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Poster
Budgeted Online Influence Maximization
Pierre Perrault · Jennifer Healey · Zheng Wen · Michal Valko

Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 07:00 PM -- 07:45 PM (PDT) @
in Poster Session 22 »

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach models better the real-world setting where the cost of influencers varies and advertizers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge-level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality-constraint setting and improves the state of the art regret bound in this case.

Keywords: Combinatorial Optimization · Networks and Relational Learning · Online Learning / Bandits · Online Learning, Active Learning, and Bandits

Author Information

Pierre Perrault (ENS Paris-Saclay, Inria)
Jennifer Healey (Adobe)

Jennifer Healey has a long history of looking into how people interact with sensors and envisioning the new experiences that this enables. She holds BS, MS and PhD degrees from MIT in EECS. During here graduate studies at the Media Lab, she pioneered the field of “Affective Computing” with Rosalind Picard and developed the first wearable computer with physiological sensors and a video camera that allowed the wearer to track their daily activities and how record how they felt while doing them. She worked at both IBM Zurich and IBM TJ Watson on AI for smart phones with a multi-modal user interface that allowed the user to switch from voice to visual (input and output) seamlessly. She has been an Instructor in Translational Medicine at Harvard Medical School and Beth Israel Deaconess Medical Center, where she worked on new algorithms to predict cardiac health from mobile sensors. She continued working in Digital Health at both HP and Intel where she helped develop the Shimmer sensing platform and the Intel Health Guide. Her research at Intel extended to sensing people in cars and cooperative autonomous driving (see her TED talk). She has also continued her work in Affective computing, developing a new software platform for cell phones which included onboard machine learning algorithms for recognizing stress from heart rate, activation from features of voice and privacy protected sentiment analysis of texts and emails (Best Demo at MobileHCI 2018).

Zheng Wen (DeepMind)
Michal Valko (DeepMind)
Michal Valko

Michal is a machine learning scientist in DeepMind Paris, tenured researcher at Inria, and the lecturer of the master course Graphs in Machine Learning at l'ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimizing the data that humans need to spend inspecting, classifying, or “tuning” the algorithms. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, or self-supervised learning. Michal is actively working on represenation learning and building worlds models. He is also working on deep (reinforcement) learning algorithm that have some theoretical underpinning. He has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. He received his Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos before taking a permanent position at Inria in 2012.

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