On the expressivity of bi-Lipschitz normalizing flows. - Alexandre Verine; Benjamin Négrevergne; Yann Chevaleyre; Fabrice Rossi (ACML, 2022)

Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows. - Alexandre Verine; Benjamin Négrevergne; Muni Sreenivas Pydi; Yann Chevaleyre (NeurIPS, 2023)

Training Normalizing Flows with the Precision-Recall Divergence. - Alexandre Verine; Benjamin Négrevergne; Muni Sreenivas Pydi; Yann Chevaleyre (CoRR, 2023)

Exploring Precision and Recall to assess the quality and diversity of LLMs. - Florian Le Bronnec; Alexandre Verine; Benjamin Négrevergne; Yann Chevaleyre; Alexandre Allauzen (ACL (1), 2024)

Optimal Budgeted Rejection Sampling for Generative Models. - Alexandre Verine; Muni Sreenivas Pydi; Benjamin Négrevergne; Yann Chevaleyre (AISTATS, 2024)

Quality and Diversity in Generative Models through the lens of f-divergences. (Explorer la qualité et la diversité des modèles génératifs). - Alexandre Verine (2024)

Conditional Distribution Quantization in Machine Learning. - Blaise Delattre; Sylvain Delattre; Alexandre Verine; Alexandre Allauzen (CoRR, 2025)

Improving Discriminator Guidance in Diffusion Models. - Alexandre Verine; Mehdi Inane; Florian Le Bronnec; Benjamin Négrevergne; Yann Chevaleyre (CoRR, 2025)

Improving Diversity in Language Models: When Temperature Fails, Change the Loss. - Alexandre Verine; Florian Le Bronnec; Kunhao Zheng; Alexandre Allauzen; Yann Chevaleyre; Benjamin Négrevergne (CoRR, 2025)