1 citations · 4 across the 14 of their papers we have counts for
11 papers · 1 filter
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient…
Entropy Reweighted Conformal Classification
Rui Luo, Nicolo Colombo
Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibrat…
Normalizing Flows for Conformal Regression
Nicolo Colombo
Conformal Prediction (CP) algorithms estimate the uncertainty of a prediction model by calibrating its outputs on labeled data. The same calibration scheme usually applies to any m…
Conformal Load Prediction with Transductive Graph Autoencoders
Rui Luo, Nicolo Colombo
Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weig…
On training locally adaptive CP
Nicolo Colombo
We address the problem of making Conformal Prediction (CP) intervals locally adaptive. Most existing methods focus on approximating the object-conditional validity of the intervals…
Differentiable Architecture Pruning for Transfer Learning
Nicolo Colombo, Yang Gao
We propose a new gradient-based approach for extracting sub-architectures from a given large model. Contrarily to existing pruning methods, which are unable to disentangle the netw…