc-valid procedures under minimal assumptions. However, ma…
Extending Prediction-Powered Inference through Conformal Prediction
Daniel Csillag, Pedro Dall'Antonia, Claudio José Struchiner +1
Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, man…
Image Super-Resolution with Guarantees via Conformalized Generative Models
Eduardo Adame, Daniel Csillag, Guilherme Tegoni Goedert
The increasing use of generative ML foundation models for image restoration tasks such as super-resolution calls for robust and interpretable uncertainty quantification methods. We…
Prediction-Powered E-Values
Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert
Quality statistical inference requires a sufficient amount of data, which can be missing or hard to obtain. To this end, prediction-powered inference has risen as a promising metho…
Strategic Conformal Prediction
Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert
When a machine learning model is deployed, its predictions can alter its environment, as better informed agents strategize to suit their own interests. With such alterations in min…