Valda – Page 9 – Value from data

Valda — Value from data

Valda is a joint research project-team between Inria Paris, École normale supérieure, and CNRS, within the Computer science department of École normale supérieure (DI/ENS, UMR 8548). The Valda team's focus is on both foundational and systems aspects of complex data management, especially human-centric data. Valda was created in the fall of 2016 and became an Inria project-team early 2018.

Presentation

Learn all about our research topics

Seminar

Come attend our research seminar

Members

Meet the members of our team

Publications

Consult our publications

Valda Seminar: Silviu Maniu

Camille Bourgaux 2020/01/13 2020/01/20Seminars

Silviu Maniu, Université Paris-Sud 7 February 2020, 10:30-11:30 ENS, S16 An Experimental Study of the Treewidth of Real-World Data Treewidth is a parameter that measures how tree-like a data instance is, and whether it can reasonably be decomposed into a data structure resembling a tree. Many computation tasks are known…

Valda Seminar: Juan Sequeda

Camille Bourgaux 2019/11/18 2019/11/18Seminars

Juan Sequeda 29 November 2019, 10:30-11:30 ENS, S16 A Brief History of Knowledge Graph’s Main Ideas Knowledge Graphs can be considered to be fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. The term “Knowledge Graph” has rapidly gained popularity…

Valda Seminar: Julien Grange

Camille Bourgaux 2019/11/12 2019/11/12Seminars

6 December 2019, 10:30-11:30 ENS, S16 Order-invariant first-order logic over hollow trees. Order-invariant first-order logic is the extension of first-order logic in which the usage of an additional ordering relation on the structure’s universe is allowed, provided that the evaluation of sentences is independent of the choice of a particular…

Valda Seminar: Yoan Russac

Camille Bourgaux 2019/10/09 2019/10/09Seminars

8 November 2019, 10:30-11:30 ENS, S16 Weighted Linear Bandits for Non-stationary environments. We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in…