5 accepted papers
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (\textit{SFT}) and reinforcement learning from human feedback (\textit{RLHF}) greatl…
Signal denoising is an important problem with a vast literature. Recently, signal denoising on graphs has received a lot of attention due to the increasing use of graph-structured signals. However, well-established signal denoising methods do not generalize to graph signals with irregular structures…
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional ty…
Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image processing and data gathering to classification and recommender systems. Recently, deep neural networks have been proposed as latent factor models for matrix completion and ha…
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter explo…