Good Papers

Showing papers from Criteo AI Lab Show all papers

57%Worth a look
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Improved Regret bounds in Tabular Reinforcement Learning under Local Differential Privacy

Hugo Richard

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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1/20 AI panelreviewers recommend it

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
71%Highly rated
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Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback

A bidder with dynamic auction values and only aggregated feedback learns near-optimal bidding policies via plug-in estimators with logarithmic or sublinear regret.

Benjamin Heymann, Otmane Sakhi

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 2/5
medium 2/10
strict 2/5
45%Niche pick
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CUVET: A Partitioning Approach for Continuous Treatment Assignment At Scale

CUVET partitions continuous treatment assignment at scale via a scalable partitioning approach that improves estimation and assignment efficiency.

Artem Betlei, Mariia Vladimirova, Victor Girou, Thibaud Rahier

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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0/20 AI panelreviewers recommend it

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
74%Highly rated
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Scalable Fair Learning via Cramér-von Mises Regularization

A Cramér-von Mises fairness regularizer with O(B log B) complexity penalizes prediction-sensitive attribute dependence during training, achieving competitive fairness-utility trade-offs with lower overhead.

Albert Gimó Contreras, Mariia Vladimirova, Olga Petrova, Reda CHHAIBI and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5