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Online Optimization and Applications Research Group
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Uncertainty Quantification of Click and Conversion Estimates for the Autobidding

UAI 2026

We propose the DenoiseBid method, which corrects the generated CTRs and CVRs to make the resulting bids more efficient in auctions.

UCB-Based Feature Engineering for Cold-Start in Recommenders

KDD 2026

CTR models rank new items poorly until they gather enough clicks. UCB-FE wraps any CTR model with position-aware upper confidence bounds at inference time, boosting cold items — no retraining, no architecture changes — and comes with a new coldNDCG metric to measure it.

Fast and Feasible: Permutation-based Constrained Reranking for Revenue Maximization

SIGIR 2026

Search and recommender systems have produced highly relevant search results. A natural next step in the development of such systems in e-commerce is to rerank these results to increase the platform's revenue from paid promotion products.

RRE-GTC: A Geo-Temporal Cluster Dataset for Real-Estate PriceEstimation and Market-Aware Search

SIGIR 2026

We introduce RRE-GTC (Ru-Real-Estate Geo-Temporal Clusters), an open resource designed to benchmark ranking, pricing, and recommendation algorithms in dynamic spatial contexts. Derived from a massive collection of apartment listings in major Russian cities, RRE-GTC aggregates 456,182 geo-temporal clusters, offering a privacy-preserving alternative to releasing raw listing-level data.

Training-Free Query Optimization via LLM-Based Plan Similarity

The Bulletin of Irkutsk State University. Series Mathematics

We introduce LLM-based Plan Mapping, a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting.

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What we work on

  • Bandits and Online Learning

    Sequential decisions under partial information, with provable regret guarantees.

    Publications: 5
  • Autobidding, Ranking and Recommender Systems

    Learning to bid, pace budgets, and rank recommendations under budget and ROI constraints.

    Publications: 9
  • DBMS Optimization

    Online learning for query optimization and adaptive buffer management in databases.

    Publications: 5
  • Optimization

    Convex and non-convex optimization, lower bounds, and parameter-free methods.

    Publications: 7

Out of the lab

  • paper17 Aug 2026UAI 2026

    Uncertainty Quantification of Click and Conversion Estimates for the Autobidding

    Ivan Zhigalskii, Andrey Pudovikov, Aleksandr Katrutsa, Egor Samosvat

    Paper
  • paper8 Aug 2026KDD 2026

    UCB-Based Feature Engineering for Cold-Start in Recommenders

    Anastasiia Soboleva, Andrey Pudovikov, Aleksandr Katrutsa, Roman Snetkov, Alina Babenko, Egor Samosvat, Yuriy Dorn

    Paper
  • paper16 Jul 2026SIGIR 2026

    Fast and Feasible: Permutation-based Constrained Reranking for Revenue Maximization

    Svetlana Shirokovskikh, Anastasiia Soboleva, Ekaterina Solodneva, Aleksandr Katrutsa, Roman Loginov, Egor Samosvat

    Paper
  • paper16 Jul 2026SIGIR 2026

    RRE-GTC: A Geo-Temporal Cluster Dataset for Real-Estate PriceEstimation and Market-Aware Search

    Irina Govorova, Aleksandr Alekseitsev, Irina Podlipnova, Meruza Kubentayeva, Yuriy Dorn

    Paper
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