Learning from Pariwise Data:
Context: The Learning from Pairwise Data project focuses on learning problems in which a set
of values have to be learned based on noisy information about pairs of these values (e.g. their ratio, product),
a framework that applies to large classes of problems including for example
- Recovering weights from comparison results in a Bradley-Terry-Luce model, with applications
as diverse as modelling customer preferences, online advertisement, ranking quality of sport teams or evaluating patient reaction to medications
- Determining experts' abilities without knowing the ground truth of their actions e.g. in a Dawid-Skene based model
- General rank-1 matrix completion with perturbed data.
Objective and approach: We aim at developing generic ultra-rapid near-linear algorithms with minimax optimality guarantees
for these learning problems. Our algorithms rely on formulating these problems as weighted least-square systems that are linear
in the estimates but highly non-linear in the data, and exploiting the graph structure of the data to solve these systems in near linear time.
The data can indeed be represented by a weighted graph in which two variables are connected when information on that pair is available,
and this graph plays a major role both in the analaysis of the algorithm and in the development of minimax lower bounds.
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Open Postdoc Positions:
I am looking for two postdocs with a strong mathematical background. Ideal candidates have an expertise in at least one of the following fields:
Graphs and Networks, Probabilities, Machine Learning, Online Learning, Optimization, Information Theory. The positions are available immediately.
How to apply: Sending an email to pascale.premereur@uclouvain.be mentioning the project name and including (at least) a CV, publiation list, and the name of two reference persons.
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News:
Our paper on Minimax Rank-1 matrix factorization will be presented at AISTAT 2020 .
Our paper on Minimax Rate for Learning From Pairwise
Comparisons has been accepted at ICML 2020 .
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Project Publications and Preprints:
- Julien M. Hendrickx, Alex Olshevsky and Venkatesh Saligrama,
Minimax Rate for Learning From Pairwise
Comparisons, ICML 2020. [PDF]
- Julien M. Hendrickx, Alex Olshevsky and Venkatesh Saligrama,
Minimax Rank-1 Factorization, AISTATS 2020. [PDF]
- Julien M. Hendrickx, Alex Olshevsky and Venkatesh Saligrama,
Graph Resistance and Learning from Pairwise Comparisons, ICML 2019. [PDF]
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Responsible :
Julien Hendrickx
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