Constrained decision-making problems are pervasive across various disciplines.

Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.

SOI by directly leveraging human feedback without being restricted by a predefined reward model nor cumbersome model selection.

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the "last mile" to enhance the efficiency of object detection.

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.

Recently, contrastive multi-view clustering (MvC) has emerged as a promising avenue for analyzing data from heterogeneous sources, typically leveraging the off-the-shelf instances as positives and randomly sampled ones as negatives. In practice, however, this paradigm would unavoidably suffer from the Dual Noisy Correspondence (DNC) problem, where noise compromises the constructions of both positive and negative pairs.