15 citations · 18 across the 4 of their papers we have counts for
5 papers
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
Dmitry Osin, Egor Surkov, Petr Mokrov +5
Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, heal…
Risk-Averse Reinforcement Learning with Itakura-Saito Loss
Igor Udovichenko, Olivier Croissant, Anita Toleutaeva +2
Risk-averse reinforcement learning finds application in various high-stakes fields. Unlike classical reinforcement learning, which aims to maximize expected returns, risk-averse ag…
EBES: Easy Benchmarking for Event Sequences
Dmitry Osin, Igor Udovichenko, Viktor Moskvoretskii +2
Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these seque…
SeqNAS: Neural Architecture Search for Event Sequence Classification
Igor Udovichenko, Egor Shvetsov, Denis Divitsky +6
Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find w…
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences
Viktor Moskvoretskii, Dmitry Osin, Egor Shvetsov +5
This study explores the application of self-supervised learning techniques for event sequences. It is a key modality in various applications such as banking, e-commerce, and health…