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87 papers · 1 filter
Near-optimal Linear Predictive Clustering in Non-separable Spaces via MIP and QPBO Reductions
Jiazhou Liang, Hassan Khurram, Scott Sanner
Linear Predictive Clustering (LPC) partitions samples based on shared linear relationships between feature and target variables, with numerous applications including marketing, med…
LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
Zhanda Zhu, Qidong Su, Yaoyao Ding +3
Low-Rank Adaptation (LoRA) has become the leading Parameter-Efficient Fine-Tuning (PEFT) method for Large Language Models (LLMs), as it significantly reduces GPU memory usage while…
GNN's Uncertainty Quantification using Self-Distillation
Hirad Daneshvar, Reza Samavi
Graph Neural Networks (GNNs) have shown remarkable performance in the healthcare domain. However, what remained challenging is quantifying the predictive uncertainty of GNNs, which…
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Rushabh Solanki, Meghana Bhange, Ulrich Aïvodji +1
The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic harms and re-entrenching existin…
Uncertainty Quantification From Scaling Laws in Deep Neural Networks
Ibrahim Elsharkawy, Yonatan Kahn, Benjamin Hooberman
Quantifying the uncertainty from machine learning analyses is critical to their use in the physical sciences. In this work we focus on uncertainty inherited from the initialization…
Controlled privacy leakage propagation throughout overlapping grouped learning
Shahrzad Kiani, Franziska Boenisch, Stark C. Draper
Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their d…