Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Muandet, Krikamol"'
We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents epistemic uncertainty that ari
Externí odkaz:
http://arxiv.org/abs/2410.12921
Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e.g., optimising the average-case risk, worst-case risk, or interpolations t
Externí odkaz:
http://arxiv.org/abs/2404.04669
Autor:
Adachi, Masaki, Planden, Brady, Howey, David A., Osborne, Michael A., Orbell, Sebastian, Ares, Natalia, Muandet, Krikamol, Chau, Siu Lun
Publikováno v:
AISTATS 238, 505--513, 2024
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing us
Externí odkaz:
http://arxiv.org/abs/2310.17273
We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior work focuses on studying a fixed pool of agents that remains static reg
Externí odkaz:
http://arxiv.org/abs/2308.16262
Test-time defenses are used to improve the robustness of deep neural networks to adversarial examples during inference. However, existing methods either require an additional trained classifier to detect and correct the adversarial samples, or perfor
Externí odkaz:
http://arxiv.org/abs/2307.11672
We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular solution concept of Shapley values extended to stochastic cooperative gam
Externí odkaz:
http://arxiv.org/abs/2305.15167
Causality is a central concept in a wide range of research areas, yet there is still no universally agreed axiomatisation of causality. We view causality both as an extension of probability theory and as a study of \textit{what happens when one inter
Externí odkaz:
http://arxiv.org/abs/2305.17139
Being able to provide explanations for a model's decision has become a central requirement for the development, deployment, and adoption of machine learning models. However, we are yet to understand what explanation methods can and cannot do. How do
Externí odkaz:
http://arxiv.org/abs/2212.06925
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield a sufficient condition for a predictor to be counterfactually invarian
Externí odkaz:
http://arxiv.org/abs/2207.09768