Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

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 …

Data valuation has wide use cases in machine learning, including improving data quality
and creating economic incentives for data sharing. This paper studies the robustness of data …

The rapidly growing field of computational social choice, at the intersection of computer
science and economics, deals with the computational aspects of collective decision making …

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

In this work, we aim to design a data marketplace; a robust real-time matching mechanism to
efficiently buy and sell training data for Machine Learning tasks. While the monetization of …

Data valuation is a powerful framework for providing statistical insights into which data are
beneficial or detrimental to model training. Many Shapley-based data valuation methods …

We study corporate control tracing controlling shareholders for thousands of listed firms from
127 countries over 2004 to 2012. Government and family control is pervasive in civil‐law …

The paradigm of Federated learning (FL) deals with multiple clients participating in
collaborative training of a machine learning model under the orchestration of a central …

The Shapley value---probably the most important normative payoff division scheme in
coalitional games---has recently been advocated as a useful measure of centrality in …