Machine learning

Posterior sampling in contextual bandits with a Gaussian prior can be implemented exactly or approximately using the Laplace approximation. The Gaussian prior is computationally efficient but it cannot describe complex distributions. In this work, we propose approximate posterior sampling algorithms for contextual bandits with a diffusion model prior. The key idea is to sample from a chain of approximate

In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because generating correct solutions in one attempt proves challenging for complex tasks. Prior works on self-debugging mostly focus on prompting methods by providing LLMs with few-shot examples, which work poorly on small open-sourced LLMs

Sequential recommendation systems often struggle to make predictions or take action when dealing with cold-start items that have limited amount of interactions. In this work, we propose SimRec – a new approach to mitigate the cold-start problem in sequential recommendation systems. SimRec addresses this challenge by leveraging the inherent similarity among items, incorporating item similarities into the

The increasing popularity of wireless sensing applications has led to a growing demand for large datasets of realistic wireless data. However, collecting such wireless data is often time-consuming and expensive. To address this challenge, we propose a synthetic data generation pipeline using human mesh generated from videos that can generate data at scale. The pipeline first generates a 3D mesh of the human

Fine-tuning large language models (LLMs) has achieved remarkable performance across various natural language processing tasks, yet it demands more and more memory as model sizes keep growing. To address this issue, the recently proposed Memory-efficient Zeroth-order (MeZO) methods attempt to fine-tune LLMs using only forward passes, thereby avoiding the need for a backpropagation graph. However, significant