56 citations · 101 across the 12 of their papers we have counts for
21 papers · 1 filter
Distribution Guided Active Feature Acquisition
Yang Li, Junier Oliva
Human agents routinely reason on instances with incomplete and muddied data (and weigh the cost of obtaining further features). In contrast, much of ML is devoted to the unrealisti…
Towards Cost Sensitive Decision Making
Yang Li, Junier Oliva
Many real-world situations allow for the acquisition of additional relevant information when making decisions with limited or uncertain data. However, traditional RL approaches eit…
Anomaly Detection via Gumbel Noise Score Matching
Ahsan Mahmood, Junier Oliva, Martin Styner
We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e. the gradie…
Towards Robust Active Feature Acquisition
Yang Li, Siyuan Shan, Qin Liu +1
Truly intelligent systems are expected to make critical decisions with incomplete and uncertain data. Active feature acquisition (AFA), where features are sequentially acquired to…
Partially Observed Exchangeable Modeling
Yang Li, Junier B. Oliva
Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional…
Arbitrary Conditional Distributions with Energy
Ryan R. Strauss, Junier B. Oliva
Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, whic…