activity
20172020
most citedEvaluating model calibration in classification

91 citations · 91 across the 1 of their papers we have counts for

collaborators

6 papers

stat.AP2020

Self-driving car safety quantification via component-level analysis

Juozas Vaicenavicius, Tilo Wiklund, Austė Grigaitė +3

In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The meth…

cs.LG2019★ 91 cited

Evaluating model calibration in classification

Juozas Vaicenavicius, David Widmann, Carl Andersson +3

Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision m…

math.ST2019

Bayesian sequential least-squares estimation for the drift of a Wiener process

Erik Ekström, Ioannis Karatzas, Juozas Vaicenavicius

Given a Wiener process with unknown and unobservable drift, we try to estimate this drift as effectively but also as quickly as possible, in the presence of a quadratic penalty for…

math.ST2017

Monotonicity and robustness in Wiener disorder detection

Erik Ekström, Juozas Vaicenavicius

We study the problem of detecting a drift change of a Brownian motion under various extensions of the classical case. Specifically, we consider the case of a random post-change dri…

math.PR2017

Optimal stopping of a Brownian bridge with an unknown pinning point

Erik Ekström, Juozas Vaicenavicius

The problem of stopping a Brownian bridge with an unknown pinning point to maximise the expected value at the stopping time is studied. A few general properties, such as continuity…

q-fin.MF2017

Asset liquidation under drift uncertainty and regime-switching volatility

Juozas Vaicenavicius

Optimal liquidation of an asset with unknown constant drift and stochastic regime-switching volatility is studied. The uncertainty about the drift is represented by an arbitrary pr…