Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …

Session-based recommendation systems (SBRs) aim to capture user preferences over time
by taking into account the sequential order of interactions within sessions. One promising …

We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal
transport formulation and learns stochastic transport plans. We show that NOT with the weak …

Recent online knowledge distillation (OKD) methods focus on capturing rich and useful
intermediate information by performing multi-layer feature learning. Existing works only …

Causal inference is capable of estimating the treatment effect (ie, the causal effect of
treatment on the outcome) to benefit the decision making in various domains. One …

Although traditional recommendation methods trained on observational interaction
information have engendered a significant impact in real-world applications, it is challenging …

Generative adversarial network (GAN) has achieved remarkable success in generating high-
quality synthetic data by learning the underlying distributions of target data. Recent efforts …

Causal inference methods are widely applied in various decision-making domains such as
precision medicine, optimal policy and economics. The main focus of causal inference is the …

Learning generative models is challenging for a network edge node with limited data and
computing power. Since tasks in similar environments share a model similarity, it is plausible …

The past few years have borne witness to a marked surge in the adoption of machine
learning (ML) techniques across a broad spectrum of fields, such as image analysis, text …