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SIGIR 20232023Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multilabel Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces
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CLeaR 20232023This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational nonlinear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇ log p(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing
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CLeaR 20232023Learning generative object models from unlabelled videos is a long standing problem relevant for causal scene modeling. We decompose this task into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained
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2023We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work of (Sellke & Slivkins, 2022) has shown that for the special case of independent arms, after collect-ing enough initial samples, the popular Thomp-son sampling algorithm becomes incentive com-patible. This was generalized to the combinato-rial
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ICME 20232023Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly expanded the ability to train machine learning models by utilizing the computational and private data resources of numerous distributed devices. However, existing FL methods typically require that devices possess accurate ground-truth
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