Individual-level data (microdata) that characterizes a population, is
es...
Data owners face increasing liability for how the use of their data coul...
To avoid discriminatory uses of their data, organizations can learn to m...
The success of sites such as ACLED and Our World in Data have demonstrat...
We consider social resource allocations that deliver an array of scarce
...
Data-driven societal event forecasting methods exploit relevant historic...
Federated learning (FL) involves multiple distributed devices jointly
tr...
Transliteration is very common on social media, but transliterated text ...
Organizations that collect and sell data face increasing scrutiny for th...
Predicting user intent and detecting the corresponding slots from text a...
Data mining algorithms are increasingly used in automated decision makin...
Federated learning (FL) involves training a model over massive distribut...
Organizations that own data face increasing legal liability for its
disc...
We build a common-knowledge concept recognition system for a Systems
Eng...
American Sign Language recognition is a difficult gesture recognition
pr...
During the onset of a disaster event, filtering relevant information fro...
Domain adaptation approaches seek to learn from a source domain and
gene...
Student's academic performance prediction empowers educational technolog...
Forecasting influenza-like illness (ILI) is of prime importance to
epide...
Effective representation learning from text has been an active area of
r...
Federated learning (FL) is a machine learning paradigm where a shared ce...
Voice-controlled personal and home assistants (such as the Amazon Echo a...
Sensors are an integral part of modern Internet of Things (IoT) applicat...
Machine learning algorithms are increasingly involved in sensitive
decis...
Currently, college-going students are taking longer to graduate than the...
The past decade has seen a growth in the development and deployment of
e...
Multi-task learning (MTL) is a supervised learning paradigm in which the...
Large-scale Hierarchical Classification (HC) involves datasets consistin...
Large-scale classification of data where classes are structurally organi...
Hierarchical Classification (HC) is a supervised learning problem where
...