Preethi Lahoti,Krishna Gummadi,Gerhard Weikum
Preethi Lahoti
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces the Risk Advisor, a novel post-hoc meta-learner for estimating fail...
Thomas Baumhauer,Pascal Schöttle,Matthias Zeppelzauer
Thomas Baumhauer
Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a "right to be forgotten". This poses a challenge to machine learning: how to proceed when an individ...
Relating instance hardness to classification performance in a dataset: a visual approach [0.03%]
基于实例难度的数据集分类性能研究：可视化方法
Pedro Yuri Arbs Paiva,Camila Castro Moreno,Kate Smith-Miles et al.
Pedro Yuri Arbs Paiva et al.
Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The results obtained are usually summarized through average statis...
Adversarial concept drift detection under poisoning attacks for robust data stream mining [0.03%]
基于中毒攻击的对抗概念漂移检测以实现稳健的数据流挖掘
Łukasz Korycki,Bartosz Krawczyk
Łukasz Korycki
Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning algorithms must not only be able to handle massive volume of rapidly arriving data, but also adapt t...
Scrutinizing XAI using linear ground-truth data with suppressor variables [0.03%]
使用抑制变量和线性基础事实数据审视XAI
Rick Wilming,Céline Budding,Klaus-Robert Müller et al.
Rick Wilming et al.
Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of procedures has been developed to shed light on their inner wor...
Lipschitzness is all you need to tame off-policy generative adversarial imitation learning [0.03%]
Lipschitz常数足以驾驭脱轨的对抗生成型模仿学习
Lionel Blondé,Pablo Strasser,Alexandros Kalousis
Lionel Blondé
Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success. We...
On the benefits of representation regularization in invariance based domain generalization [0.03%]
基于不变性的领域泛化中表示正则化的裨益分析
Changjian Shui,Boyu Wang,Christian Gagné
Changjian Shui
A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Domain generalization aims to alleviate such a prediction gap between the observed and unseen environme...
Dimosthenis Pasadakis,Christie Louis Alappat,Olaf Schenk et al.
Dimosthenis Pasadakis et al.
Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algor...
Bo Kang,Darío García García,Jefrey Lijffijt et al.
Bo Kang et al.
Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyon...
A network-based positive and unlabeled learning approach for fake news detection [0.03%]
一种基于网络的正例和无标签学习的假新闻检测方法
Mariana Caravanti de Souza,Bruno Magalhães Nogueira,Rafael Geraldeli Rossi et al.
Mariana Caravanti de Souza et al.
Fake news can rapidly spread through internet users and can deceive a large audience. Due to those characteristics, they can have a direct impact on political and economic events. Machine Learning approaches have been used to assist fake ne...