EBES: Easy Benchmarking for Event Sequences
Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these sequences is crucial...
SeqNAS: Neural Architecture Search for Event Sequence Classification
Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find widespread use in...
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences
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 healthcare. However,...
Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
Reconstructing facial images from black-box recognition models poses a significant privacy threat. While many methods require access to embeddings, we address the more challenging scenario of...
Darker than Black-Box: Face Reconstruction from Similarity Queries
Several methods for inversion of face recognition models were recently presented, attempting to reconstruct a face from deep templates. Although some of these approaches work in a black-box setup...
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, healthcare, business...
Risk-Averse Reinforcement Learning with Itakura-Saito Loss
Risk-averse reinforcement learning finds application in various high-stakes fields. Unlike classical reinforcement learning, which aims to maximize expected returns, risk-averse agents choose...
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter task is to take the...
Estimating Barycenters of Distributions with Neural Optimal Transport
Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions. A theoretically appealing notion...