Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Muandet Krikamol"'
Publikováno v:
Journal of Causal Inference, Vol 11, Iss 1, Pp 2-63 (2023)
We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR). The MMR objective is formulated by maximizing the interaction betw
Externí odkaz:
https://doaj.org/article/9c6ab6fcd8ab48d8a5b5d1b45f463d07
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