Guillaume Staerman

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 35

Top Topics

Mahalanobis Distance

Generalization Bounds

Robust Statistics

Top Venues

Trans. Mach. Learn. Res.

Publications

Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno Miguel Guerreiro
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation. CoRR (2024)
Guillaume Staerman, Marta Campi, Gareth W. Peters
Signature Isolation Forest. CoRR (2024)
Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes, Jackie C. K. Cheung, Pablo Piantanida, Pierre Colombo
Unsupervised Layer-Wise Score Aggregation for Textual OOD Detection. AAAI (2024)
Guillaume Staerman, Virginie Loison, Thomas Moreau
Unmixing Noise from Hawkes Process to Model Learned Physiological Events. CoRR (2024)
Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau
Flexible Parametric Inference for Space-Time Hawkes Processes. CoRR (2024)
Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. 2024 (2024)
Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes, Jackie CK Cheung, Pablo Piantanida, Pierre Colombo
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection. CoRR (2023)
Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. ICML (2023)
Anass Aghbalou, Guillaume Staerman
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. ICML (2023)
Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon
Functional anomaly detection: a benchmark study. Int. J. Data Sci. Anal. 16 (1) (2023)
Anass Aghbalou, Guillaume Staerman
Hypothesis Transfer Learning with Surrogate Classification Losses. CoRR (2023)
Eduardo Dadalto Câmara Gomes, Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection. CoRR (2023)
Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
A Novel Information Theoretic Objective to Disentangle Representations for Fair Classification. IJCNLP (Findings) (2023)
Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
Toward Stronger Textual Attack Detectors. EMNLP (Findings) (2023)
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)
Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification. CoRR (2023)
Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
Toward Stronger Textual Attack Detectors. CoRR (2023)
Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
Beyond Mahalanobis-Based Scores for Textual OOD Detection. CoRR (2022)
Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon
Functional Anomaly Detection: a Benchmark Study. CoRR (2022)
Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. CoRR (2022)
Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
Learning Disentangled Textual Representations via Statistical Measures of Similarity. CoRR (2022)
Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
Learning Disentangled Textual Representations via Statistical Measures of Similarity. ACL (1) (2022)
Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
Beyond Mahalanobis Distance for Textual OOD Detection. NeurIPS (2022)
Pierre Colombo, Guillaume Staerman, Chloé Clavel, Pablo Piantanida
Automatic Text Evaluation through the Lens of Wasserstein Barycenters. EMNLP (1) (2021)
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS (2021)
Pierre Colombo, Guillaume Staerman, Chloé Clavel, Pablo Piantanida
Automatic Text Evaluation through the Lens of Wasserstein Barycenters. CoRR (2021)
Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. ICML (2021)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Depth-based pseudo-metrics between probability distributions. CoRR (2021)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. CoRR (2021)
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR (2020)
Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon
How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers. CoRR (2020)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. AISTATS (2020)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure. CoRR (2019)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Functional Isolation Forest. ACML (2019)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Functional Isolation Forest. CoRR (2019)