Conformal prediction is a powerful tool for uncertainty qua ntification, but its application to time-series data is constrained by the violati on of the exchangeability assumption. Current solutions for time-series prediction typically operate in the output space and rely on manually selected weights to addres s distribution drift, leading to overly conservative predictions. To enable dyna mic weight learning in the semantically rich latent space, we introduce a novel a pproach called Con-formalized Time Series with Semantic Features (CT -SSF). CT -SSF utilizes the inductive bias in deep representation learning to dynamica lly adjust weights, prioritizing semantic features relevant to the current predic tion. Theoretically, we show that CT -SSF surpasses previous methods defined in the ou tput space. Experiments on synthetic and benchmark datasets demonstrate tha t CT -SSF significantly outperforms existing state-of-the-art (SOT A) conformal p rediction techniques in terms of prediction efficiency while maintaining a valid cov erage guarantee.

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling.

Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.

One notable advantage of using OT in the latter setting is that, compared to other generative approaches, such as GANs, Normalizing Flows, or Diffusion Models, there is no assumption for one of the measures to be defined in a closed form ( e.g., Gaussian or uniform)

As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has become essential.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency