9 citations · 48 across the 21 of their papers we have counts for
6 papers · 1 filter
Time-Aware Item Weighting for the Next Basket Recommendations
Aleksey Romanov, Oleg Lashinin, Marina Ananyeva +1
In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information a…
RecBaselines2023: a new dataset for choosing baselines for recommender models
Veronika Ivanova, Oleg Lashinin, Marina Ananyeva +1
The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number…
Katakomba: Tools and Benchmarks for Data-Driven NetHack
Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov +1
NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions. One of the promising directions for…
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Stanislav Dereka, Ivan Karpukhin, Maksim Zhdanov +1
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homog…
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin +1
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.…
Anti-Exploration by Random Network Distillation
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov +1
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-…