Guillaume Staerman

39 papers A* 6A 4B 1C 1Journal 23Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	Numerically Efficient Parametric Inference for Learning Space-Time Hawkes Processes. DSAA Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau
2025	A	`conf`	Signature Isolation Forest. AISTATS Marta Campi, Guillaume Staerman, Gareth W. Peters, Tomoko Masui
2025	A	`conf`	UNHaP: Unmixing Noise from Hawkes Processes. AISTATS Virginie Loison, Guillaume Staerman, Thomas Moreau
2024	J	`jnl`	A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc
2024	A*	`conf`	Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation. EMNLP Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno Miguel Guerreiro
2024	J	`jnl`	Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation. CoRR Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno Miguel Guerreiro
2024	J	`jnl`	Flexible Parametric Inference for Space-Time Hawkes Processes. CoRR Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau
2024	J	`jnl`	Signature Isolation Forest. CoRR Guillaume Staerman, Marta Campi, Gareth W. Peters
2024	J	`jnl`	Unmixing Noise from Hawkes Process to Model Learned Physiological Events. CoRR Guillaume Staerman, Virginie Loison, Thomas Moreau
2024	A*	`conf`	Unsupervised Layer-Wise Score Aggregation for Textual OOD Detection. AAAI Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes, Jackie C. K. Cheung, Pablo Piantanida, Pierre Colombo
2023	J	`jnl`	A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection. CoRR Eduardo Dadalto Câmara Gomes, Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
2023	J	`jnl`	A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection. Trans. Mach. Learn. Res. Marine Picot, Federica Granese, Guillaume Staerman, Marco Romanelli, Francisco Messina, Pablo Piantanida, Pierre Colombo
2023	—	`conf`	A Novel Information Theoretic Objective to Disentangle Representations for Fair Classification. IJCNLP (Findings) Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
2023	J	`jnl`	A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification. CoRR Pierre Colombo, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
2023	A*	`conf`	FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. ICML Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau
2023	J	`jnl`	Functional anomaly detection: a benchmark study. Int. J. Data Sci. Anal. Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon
2023	J	`jnl`	Hypothesis Transfer Learning with Surrogate Classification Losses. CoRR Anass Aghbalou, Guillaume Staerman
2023	A*	`conf`	Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. ICML Anass Aghbalou, Guillaume Staerman
2023	—	`conf`	Toward Stronger Textual Attack Detectors. EMNLP (Findings) Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
2023	J	`jnl`	Toward Stronger Textual Attack Detectors. CoRR Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
2023	J	`jnl`	Unsupervised Layer-wise Score Aggregation for Textual OOD Detection. CoRR Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes, Jackie C. K. Cheung, Pablo Piantanida, Pierre Colombo
2022	A*	`conf`	Beyond Mahalanobis Distance for Textual OOD Detection. NeurIPS Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
2022	J	`jnl`	Beyond Mahalanobis-Based Scores for Textual OOD Detection. CoRR Pierre Colombo, Eduardo Dadalto Câmara Gomes, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
2022	J	`jnl`	FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. CoRR Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau
2022	J	`jnl`	Functional Anomaly Detection: a Benchmark Study. CoRR Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon
2022	—	`conf`	Learning Disentangled Textual Representations via Statistical Measures of Similarity. ACL (1) Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
2022	J	`jnl`	Learning Disentangled Textual Representations via Statistical Measures of Similarity. CoRR Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida
2021	J	`jnl`	Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon
2021	—	`conf`	Automatic Text Evaluation through the Lens of Wasserstein Barycenters. EMNLP (1) Pierre Colombo, Guillaume Staerman, Chloé Clavel, Pablo Piantanida
2021	J	`jnl`	Automatic Text Evaluation through the Lens of Wasserstein Barycenters. CoRR Pierre Colombo, Guillaume Staerman, Chloé Clavel, Pablo Piantanida
2021	J	`jnl`	Depth-based pseudo-metrics between probability distributions. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2021	A*	`conf`	Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. ICML Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon
2021	A	`conf`	When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
2020	J	`jnl`	How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers. CoRR Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon
2020	A	`conf`	The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. AISTATS Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon
2020	J	`jnl`	When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
2019	C	`conf`	Functional Isolation Forest. ACML Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2019	J	`jnl`	Functional Isolation Forest. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2019	J	`jnl`	The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon