Archive for Ocean

a spam in its own class

Posted in Mountains, pictures, Running, University life with tags , , , , , , , , on December 22, 2022 by xi'an

Dear Dr. Robert,

We would like to congratulate you on the selection of your topic for the ERC Synergy Grant 2022. We also find your approach “ – ” fundamentally exciting and wonder if you plan to use ultrafast wide tunable lasers as well.

Our multiple award-winning laser system is precisely designed for science and has already enabled many papers. Our USP, besides the flexibility and the spectrum capabilities, is the high stability in terms of power, reproducibility and reliability. You can find a first overview here.

If this is interesting for you, we would like to hear about the possible application in a short video call to evaluate fairly and transparently whether our system can fit. Otherwise, if this is not of interest to you, we will not press you further on this.

We are looking forward to your feedback. Either way, we wish you and your Université Paris Dauphine – PSL much success.

Kind regards,

Ben

Ocean’s four!

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , on October 25, 2022 by xi'an

Fantastic news! The ERC-Synergy¹ proposal we submitted last year with Michael Jordan, Éric Moulines, and Gareth Roberts has been selected by the ERC (which explains for the trips to Brussels last month). Its acronym is OCEAN [hence the whale pictured by a murmuration of starlings!], which stands for On intelligenCE And Networks​: Mathematical and Algorithmic Foundations for Multi-Agent Decision-Making​. Here is the abstract, which will presumably turn public today along with the official announcement from the ERC:

Until recently, most of the major advances in machine learning and decision making have focused on a centralized paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases. In particular, centralized learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power. It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralized networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.

OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent’s level. OCEAN will study the interaction of learning with market constraints (scarcity, fairness), connecting adaptive microeconomics and market-aware machine learning.

OCEAN builds on a decade of joint advances in stochastic optimization, probabilistic machine learning, statistical inference, Bayesian assessment of uncertainty, computation, game theory, and information science, with PIs having complementary and internationally recognized skills in these domains. OCEAN will shed a new light on the value and handling data in a competitive, potentially antagonistic, multi-agent environment, and develop new theories and methods to address these pressing challenges. OCEAN requires a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavors that will transform statistical learning in the long term while opening up exciting and novel areas of research.

Since the ERC support in this grant mostly goes to PhD and postdoctoral positions, watch out for calls in the coming months or contact us at any time.

Continue reading

%d bloggers like this: