Zobrazeno 1 - 10
of 986
pro vyhledávání: '"BATES, STEPHEN"'
Statistical protocols are often used for decision-making involving multiple parties, each with their own incentives, private information, and ability to influence the distributional properties of the data. We study a game-theoretic version of hypothe
Externí odkaz:
http://arxiv.org/abs/2412.16452
This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty quantifica
Externí odkaz:
http://arxiv.org/abs/2411.11824
Remote sensing map products are used to obtain estimates of environmental quantities, such as deforested area or the effect of conservation zones on deforestation. However, the quality of map products varies, and - because maps are outputs of complex
Externí odkaz:
http://arxiv.org/abs/2407.13659
Autor:
Nguyen, Drew T., Pathak, Reese, Angelopoulos, Anastasios N., Bates, Stephen, Jordan, Michael I.
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quanti
Externí odkaz:
http://arxiv.org/abs/2403.19605
We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a po
Externí odkaz:
http://arxiv.org/abs/2402.01139
Inference for prediction errors is critical in time series forecasting pipelines. However, providing statistically meaningful uncertainty intervals for prediction errors remains relatively under-explored. Practitioners often resort to forward cross-v
Externí odkaz:
http://arxiv.org/abs/2309.07435
Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two funda
Externí odkaz:
http://arxiv.org/abs/2309.01837