Objective
Online decision-making, characterized by the need to make decisions without knowledge of the future, lies at the heart of numerous applications. Despite its prevalence, our grasp of effective strategies for handling the associated uncertainty remains poor. Through the lens of the established framework of online algorithms as well as the emerging field of learning-augmented algorithms, this project aims to address central challenges in decision making under uncertainty.
While there has been extensive research on online algorithms, many of the field's core challenges remain unresolved. However, several recent discoveries of new algorithmic design and analysis techniques have opened up novel avenues for overcoming previous obstacles.
Alongside these technical advancements, the rise of machine learning is now significantly enriching our toolset for dealing with uncertainty. This has motivated the recent emergence of the field of learning-augmented algorithms. Here, an algorithm's input is augmented with predictions, aiming for near-optimal performance if predictions are reasonably good, while still retaining classical worst-case guarantees even for highly erroneous predictions.
Inspired by these recent developments, this project aims to substantially elevate our understanding of decision-making under uncertainty. The main objectives are (1) to explore new directions around the concept of work functions, (2) to elevate the mirror descent technique into a generic tool for online algorithm design, (3) to develop universal techniques for designing learning-augmented algorithms, and (4) to expand the scope of learning-augmented algorithms to new domains.
The project addresses questions at the forefront of theoretical computer science, building on the PI's recent success in resolving several long-standing problems, and strives for foundational contributions to the timely issue of leveraging machine-learned predictions for improved algorithm design.
Fields of science (EuroSciVoc)
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
OX1 2JD Oxford
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.