Search Results for author:
Found 23 papers, 11 papers with code
Learning to Watermark LLM-generated Text via Reinforcement Learning
While prior works focus on token-level watermark that embeds signals into the output, we design a model-level watermark that embeds signals into the LLM weights, and such signals can be detected by a paired detector.
Rethinking Machine Unlearning for Large Language Models
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning.
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors.
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction
The advantage of the multimodal model lies in its ability to process diverse sources of data using proper models and suitable fusion methods, which would enhance the noise resistance of the model while obtaining data diversity.
Effective and Efficient Federated Tree Learning on Hybrid Data
To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data.
Large Language Model Unlearning
To the best of our knowledge, our work is among the first to explore LLM unlearning.
EDoG: Adversarial Edge Detection For Graph Neural Networks
In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation.
LOT: Layer-wise Orthogonal Training on Improving $\ell_2$ Certified Robustness
On the other hand, as existing works show that semi-supervised training helps improve empirical robustness, we aim to bridge the gap and prove that semi-supervised learning also improves the certified robustness of Lipschitz-bounded models.
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks.
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
We propose a general theoretical framework proving that factors involving the model function class regularization are sufficient conditions for relative domain transferability.
On the Certified Robustness for Ensemble Models and Beyond
Thus, to explore the conditions that guarantee to provide certifiably robust ensemble ML models, we first prove that diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption.
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space.
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction.
RAB: Provable Robustness Against Backdoor Attacks
In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models.
TSS: Transformation-Specific Smoothing for Robustness Certification
Moreover, to the best of our knowledge, TSS is the first approach that achieves nontrivial certified robustness on the large-scale ImageNet dataset.
Detecting AI Trojans Using Meta Neural Analysis
To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution.
Characterizing Malicious Edges targeting on Graph Neural Networks
Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets.
A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks.
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.
