2 citations · 4 across the 6 of their papers we have counts for
8 papers
Robustness in Both Domains: CLIP Needs a Robust Text Encoder
Elias Abad Rocamora, Christian Schlarmann, Naman Deep Singh +3
Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-…
Single-pass Detection of Jailbreaking Input in Large Language Models
Leyla Naz Candogan, Yongtao Wu, Elias Abad Rocamora +2
Defending aligned Large Language Models (LLMs) against jailbreaking attacks is a challenging problem, with existing approaches requiring multiple requests or even queries to auxili…
Certified Robustness Under Bounded Levenshtein Distance
Elias Abad Rocamora, Grigorios G. Chrysos, Volkan Cevher
Text classifiers suffer from small perturbations, that if chosen adversarially, can dramatically change the output of the model. Verification methods can provide robustness certifi…
Linear Attention for Efficient Bidirectional Sequence Modeling
Arshia Afzal, Elias Abad Rocamora, Leyla Naz Candogan +5
Linear Transformers and State Space Models have emerged as efficient alternatives to softmax Transformers for causal sequence modeling, enabling parallel training via matrix multip…
Membership Inference Attacks against Large Vision-Language Models
Zhan Li, Yongtao Wu, Yihang Chen +3
Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios. However, their emergence also raises sign…
Revisiting Character-level Adversarial Attacks for Language Models
Elias Abad Rocamora, Yongtao Wu, Fanghui Liu +2
Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based met…
Efficient local linearity regularization to overcome catastrophic overfitting
Elias Abad Rocamora, Fanghui Liu, Grigorios G. Chrysos +2
Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-ste…
Learning task-specific features for 3D pointcloud graph creation
Elías Abad-Rocamora, Javier Ruiz-Hidalgo
Processing 3D pointclouds with Deep Learning methods is not an easy task. A common choice is to do so with Graph Neural Networks, but this framework involves the creation of edges…