Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …

Abstract Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …

Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

As data becomes the fuel driving technological and economic growth, a fundamental
challenge is how to quantify the value of data in algorithmic predictions and decisions. For …

Abstract {\em “How much is my data worth?”} is an increasingly common question posed by
organizations and individuals alike. An answer to this question could allow, for instance …

Federated Learning (FL) bridges the gap between collaborative machine learning and
preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is …

Algorithmic systems that employ machine learning play an increasing role in making
substantive decisions in modern society, ranging from online personalization to insurance …

Data Shapley has recently been proposed as a principled framework to quantify the
contribution of individual datum in machine learning. It can effectively identify helpful or …

The Shapley value concept from cooperative game theory has become a popular technique
for interpreting ML models, but efficiently estimating these values remains challenging …

Given a data set $\mathcal {D} $ containing millions of data points and a data consumer who
is willing to pay for\$$ X $ to train a machine learning (ML) model over $\mathcal {D} $, how …