Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations--an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible.

Our code is available at GitHub.

V ov95, CBL06] and online convex optimization [Haz16, Ora19] have been developed. Until the labels of all examples of X have been predicted: The learning algorithm picks a point x X and makes a prediction z R about its label.

To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process.

Text-to-Query systems have surprisingly not been investigated so far.

We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed.

In this paper, we study the same problem in the Massart noise model that we now define.

Item Discrimination (ID) theory, which is widely used in educational assessment, measures the ability of individual test items to differentiate between high and low performers. Inspired by this theory, wepropose anID-induced prompt synthesis frameworkforevaluating LLMs to ensure the evaluation set can continually update and refine according to model abilities.