When Does a Predictor Know Its Own Loss?

Apple Machine Learning Research

Given a predictor and a loss function, how well can we predict the loss that the predictor will incur on an input? This is the problem of loss prediction, a key computational task associated with uncertainty estimation for a predictor. In a classification setting, a predictor will typically predict a distribution over labels and hence have its own estimate of the loss that it will incur, given by the entropy of the predicted distribution. Should we trust this estimate? In other words, when does the predictor know what it knows and what it does not know?
In this work we study the theoretical…
Go to Source
11/03/2025 – 18:06 /
SoMe: @hoffeldt.bsky.social

Admin

About Admin

As an experienced Human Resources leader, I bring a wealth of expertise in corporate HR, talent management, consulting, and business partnering, spanning diverse industries such as retail, media, marketing, PR, graphic design, NGO, law, assurance, consulting, tax services, investment, medical, app/fintech, and tech/programming. I have primarily worked with service and sales companies at local, regional, and global levels, both in Europe and the Asia-Pacific region. My strengths lie in operations, development, strategy, and growth, and I have a proven track record of tailoring HR solutions to meet unique organizational needs. Whether it's overseeing daily HR tasks or crafting and implementing new processes for organizational efficiency and development, I am skilled in creating innovative human capital management programs and impactful company-wide strategic solutions. I am deeply committed to putting people first and using data-driven insights to drive business value. I believe that building modern and inclusive organizations requires a focus on talent development and daily operations, as well as delivering results. My passion for HRM is driven by a strong sense of empathy, integrity, honesty, humility, and courage, which have enabled me to build and maintain positive relationships with employees at all levels.

    You May Also Like

    error: Content is protected !!