Spectral Sequence Motif Discovery. - Nicolò Colombo; Nikos Vlassis (CoRR, 2014)

Stable Spectral Learning Based on Schur Decomposition. - Nicolò Colombo; Nikos Vlassis (UAI, 2015)

FastMotif: spectral sequence motif discovery. - Nicolò Colombo; Nikos Vlassis (Bioinform., 2015)

Tensor Decomposition via Joint Matrix Schur Decomposition. - Nicolò Colombo; Nikos Vlassis (ICML, 2016)

A posteriori error bounds for joint matrix decomposition problems. - Nicolò Colombo; Nikos Vlassis (NIPS, 2016)

Approximate Joint Matrix Triangularization. - Nicolò Colombo; Nikos Vlassis (CoRR, 2016)

Experimental design trade-offs for gene regulatory network inference: An in silico study of the yeast Saccharomyces cerevisiae cell cycle. - Johan Markdahl; Nicolò Colombo; Johan Thunberg; Jorge M. Gonçalves (CDC, 2017)

Tomography of the London Underground: a Scalable Model for Origin-Destination Data. - Nicolò Colombo; Ricardo Silva 001; Soong Moon Kang (NIPS, 2017)

Bayesian Semi-supervised Learning with Graph Gaussian Processes. - Yin Cheng Ng; Nicolò Colombo; Ricardo Silva 001 (NeurIPS, 2018)

Counterfactual Distribution Regression for Structured Inference. - Nicolò Colombo; Ricardo Silva 001; Soong Moon Kang; Arthur Gretton (CoRR, 2019)

Training conformal predictors. - Nicolò Colombo; Vladimir Vovk (COPA, 2020)

Multiple Metric Learning for Structured Data. - Nicolò Colombo (CoRR, 2020)

Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification. - Nicolò Colombo; Yang Gao (CoRR, 2020)

Adapting by Pruning: A Case Study on BERT. - Yang Gao; Nicolò Colombo; Wei Wang (CoRR, 2021)

Differentiable Architecture Pruning for Transfer Learning. - Nicolò Colombo; Yang Gao (CoRR, 2021)

On training locally adaptive CP. - Nicolò Colombo (COPA, 2023)

Entropy Reweighted Conformal Classification. - Rui Luo 002; Nicolò Colombo (COPA, 2024)

The Lottery Ticket Hypothesis and Reinitialisation with Masks. - Hyosun Choi; Christopher Watkins; Li Zhang; Nicolò Colombo (ICMLC, 2024)

Structured Learning of Compositional Sequential Interventions. - Jialin Yu; Andreas Koukorinis; Nicolò Colombo; Yuchen Zhu; Ricardo Silva 001 (NeurIPS, 2024)

Normalizing Flows for Conformal Regression. - Nicolò Colombo (UAI, 2024)

State-space models can learn in-context by gradient descent. - Neeraj Mohan Sushma; Yudou Tian; Harshvardhan Mestha; Nicolò Colombo; David Kappel; Anand Subramoney (CoRR, 2024)

A Review and Comparative Analysis of Univariate Conformal Regression Methods. - Jie Bao; Nicolò Colombo; Valery Manokhin; Suqun Cao; Rui Luo (COPA, 2025)

Residual Reweighted Conformal Prediction for Graph Neural Networks. - Zheng Zhang; Jie Bao; Zhixin Zhou; Nicolò Colombo; Lixin Cheng; Rui Luo (UAI, 2025)

Correction to: Conformal load prediction with transductive graph autoencoders. - Rui Luo 002; Nicolò Colombo (Mach. Learn., 2025)

Enhanced route planning with calibrated uncertainty set. - Lingxuan Tang; Rui Luo 002; Zhixin Zhou; Nicolò Colombo (Mach. Learn., 2025)

Enhanced Route Planning with Calibrated Uncertainty Set. - Lingxuan Tang; Rui Luo; Zhixin Zhou; Nicolò Colombo (CoRR, 2025)

Conformal load prediction with transductive graph autoencoders. - Rui Luo 002; Nicolò Colombo (Mach. Learn., 2025)

Assumption-free fidelity bounds for hardware noise characterization. - Nicolò Colombo (CoRR, 2025)