Learning with privileged and sensitive information: a gradient-boosting approach.

We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features.

Human-Guided Learning for Probabilistic Logic Models.

Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a "mere labeler" in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice.

Extracting Adverse Drug Events from Text using Human Advice.

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society in general. When methods extract ADEs from observational data, there is a necessity to evaluate these methods. More precisely, it is important to know what is already known in the literature.

Gene expression in normal human bronchial epithelial (NHBE) cells following in vitro exposure to cigarette smoke condensate.

Cigarettes that burn tobacco produce a complex mixture of chemicals, including mutagens and carcinogens. Cigarettes that primarily heat tobacco produce smoke with marked reductions in the amount of mutagens and carcinogens and demonstrate reduced mutagenicity and carcinogenicity in a battery of toxicological assays. Chemically induced oxidative stress, DNA damage, and inflammation may alter cell cycle regulation and are important biological events in the carcinogenic process.