Search Results for author: Jonathan P. Williams

Found 7 papers, 1 papers with code

Multivariate and Online Transfer Learning with Uncertainty Quantification

no code implementations19 Nov 2024 Jimmy Hickey, Jonathan P. Williams, Brian J. Reich, Emily C. Hector

Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants.

Transfer Learning Uncertainty Quantification

Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict

no code implementations18 Oct 2024 David Randahl, Jonathan P. Williams, Håvard Hegre

Forecasting of armed conflicts is an important area of research that has the potential to save lives and prevent suffering.

Conformal Prediction Prediction Intervals

Uncertainty quantification in automated valuation models with locally weighted conformal prediction

no code implementations11 Dec 2023 Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams

Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncertainty.

Conformal Prediction Uncertainty Quantification

Valid Inference for Machine Learning Model Parameters

1 code implementation21 Feb 2023 Neil Dey, Jonathan P. Williams

However, this can come with the risk of overtraining; in order for the model to generalize well, it is of great importance that we are able to find the optimal parameter for the model on the entire population -- not only on the given training sample.

valid

Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)

no code implementations29 Nov 2022 Jimmy Hickey, Jonathan P. Williams, Emily C. Hector

Most existing transfer learning approaches are based on fine-tuning pre-trained neural network models, and fail to provide crucial uncertainty quantification.

Transfer Learning Uncertainty Quantification

A Tale of Two Transition Disks: ALMA long-baseline observations of ISO-Oph 2 reveal two closely packed non-axisymmetric rings and a $\sim$2 au cavity

no code implementations7 Oct 2020 Camilo González-Ruilova, Lucas A. Cieza, Antonio S. Hales, Sebastián Pérez, Alice Zurlo, Carla Arce-Tord, Simón Casassus, Hector Cánovas, Mario Flock, Gregory J. Herczeg, Paola Pinilla, Daniel J. Price, David A. Principe, Dary Ruíz-Rodríguez, Jonathan P. Williams

ISO-Oph 2 is a wide-separation (240 au) binary system where the primary star harbors a massive (M$_{dust}$ $\sim$40 M$_{\oplus}$) ring-like disk with a dust cavity $\sim$50 au in radius and the secondary hosts a much lighter (M$_{dust}$ $\sim$0. 8 M$_{\oplus}$) disk.

Earth and Planetary Astrophysics Solar and Stellar Astrophysics 85-11 J.2

The ALMA Early Science view of FUor/EXor objects. I. Through the looking-glass of V2775 Ori

no code implementations2 Nov 2016 Alice Zurlo, Lucas A. Cieza, Jonathan P. Williams, Hector Canovas, Sebastian Perez, Antonio Hales, Koraljka Mužić, David A. Principe, Dary Ruíz-Rodríguez, John Tobin, Yichen Zhang, Zhaohuan Zhu, Simon Casassus, Jose L. Prieto

We report the detection of a marginally resolved circumstellar disc in the ALMA continuum with an integrated flux of $106 \pm 2$ mJy, characteristic radius of $\sim$ 30 au, inclination of $14. 0^{+7. 8}_{-14. 5}$ deg, and is oriented nearly face-on with respect to the plane of the sky.

Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

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