Pierre-André Noël

23 papers A* 5Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion. CoRR Frederick Shpilevskiy, Saiyue Lyu, Krishnamurthy Dj Dvijotham, Mathias Lécuyer, Pierre-André Noël
2025	J	`jnl`	AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding. CoRR Ahmed Masry, Juan A. Rodríguez, Tianyu Zhang, Suyuchen Wang, Chao Wang, Aarash Feizi, Akshay Kalkunte Suresh, Abhay Puri, Xiangru Jian, Pierre-André Noël, Sathwik Tejaswi Madhusudhan, Marco Pedersoli, Bang Liu, Nicolas Chapados, Yoshua Bengio, Enamul Hoque, Christopher Pal, Issam H. Laradji, David Vázquez, Perouz Taslakian, Spandana Gella, Sai Rajeswar
2025	A*	`conf`	BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks. ICLR Juan A. Rodríguez, Xiangru Jian, Siba Smarak Panigrahi, Tianyu Zhang, Aarash Feizi, Abhay Puri, Akshay Kalkunte Suresh, François Savard, Ahmed Masry, Shravan Nayak, Rabiul Awal, Mahsa Massoud, Amirhossein Abaskohi, Zichao Li, Suyuchen Wang, Pierre-André Noël, Mats Leon Richter, Saverio Vadacchino, Shubham Agarwal, Sanket Biswas, et al.
2025	J	`jnl`	Breaking the Bottleneck with DiffuApriel: High-Throughput Diffusion LMs with Mamba Backbone. CoRR Vaibhav Singh, Oleksiy Ostapenko, Pierre-André Noël, Torsten Scholak
2025	J	`jnl`	Unifying Autoregressive and Diffusion-Based Sequence Generation. CoRR Nima Fathi, Torsten Scholak, Pierre-André Noël
2024	J	`jnl`	BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks. CoRR Juan A. Rodríguez, Xiangru Jian, Siba Smarak Panigrahi, Tianyu Zhang, Aarash Feizi, Abhay Puri, Akshay Kalkunte, François Savard, Ahmed Masry, Shravan Nayak, Rabiul Awal, Mahsa Massoud, Amirhossein Abaskohi, Zichao Li, Suyuchen Wang, Pierre-André Noël, Mats Leon Richter, Saverio Vadacchino, Shubbam Agarwal, Sanket Biswas, Sara Shanian, Ying Zhang, Noah Bolger, Kurt MacDonald, Simon Fauvel, Sathwik Tejaswi, Srinivas Sunkara, João Monteiro, Krishnamurthy Dj Dvijotham, Torsten Scholak, Nicolas Chapados, Sepideh Kharagani, Sean Hughes, M. Tamer Özsu, Siva Reddy, Marco Pedersoli, Yoshua Bengio, Christopher Pal, Issam H. Laradji, Spandana Gella, Perouz Taslakian, David Vázquez, Sai Rajeswar
2024	A*	`conf`	Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. NeurIPS Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro
2024	A*	`conf`	RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. NeurIPS João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar Mudumba, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Chris Pal, Perouz Taslakian
2024	J	`jnl`	RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. CoRR João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2024	—	`conf`	XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. EMNLP (Findings) João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2024	J	`jnl`	XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference. CoRR João Monteiro, Étienne Marcotte, Pierre-André Noël, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
2023	A*	`conf`	Constraining Representations Yields Models That Know What They Don't Know. ICLR João Monteiro, Pau Rodríguez, Pierre-André Noël, Issam H. Laradji, David Vázquez
2023	J	`jnl`	Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. CoRR Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro
2023	A*	`conf`	Flaky Performances When Pretraining on Relational Databases (Student Abstract). AAAI Shengchao Liu, David Vázquez, Jian Tang, Pierre-André Noël
2022	J	`jnl`	Constraining Representations Yields Models That Know What They Don't Know. CoRR João Monteiro, Pau Rodríguez, Pierre-André Noël, Issam H. Laradji, David Vázquez
2022	J	`jnl`	Flaky Performances when Pretraining on Relational Databases. CoRR Shengchao Liu, David Vázquez, Jian Tang, Pierre-André Noël
2021	J	`jnl`	On the Value of ML Models. CoRR Fabio Casati, Pierre-André Noël, Jie Yang
2015	J	`jnl`	Quantifying dynamical spillover in co-evolving multiplex networks. CoRR Vikram Vijayaraghavan, Pierre-André Noël, Zeev Maoz, Raissa M. D'Souza
2014	J	`jnl`	Peeking into the invitation-based adoption process of OSN-based applications. Comput. Commun. Rev. Mohammad Rezaur Rahman, Pierre-André Noël, Chen-Nee Chuah, Balachander Krishnamurthy, Raissa M. D'Souza, Shyhtsun Felix Wu
2012	J	`jnl`	Bond percolation on a class of correlated and clustered random graphs CoRR Antoine Allard, Laurent Hébert-Dufresne, Pierre-André Noël, Vincent Marceau, Louis J. Dubé
2012	J	`jnl`	Exact solution of bond percolation on small arbitrary graphs CoRR Antoine Allard, Laurent Hébert-Dufresne, Pierre-André Noël, Vincent Marceau, Louis J. Dubé
2011	J	`jnl`	Exact correspondence between a Markov process and propagation on networks CoRR Pierre-André Noël, Antoine Allard, Laurent Hébert-Dufresne, Vincent Marceau, Louis J. Dubé
2011	J	`jnl`	Modeling the dynamical interaction between epidemics on overlay networks CoRR Vincent Marceau, Pierre-André Noël, Laurent Hébert-Dufresne, Antoine Allard, Louis J. Dubé