7 papers · 1 filter
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Wei Cui, Tongzi Wu, Jesse C. Cresswell +2
Meta-learning represents a strong class of approaches for solving few-shot learning tasks. Nonetheless, recent research suggests that simply pre-training a generic encoder can pote…
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi +8
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open pr…
TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models
Tianyin Liao, Chunyu Hu, Yicheng Sui +4
Link prediction is a fundamental task in graph machine learning with widespread applications such as recommendation systems, drug discovery, knowledge graphs, etc. In the foundatio…
Self-Supervised Representation Learning as Mutual Information Maximization
Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu +2
Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works att…
Conformal Prediction Sets Can Cause Disparate Impact
Jesse C. Cresswell, Bhargava Kumar, Yi Sui +1
Conformal prediction is a statistically rigorous method for quantifying uncertainty in models by having them output sets of predictions, with larger sets indicating more uncertaint…
Conformal Prediction Sets Improve Human Decision Making
Jesse C. Cresswell, Yi Sui, Bhargava Kumar +1
In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure. Machine learning models that output calibrated prediction…