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Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance,…

Machine Learning · Statistics 2018-10-30 Heinrich Jiang , Been Kim , Melody Y. Guan , Maya Gupta

The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADAS). Existing safety performance justification of the…

Robotics · Computer Science 2022-05-25 Bowen Weng , Minghao Zhu , Keith Redmill

Einstein-Podolsky-Rosen (EPR) steering allows Alice to remotely prepare a state in some specific bases for Bob through her choice of measurements. The temporal analogue of EPR steering, temporal steering, also reveals the steerability of a…

Quantum Physics · Physics 2017-01-04 Huan-Yu Ku , Shin-Liang Chen , Hong-Bin Chen , Neill Lambert , Yueh-Nan Chen , Franco Nori

We show how to construct loss-tolerant linear steering inequalities using a generic set of von Neumann measurements that are violated by $d$-dimensional states, and that rely only upon a simple property of the set of measurements used (the…

Quantum Physics · Physics 2015-09-02 Paul Skrzypczyk , Daniel Cavalcanti

The notion of confidence distributions is applied to inference about the parameter in a simple autoregressive model, allowing the parameter to take the value one. This makes it possible to compare to asymptotic approximations in both the…

Methodology · Statistics 2023-03-28 Rolf Larsson

The term Einstein-Podolsky-Rosen steering refers to a quantum correlation intermediate between entanglement and Bell nonlocality, which has been connected to another fundamental quantum property: measurement incompatibility. In the…

Quantum Physics · Physics 2017-11-08 Jukka Kiukas , Costantino Budroni , Roope Uola , Juha-Pekka Pellonpää

This paper introduces Admissibility Alignment: a reframing of AI alignment as a property of admissible action and decision selection over distributions of outcomes under uncertainty, evaluated through the behavior of candidate policies. We…

Artificial Intelligence · Computer Science 2026-01-06 Chris Duffey

The paper develops a novel and general methodology to characterize the nonlinearity of structural systems and to provide a mathematically proven basis for applying partial safety factors to nonlinear structural systems. It establishes, for…

Applications · Statistics 2022-12-06 Tammam Bakeer

An appeal for symmetry is made to build established notions of specific representation and specific nonlinearity of measurement (often called model error) into a canonical linear regression model. Additive components are derived from the…

Applications · Statistics 2021-10-19 Richard E. Danielson

Steering is a manifestation of quantum correlations that embodies the Einstein-Podolsky-Rosen (EPR) paradox. While there have been recent attempts to quantify steering, continuous variable systems remained elusive. We introduce a steering…

Quantum Physics · Physics 2015-03-11 Ioannis Kogias , Gerardo Adesso

Steering, a quantum property stronger than entanglement but weaker than non-locality in the quantum correlation hierarchy, is a key resource for one-sided device-independent quantum key distribution applications, in which only one of the…

The current definition of a Bayesian credible set cannot, in general, achieve an arbitrarily preassigned credible level. This drawback is particularly acute for classification problems, where there are only a finite number of achievable…

Statistics Theory · Mathematics 2023-08-23 Chaegeun Song , Bing Li

To address the difficult problem of multi-step ahead prediction of non-parametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of non-parametric time series…

Methodology · Statistics 2023-11-02 Dimitris N. Politis , Kejin Wu

Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on…

Machine Learning · Statistics 2013-12-16 Wei Sun , Junhui Wang , Yixin Fang

Quantum steering, also called Einstein-Podolsky-Rosen steering, is the intriguing phenomenon associated with the ability of spatially separated observers to steer---by means of local measurements---the set of conditional quantum states…

Quantum Physics · Physics 2017-01-03 Chung-Yun Hsieh , Yeong-Cherng Liang , Ray-Kuang Lee

How do we design measures of social bias that we trust? While prior work has introduced several measures, no measure has gained widespread trust: instead, mounting evidence argues we should distrust these measures. In this work, we design…

Computation and Language · Computer Science 2024-07-19 Rishi Bommasani , Percy Liang

We study the problem of empirical coordination subject to a fidelity criterion for a general set-up. We prove a result which indicates a strong connection between our framework and the framework of empirical coordination developed in [1].…

Information Theory · Computer Science 2019-07-16 Michail Mylonakis , Photios A. Stavrou , Mikael Skoglund

Bell nonlocality between distant quantum systems---i.e., joint correlations which violate a Bell inequality---can be verified without trusting the measurement devices used, nor those performing the measurements. This leads to…

Quantum Physics · Physics 2015-01-16 Sacha Kocsis , Michael J. W. Hall , Adam J. Bennet , Dylan J. Saunders , G. J. Pryde

Quantum steering is the ability to remotely prepare different quantum states by using entangled pairs as a resource. Very recently, the concept of steering has been quantified with the use of inequalities, leading to substantial…

A vital stage in the mathematical modelling of real-world systems is to calibrate a model's parameters to observed data. Likelihood-free parameter inference methods, such as Approximate Bayesian Computation, build Monte Carlo samples of the…

Computation · Statistics 2021-12-23 Thomas P Prescott , Ruth E Baker
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