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@ooaarg

Online Optimization And Applications Research Group

Browse our reference implementations and benchmarks for our papers on online optimization, bandits, and database systems.

OOAARG — Online Optimization And Applications Research Group

Website: ooaarg.github.io

What we work on

  • Bandits & Online Learning — sequential decision-making with regret guarantees
  • Autobidding, Ranking, Recommender Systems — auction-time bidding under budget/ROI constraints
  • Database optimization — query plan search via MCTS and learned cost models
  • Convex / non-smooth optimization — zeroth-order methods, heavy-tailed noise

Publications

Full publications

Get in touch

ooaarg.github.io/about

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  1. ucbfe ucbfe Public

    Official implementation of code for the KDD 2026 paper "UCB-Based Feature Engineering for Cold-Start in Recommenders"

    Python 4

  2. tfmctsqo tfmctsqo Public

    Official repository for "Practical Training-Free MCTS Query Optimization": reference implementation, PostgreSQL integration, experiments, baselines, and reproducibility artifacts.

    Python 2

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Showing 7 of 7 repositories

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