2026
Conformal Prediction Meets Long-tail Classification
AAAI 2026technical
Conformal Prediction (CP) is a popular method for uncertainty quantification that converts a pretrained model
10 accepted papers
Conformal Prediction (CP) is a popular method for uncertainty quantification that converts a pretrained model
Large Language Model (LLM) agents have shown significant potential in automated optimization modeling for mathematical problems. However, real-world problems are still challenging due to their knowledge-intensive nature. Existing methods, constrained by static parametric knowledge, often lack the do…
Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that multi-expert L2D is fundamentally more challenging than the single-expert case. With multiple experts, the classifier's…
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to…
Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods, including pruning and quantization, calibration data plays a vital role by informing the weight importance and activatio…
Recent advances in large language models have led to numerous task-specialized fine-tuned variants, creating a need for efficient model merging techniques that preserve specialized capabilities while avoiding costly retraining. While existing task vector-based merging methods show promise, they typi…
Large Language Models (LLMs) have shown human-like reasoning abilities but still face challenges in solving complex logical problems. Existing unidirectional chaining methods, such as forward chaining and backward chaining, suffer from issues like low prediction accuracy and efficiency. To address t…
Learning to defer (L2D) allows the classifier to defer its prediction to an expert for safer predictions, by balancing the system’s accuracy and extra costs incurred by consulting the expert. Various loss functions have been proposed for L2D, but they were shown to cause the underfitting of trained…
In contrast to ordinary supervised classification tasks that require massive data with high-quality labels, complementary-label learning (CLL) deals with the weakly-supervised learning scenario where each instance is equipped with a complementary label, which specifies a class the instance does not…
While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of trans…