Sampling Strategies for Data-Driven Inference of Input–Output System Properties

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights ...

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Inference for the Visibility Distribution for Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is used throughout the world to estimate prevalences and population sizes for hard-to-reach populations. Although RDS is an effective method for enrolling people from key populations (KPs) in studies, it relies on an unknown sampling mechanism and thus each individual’s inclusion probability is unknown. Current estimators rely on a participant’s network size (de...

Data-Driven Strategies for an Automated Dialogue System

We present a prototype natural-language problem-solving application for a financial services call center, developed as part of the Amitiés multilingual human-computer dialogue project. Our automated dialogue system, based on empirical evidence from real call-center conversations, features a datadriven approach that allows for mixed system/customer initiative and spontaneous conversation. Prelim...

User-driven sampling strategies in image exploitation

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the hu...