Clustering and Learning from Imbalanced Data. - Naman D. Singh; Abhinav Dhall (CoRR, 2018)

Domain Adaptation based Topic Modeling Techniques for Engagement Estimation in the Wild. - Amanjot Kaur; Bishal Ghosh; Naman Deep Singh; Abhinav Dhall (FG, 2019)

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. - Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein 001 (AAAI, 2022)

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. - Naman Deep Singh; Francesco Croce; Matthias Hein 001 (NeurIPS, 2023)

Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models. - Francesco Croce; Naman D. Singh; Matthias Hein 001 (CoRR, 2023)

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models. - Francesco Croce; Naman D. Singh; Matthias Hein 001 (ECCV (79), 2024)

Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models. - Christian Schlarmann; Naman Deep Singh; Francesco Croce; Matthias Hein 001 (ICML, 2024)

Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP. - Naman Deep Singh; Francesco Croce; Matthias Hein 001 (CoRR, 2024)

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics. - Francesco Croce; Christian Schlarmann; Naman Deep Singh; Matthias Hein 001 (SaTML, 2025)

Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning. - Amit Peleg; Naman Deep Singh; Matthias Hein 001 (CoRR, 2025)

Robustness in Both Domains: CLIP Needs a Robust Text Encoder. - Elías Abad-Rocamora; Christian Schlarmann; Naman Deep Singh; Yongtao Wu; Matthias Hein 001; Volkan Cevher (CoRR, 2025)