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VK-LSVD: A Large-Scale Industrial Dataset for Short-Video Recommendation
Alexander Poplavsky, Alexander D'yakonov, Yuriy Dorn, Andrey Zimovnov
We introduce the VK Large Short-Video Dataset (VK-LSVD), the largest publicly available industrial dataset of its kind.
Practical MCTS-based Query Optimization: A Reproducibility Study and new MCTS algorithm for complex queries
Vladimir Burlakov, Alena Rybakina, Sergey Kudashev, Konstantin Gilev, Alexander Demin, Denis Ponomaryov, Yuriy Dorn
This paper presents a comprehensive reproducibility study of these methods, revealing that they often fail to support the claimed performance gains when subjected to diverse workloads.
Uncertainty Quantification of Click and Conversion Estimates for the Autobidding
Ivan Zhigalskii, Andrey Pudovikov, Aleksandr Katrutsa, Egor Samosvat
We propose the DenoiseBid method, which corrects the generated CTRs and CVRs to make the resulting bids more efficient in auctions.
Survey of Modern Smooth Optimization Algorithms with Comparison Oracle
Aleksandr Lobanov, Alexander Gasnikov
This review provides an overview of contemporary algorithms for smooth, multivariate optimization that utilize only information about the order of the function values, rather than their numerical magnitudes.
Zeroth-order methods for non-smooth stochastic problems under heavy-tailed noise
Nail Bashirov, Alexander Gasnikov, Aleksandr Lobanov
We propose gradient-free algorithms with zeroth-order oracle under adversarial noise with unbounded variance, for non-smooth convex and convex-concave optimization problems.
IDENTIFICATION OF THE BRAESS PARADOX IN A STABLE DYNAMIC MODEL IN NETWORK WITH ONE SOURCE AND MULTIPLE SINKS
Oleg Shitikov, Yuriy Dorn
We study the problem of identifying edges in a transportation graph where the introduction of an additional toll would enhance the efficiency of network usage within the Nesterov–de Palma equilibrium model.
Hint Based Query Optimization with LLM Agent and Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Burlakov
A two–path architecture for query–optimizer hint selection that combines fast nearest–neighbor transfer in an LLM–derived plan–embedding space with a budgeted LLM agent that searches the hint space under DBMS feedback.
UCB-type Algorithm for Budget-Constrained Expert Learning
Ilgam Latypov, Alexandra Suvorikova, Alexey Kroshnin, Alexander Gasnikov, Yuriy Dorn
We introduce M-LCB, a computationally efficient UCB-style meta-algorithm that provides anytime regret guarantees.
Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms
Andrey Pudovikov, Aleksandra Khirianova, Ekaterina Solodneva, Aleksandr Katrutsa, Egor Samosvat, Yuriy Dorn
We consider the most efficient autobidding algorithms from different classes, e.g., ones based on the controllers, RL, optimal formulas, etc., and benchmark them in the bidding environment.
RARe: Raising Ad Revenue Framework with Context-Aware Reranking
Ekaterina Solodneva, Aleksandra Khirianova, Aleksandr Katrutsa, Roman Loginov, Andrey Tikhanov, Egor Samosvat, Yuriy Dorn
We propose and compare two different click models that take into account the context of items in a search result.
Robust autobidding for noisy conversion prediction models
Andrey Pudovikov, Aleksandra Khirianova, Ekaterina Solodneva, Gleb Molodtsov, Aleksandr Katrutsa, Yuriy Dorn, Egor Samosvat
We propose RobustBid, an efficient method for robust autobidding taking into account uncertainty in CTR and CVR predictions.
Training-Free Query Optimization via LLM-Based Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Burlakov
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.
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov, Andrey Pudovikov
We propose a new method for constructing UCB-type algorithms for stochastic multi-armed bandits based on general convex optimization methods with an inexact oracle.
Bat: Benchmark for auto-bidding task
Aleksandra Khirianova, Ekaterina Solodneva, Andrey Pudovikov, Sergey Osokin, Egor Samosvat, Yuriy Dorn, Alexander Ledovsky, Yana Zenkova
We propose RobustBid, an efficient method for robust autobidding taking into account uncertainty in CTR and CVR predictions.
Functional multi-armed bandit and the best function identification problems
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov, Anastasiia Soboleva
We propose two new classes of problems: the functional multi-armed bandit problem (FMAB) and the best function identification problem.
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics
Anastasiia Soboleva, Andrey Pudovikov, Roman Snetkov, Alina Babenko, Egor Samosvat, Yuriy Dorn
We developed a UCB-like algorithm under multi-armed bandit (MAB) setting for positional-based model (PBM), specifically tailored to auction pay-per-click systems.
Optimal Traffic Allocation for Multi-Slot Sponsored Search: Balance of Efficiency and Fairness
Anastasiia Soboleva, Alexander Ledovsky, Yuriy Dorn, Egor Samosvat, Andrey Tikhanov, Fyodor Prazdnikov
We propose a novel ad allocation model that departs from traditional auction mechanics.
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
Aleksandr Lobanov, Alexander Gasnikov
This paper is devoted to the study of stochastic optimization problems under the generalized smoothness assumption.
γ-Competitiveness: An Approach to Multi-Objective Optimization with High Computation Costs in Lipschitz Functions
We introduce an extension of the concept of competitive solutions and propose the Scalarization With Competitiveness Method (SWCM) for multi-criteria problems.
On quasi-convex smooth optimization problems by a comparison oracle
Alexander Gasnikov, Mohammad Alkousa, Aleksandr Lobanov, Yuriy Dorn, Fedor Stonyakin, Ilya Kuruzov, Sanjeev Singh
This paper is devoted to an approach to minimizing quasi-convex functions using a recently proposed comparison oracle only.
Learning-Augmented Online Caching: New Upper Bounds
Daniel Skachkov, Denis Ponomaryov, Yuriy Dorn, Alexander Demin
We address the problem of learning-augmented online caching for DBMS in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page.
EEvA: Fast Expert-Based Algorithms for Buffer Page Replacement
Alexander Demin, Yuriy Dorn, Aleksandr Katrutsa, Daniil Kazantsev, Ilgam Latypov, Yulia Maximlyuk, Denis Ponomaryov
In this paper, we propose a new family of page replacement algorithms for DB buffer manager which demonstrate a superior performance wrt competitors on custom data access patterns and imply a low computational overhead on TPC-C.
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits
Yuriy Dorn, Nikita Kornilov, Nikolay Kutuzov, Alexander Nazin, Eduard Gorbunov, Alexander Gasnikov
In this paper, we propose a new version of INF called the Implicitly Normalized Forecaster with clipping (INF-clip) for MAB problems with heavy-tailed reward distributions.