Zobrazeno 1 - 10
of 964
pro vyhledávání: '"WILLIAMSON, P. C."'
What does it mean to say that, for example, the probability for rain tomorrow is between 20% and 30%? The theory for the evaluation of precise probabilistic forecasts is well-developed and is grounded in the key concepts of proper scoring rules and c
Externí odkaz:
http://arxiv.org/abs/2410.23001
Machine Learning research, as most of Statistics, heavily relies on the concept of a data-generating probability distribution. The standard presumption is that since data points are `sampled from' such a distribution, one can learn from observed data
Externí odkaz:
http://arxiv.org/abs/2407.17395
The most common approach to causal modelling is the potential outcomes framework due to Neyman and Rubin. In this framework, outcomes of counterfactual treatments are assumed to be well-defined. This metaphysical assumption is often thought to be pro
Externí odkaz:
http://arxiv.org/abs/2407.17385
We study the predictability of online speech on social media, and whether predictability improves with information outside a user's own posts. Recent work suggests that the predictive information contained in posts written by a user's peers can surpa
Externí odkaz:
http://arxiv.org/abs/2407.12850
Supervised learning has gone beyond the expected risk minimization framework. Central to most of these developments is the introduction of more general aggregation functions for losses incurred by the learner. In this paper, we turn towards online le
Externí odkaz:
http://arxiv.org/abs/2406.02292
Motivated by recently emerging problems in machine learning and statistics, we propose data models which relax the familiar i.i.d. assumption. In essence, we seek to understand what it means for data to come from a set of probability measures. We sho
Externí odkaz:
http://arxiv.org/abs/2404.09741
We introduce a notion of distance between supervised learning problems, which we call the Risk distance. This optimal-transport-inspired distance facilitates stability results; one can quantify how seriously issues like sampling bias, noise, limited
Externí odkaz:
http://arxiv.org/abs/2403.01660
Autor:
Derr, Rabanus, Williamson, Robert C.
Machine learning is about forecasting. Forecasts, however, obtain their usefulness only through their evaluation. Machine learning has traditionally focused on types of losses and their corresponding regret. Currently, the machine learning community
Externí odkaz:
http://arxiv.org/abs/2401.14483
Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature on corruption predominantly focuses on specific settings and learning scenarios, lacking a unified view. There is still a limited understandi
Externí odkaz:
http://arxiv.org/abs/2307.08643
We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of unc
Externí odkaz:
http://arxiv.org/abs/2306.14624