4 citations · 5 across the 18 of their papers we have counts for
19 papers
Fine-Tuning Autobidders with Group Relative Policy Optimization
Anton Safin, Alexandra Khirianova, Andrey Pudovikov +2
Automated bidding (autobidding) is a core component of modern online advertising systems. Within this component, advertisers delegate sequential bid decisions to algorithms that mu…
Fast and Feasible: Permutation-based Constrained Reranking for Revenue Maximization
Svetlana Shirokovskikh, Anastasiia Soboleva, Ekaterina Solodneva +3
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 incr…
Uncertainty Quantification of Click and Conversion Estimates for the Autobidding
Ivan Zhigalskii, Andrey Pudovikov, Aleksandr Katrutsa +1
Modern e-commerce platforms employ various auction mechanisms to allocate paid slots for a given item. To scale this approach to the millions of auctions, the platforms suggest pro…
Empirical evaluation of the Frank-Wolfe methods for constructing white-box adversarial attacks
Kristina Korotkova, Aleksandr Katrutsa
The construction of adversarial attacks for neural networks appears to be a crucial challenge for their deployment in various services. To estimate the adversarial robustness of a…
Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms
Andrey Pudovikov, Alexandra Khirianova, Ekaterina Solodneva +3
Advertisement auctions play a crucial role in revenue generation for e-commerce companies. To make the bidding procedure scalable to thousands of auctions, the automatic bidding (a…
Robust autobidding for noisy conversion prediction models
Andrey Pudovikov, Alexandra Khirianova, Ekaterina Solodneva +4
Managing millions of digital auctions is an essential task for modern advertising auction systems. The main approach to managing digital auctions is an autobidding approach, which…
Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
Tatyana Matveeva, Aleksandr Katrutsa, Evgeny Frolov
Adaptive gradient methods like Adagrad and its variants are widespread in large-scale optimization. However, their use of diagonal preconditioning matrices limits the ability to ca…
Cluster Topology-Driven Placement of Experts Reduces Network Traffic in MoE Inference
Danil Sivtsov, Aleksandr Katrutsa, Ivan Oseledets
Efficient deployment of a pre-trained LLM to a cluster with multiple servers is a critical step for providing fast responses to users' queries. The recent success of Mixture-of-Exp…
NNTile: a machine learning framework capable of training extremely large GPT language models on a single node
Aleksandr Mikhalev, Aleksandr Katrutsa, Konstantin Sozykin +1
This study presents an NNTile framework for training large deep neural networks in heterogeneous clusters. The NNTile is based on a StarPU library, which implements task-based para…
RARe: Raising Ad Revenue Framework with Context-Aware Reranking
Ekaterina Solodneva, Alexandra Khirianova, Aleksandr Katrutsa +4
Modern recommender systems excel at optimizing search result relevance for e-commerce platforms. While maintaining this relevance, platforms seek opportunities to maximize revenue…
Functional multi-armed bandit and the best function identification problems
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov +1
Bandit optimization usually refers to the class of online optimization problems with limited feedback, namely, a decision maker uses only the objective value at the current point t…
Data-driven optimal prediction with control
Aleksandr Katrutsa, Ivan Oseledets, Sergey Utyuzhnikov
This study presents the extension of the data-driven optimal prediction approach to the dynamical system with control. The optimal prediction is used to analyze dynamical systems i…
EEvA: Fast Expert-Based Algorithms for Buffer Page Replacement
Alexander Demin, Yuriy Dorn, Aleksandr Katrutsa +4
Optimal page replacement is an important problem in efficient buffer management. The range of replacement strategies known in the literature varies from simple but efficient FIFO-b…
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov +1
In this study, 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.…
Memory-efficient particle filter recurrent neural network for object localization
Roman Korkin, Ivan Oseledets, Aleksandr Katrutsa
This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem. This problem is to recover the object s…
Multiparticle Kalman filter for object localization in symmetric environments
Roman Korkin, Ivan Oseledets, Aleksandr Katrutsa
This study considers the object localization problem and proposes a novel multiparticle Kalman filter to solve it in complex and symmetric environments. Two well-known classes of f…
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
Albert Sayapin, Gleb Balitskiy, Daniel Bershatsky +5
In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a class…
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Valentin Leplat, Daniil Merkulov, Aleksandr Katrutsa +3
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted…
Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction
Aleksandr Katrutsa, Sergey Utyuzhnikov, Ivan Oseledets
The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data. This is entirely a data-driven approach that extracts all necessary information fr…