Search Results for author: Sergey Kolesnikov

Found 21 papers, 14 papers with code

RecBaselines2023: a new dataset for choosing baselines for recommender models

no code implementations25 Jun 2023 Veronika Ivanova, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov

To solve this problem, we have collected and published a dataset containing information about the recommender models used in 903 papers, both as baselines and as proposed approaches.

Collaborative Filtering Descriptive

Katakomba: Tools and Benchmarks for Data-Driven NetHack

1 code implementation14 Jun 2023 Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, Sergey Kolesnikov

NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions.

D4RL NetHack +2

Revisiting the Minimalist Approach to Offline Reinforcement Learning

3 code implementations16 May 2023 Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, Sergey Kolesnikov

In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such design elements built on top of the TD3+BC method.

D4RL Offline RL +2

Anti-Exploration by Random Network Distillation

2 code implementations31 Jan 2023 Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov

Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.


Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

2 code implementations20 Nov 2022 Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, Sergey Kolesnikov

This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.

Offline RL reinforcement-learning +1

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

2 code implementations20 Nov 2022 Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov, Sergey Kolesnikov

Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.

Offline RL

CORL: Research-oriented Deep Offline Reinforcement Learning Library

2 code implementations13 Oct 2022 Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, Sergey Kolesnikov

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.

Benchmarking D4RL +2

EXACT: How to Train Your Accuracy

1 code implementation19 May 2022 Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov

Classification tasks are usually evaluated in terms of accuracy.

Ranked #2 on Image Classification on SVHN (Percentage correct metric)

General Classification Image Classification

CVTT: Cross-Validation Through Time

no code implementations11 May 2022 Mikhail Andronov, Sergey Kolesnikov

The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community.

Recommendation Systems

Probabilistic Embeddings Revisited

1 code implementation14 Feb 2022 Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov

We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research.

Face Verification Image Retrieval +2

Next Period Recommendation Reality Check

no code implementations11 Oct 2021 Sergey Kolesnikov, Oleg Lashinin, Michail Pechatov, Alexander Kosov

In this article, we aim to fill the gap in RecSys methods evaluation on the NPR task using publicly available datasets and (1) introduce the TTRS, a large-scale financial transactions dataset suitable for RecSys methods evaluation; (2) benchmark popular RecSys approaches on several datasets for the NPR task.

Recommendation Systems

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

no code implementations8 Oct 2021 Vladislav Kurenkov, Sergey Kolesnikov

In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms.

Decision Making energy management +4

LRWR: Large-Scale Benchmark for Lip Reading in Russian language

no code implementations14 Sep 2021 Evgeniy Egorov, Vasily Kostyumov, Mikhail Konyk, Sergey Kolesnikov

Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas.

Lipreading Lip Reading +2

Sample Efficient Ensemble Learning with Catalyst.RL

2 code implementations29 Mar 2020 Sergey Kolesnikov, Valentin Khrulkov

We present Catalyst. RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research.

Ensemble Learning reinforcement-learning +1

Catalyst.RL: A Distributed Framework for Reproducible RL Research

1 code implementation28 Feb 2019 Sergey Kolesnikov, Oleksii Hrinchuk

Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms.

Continuous Control

Run, skeleton, run: skeletal model in a physics-based simulation

1 code implementation18 Nov 2017 Mikhail Pavlov, Sergey Kolesnikov, Sergey M. Plis

In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible.

Navigate Policy Gradient Methods +1

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