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Marco Romanelli 0002
Person information
- affiliation: New York University, NY, USA
- affiliation (former): University of Siena, Italy
- affiliation (former): Inria, École Polytechnique, Paris, France
- affiliation (former): University of Paris Saclay, France
Other persons with the same name
- Marco Romanelli 0001
— University of Pisa, Italy
- Marco Romanelli 0003 — EPFL, Lausanne, Switzerland
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2020 – today
- 2024
- [j3]Georg Pichler
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning. IEEE Trans. Dependable Secur. Comput. 21(4): 4290-4296 (2024) - [c13]Federica Granese, Marco Romanelli, Pablo Piantanida:
Optimal Zero-Shot Detector for Multi-Armed Attacks. AISTATS 2024: 2467-2475 - [c12]Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg:
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem. AISTATS 2024: 4051-4059 - [c11]Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg:
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization. ICLR 2024 - [c10]Eduardo Dadalto Câmara Gomes, Marco Romanelli, Georg Pichler, Pablo Piantanida:
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection. ICLR 2024 - [c9]Andrés Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida:
Beyond the Norms: Detecting Prediction Errors in Regression Models. ICML 2024 - [c8]Saswat Das, Marco Romanelli, Ferdinando Fioretto:
Disparate Impact on Group Accuracy of Linearization for Private Inference. ICML 2024 - [i15]Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg:
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization. CoRR abs/2401.12205 (2024) - [i14]Saswat Das
Disparate Impact on Group Accuracy of Linearization for Private Inference. CoRR abs/2402.03629 (2024) - [i13]Federica Granese, Marco Romanelli, Pablo Piantanida:
Optimal Zero-Shot Detector for Multi-Armed Attacks. CoRR abs/2402.15808 (2024) - [i12]Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg:
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem. CoRR abs/2402.16926 (2024) - [i11]Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto:
Low-rank finetuning for LLMs: A fairness perspective. CoRR abs/2405.18572 (2024) - [i10]Andrés Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida:
Beyond the Norms: Detecting Prediction Errors in Regression Models. CoRR abs/2406.06968 (2024) - 2023
- [j2]Marine Picot, Federica Granese, Guillaume Staerman, Marco Romanelli, Francisco Messina, Pablo Piantanida, Pierre Colombo:
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection. Trans. Mach. Learn. Res. 2023 (2023) - [i9]Federica Granese, Marco Romanelli, Siddharth Garg, Pablo Piantanida:
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection. CoRR abs/2302.02216 (2023) - [i8]Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search. CoRR abs/2305.13164 (2023) - [i7]Eduardo Dadalto, Marco Romanelli, Georg Pichler, Pablo Piantanida:
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection. CoRR abs/2306.01710 (2023) - 2022
- [c7]Federica Granese, Marine Picot, Marco Romanelli, Francesco Messina, Pablo Piantanida:
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors. ECML/PKDD (3) 2022: 286-303 - [i6]Georg Pichler, Marco Romanelli, Leonardo Rey Vega, Pablo Piantanida:
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning. CoRR abs/2203.16463 (2022) - [i5]Federica Granese, Marine Picot, Marco Romanelli, Francisco Messina, Pablo Piantanida:
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors. CoRR abs/2206.15415 (2022) - 2021
- [c6]Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida:
DOCTOR: A Simple Method for Detecting Misclassification Errors. NeurIPS 2021: 5669-5681 - [i4]Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida:
DOCTOR: A Simple Method for Detecting Misclassification Errors. CoRR abs/2106.02395 (2021) - 2020
- [b1]Marco Romanelli:
Machine learning methods for privacy protection : leakage measurement and mechanisms design. (Méthodes d'apprentissage machine pour la protection de la vie privée : mesure de leakage et design des mécanismes). Institut Polytechnique of Paris, France, 2020 - [c5]Moreno Falaschi, Catuscia Palamidessi, Marco Romanelli:
Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. Gabbrielli's Festschrift 2020: 11:1-11:20 - [c4]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Estimating g-Leakage via Machine Learning. CCS 2020: 697-716 - [c3]Catuscia Palamidessi
Modern Applications of Game-Theoretic Principles (Invited Paper). CONCUR 2020: 4:1-4:9 - [c2]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Optimal Obfuscation Mechanisms via Machine Learning. CSF 2020: 153-168 - [i3]Catuscia Palamidessi, Marco Romanelli:
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy. CoRR abs/2001.09654 (2020) - [i2]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Pablo Piantanida:
Estimating g-Leakage via Machine Learning. CoRR abs/2005.04399 (2020)
2010 – 2019
- 2019
- [j1]Mattia Dalsass, Margherita Bodini
STRAIN: an R package for multi-locus sequence typing from whole genome sequencing data. BMC Bioinform. 20-S(9): 347:1-347:8 (2019) - [i1]Marco Romanelli, Catuscia Palamidessi, Konstantinos Chatzikokolakis:
Generating Optimal Privacy-Protection Mechanisms via Machine Learning. CoRR abs/1904.01059 (2019) - 2018
- [c1]Catuscia Palamidessi
Feature Selection with Rényi Min-Entropy. ANNPR 2018: 226-239
Coauthor Index
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last updated on 2024-12-10 20:51 CET by the dblp team
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