333 citations · 395 across the 31 of their papers we have counts for
7 papers · 1 filter
The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks
Zhou Feng, Jiahao Chen, Chunyi Zhou +5
Deep neural networks (DNNs) underpin critical applications yet remain vulnerable to backdoor attacks, typically reliant on heuristic brute-force methods. Despite significant empiri…
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Susan Athey, Herman Brunborg, Tianyu Du +2
This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history. Because histories are sequences of occupations, th…
ReMasker: Imputing Tabular Data with Masked Autoencoding
Tianyu Du, Luca Melis, Ting Wang
We present ReMasker, a new method of imputing missing values in tabular data by extending the masked autoencoding framework. Compared with prior work, ReMasker is both simple -- be…
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Zhaohan Xi, Tianyu Du, Changjiang Li +5
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this…
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
Yunruo Zhang, Tianyu Du, Shouling Ji +2
It is well-known that recurrent neural networks (RNNs), although widely used, are vulnerable to adversarial attacks including one-frame attacks and multi-frame attacks. Though a fe…
Torch-Choice: A PyTorch Package for Large-Scale Choice Modeling with Python
Tianyu Du, Ayush Kanodia, Susan Athey
The is an open-source library for flexible, fast choice modeling with Python and PyTorch. provides a data s…
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…