10 citations · 17 across the 18 of their papers we have counts for
18 papers
Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction
Ann-Kristin Malz, Gregory Ashton, Nicolo Colombo
In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. How…
Combining gravitational wave search pipelines to find subthreshold signals in GWTC-5.0
Ann-Kristin Malz, Samuel Russell, Gregory Ashton +1
The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. Howev…
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
In-context learning lets a sequence model adapt to a new task from examples in its input. A prominent line of work shows how self-attention can be constructed to implement 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…
Structured Learning of Compositional Sequential Interventions
Jialin Yu, Andreas Koukorinis, Nicolò Colombo +2
We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as "clo…
Calibrating gravitational-wave search algorithms with conformal prediction
Gregory Ashton, Nicolo Colombo, Ian Harry +1
In astronomy, we frequently face the decision problem: does this data contain a signal? Typically, a statistical approach is used, which requires a threshold. The choice of thresho…
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…
Adapting by Pruning: A Case Study on BERT
Yang Gao, Nicolo Colombo, Wei Wang
Adapting pre-trained neural models to downstream tasks has become the standard practice for obtaining high-quality models. In this work, we propose a novel model adaptation paradig…
Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
Nicolo Colombo, Yang Gao
The history of deep learning has shown that human-designed problem-specific networks can greatly improve the classification performance of general neural models. In most practical…
Training conformal predictors
Nicolo Colombo, Vladimir Vovk
Efficiency criteria for conformal prediction, such as \emph{observed fuzziness} (i.e., the sum of p-values associated with false labels), are commonly used to \emph{evaluate} the p…
Multiple Metric Learning for Structured Data
Nicolo Colombo
We address the problem of merging graph and feature-space information while learning a metric from structured data. Existing algorithms tackle the problem in an asymmetric way, by…
Counterfactual Distribution Regression for Structured Inference
Nicolo Colombo, Ricardo Silva, Soong M Kang +1
We consider problems in which a system receives external \emph{perturbations} from time to time. For instance, the system can be a train network in which particular lines are repea…
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle
Johan Markdahl, Nicolo Colombo, Johan Thunberg +1
Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, e…
Approximate Joint Matrix Triangularization
Nicolo Colombo, Nikos Vlassis
We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the es…
Spectral Sequence Motif Discovery
Nicolò Colombo, Nikos Vlassis
Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, motif finding algorithms of incre…