Seminars 2017-2018 (preliminary titles) :

October 12 (Amphi 018, RDC bâtiment UFR IM2AG)*: audio-visual analysis for human-robit interaction by Radu Horaud (INRIA Grenoble)

Robots have gradually moved from factory floors to populated spaces. Therefore, there is a crucial need to endow robots with communicative skills. One of the prerequisites of human-robot communication (or more generally, interaction) is the ability of robots to perceive their environment, to detect people, to track them over time, and to identify communicative cues, such as “who looks at whom” and “who speaks to whom”. Therefore we are interested in analysing situations in which several people are present, understand their activities, estimate who speaks and who doesn’t, etc. For that purpose we combine computer vision, audio signal processing and machine learning methods. We will briefly present the research that we carried out within this topic, and stress the importance of learning from sensory data.

Related articles
- Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion
- Tracking a Varying Number of People with a Visually-Controlled Robotic Head

 

October 26 (Auditorium Bâtiment Imag)*: a holistic view of human factors in crowdsourcing by Sihem Amer-Yahia (CNRS, LIG, Grenoble)

For over 40 years, organization studies have examined human factors in physical workplaces and their influence on the ability of an individual to perform a task, or a set of tasks, alone or in collaboration with others. In a virtual marketplace, the crowd is typically volatile, its arrival and departure asynchronous, and its levels of attention and accuracy diverse. This has generated a wealth of new research ranging from studying workers’ fatigue in task completion to examining the role of motivation in task assignment. I will review such work and argue that we need a holistic view to take full advantage of human factors such as skills, expected wage and motivation, in improving the performance of a crowdsourcing platform.

November 16: attribution modeling increases efficiency of bidding in display advertising by Eustache Diemert (Criteo Research, Paris)

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising. Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.

Date to be confirmed
By Merouane Debbah (Ecole Centrale Supélec, Univerty Paris-Saclay, Vice President  R&D Center Huawei France, Director of the Mathematical and Algorithmic Sciences Lab)

Date to be confirmed
by Romain Couillet (Ecole Centrale Supélec, Univerty Paris-Saclay)