Customer-obsessed science

User modeling is of great importance in personalization services. Many existing methods treat users as interaction sequences to capture users’ evolving interests. Another line of research models each user as a user graph in which the users’ interactions are modeled as nodes. Nodes (interactions) in user graphs are connected via edges that reflect certain relations such as item similarity. The graph-based

In this work, we investigate the possibility of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model that learns the underlying task distribution and combine Thompson sampling

Large transformer-based models have demonstrated state of the art results on several Natural Language Understanding (NLU) tasks. However, their deployment comes at the cost of increased footprint and inference latency, limiting their adoption to real-time applications, especially on resource constrained devices. In order to optimize the trade-off between model accuracy, footprint and inference latency,

Quantum programs are written in high-level languages, whereas quantum hardware can only execute low-level native gates. To run programs on quantum systems, each highlevel instruction must be decomposed into native gates. This process is called gate nativization and is performed by the compiler. Recent quantum computers support a richer native gate set to reduce crosstalk by tackling frequency crowding and

Privacy-preserving federated learning (PPFL) is a paradigm of distributed privacy-preserving machine learning training in which a set of clients, each holding siloed training data, jointly compute a shared global model under the orchestration of an aggregation server. The system has the property that no party learns any information about any client’s training data, besides what could be inferred from the