182 citations · 389 across the 15 of their papers we have counts for
16 papers
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Melissa Mozifian, Tristan Sylvain, Dave Evans +1
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explor…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
Hojjat Aghakhani, Wei Dai, Andre Manoel +7
With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massiv…
Memorization in NLP Fine-tuning Methods
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang +2
Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little…
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang, Jinghui Chen, Quanquan Gu +1
Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability…
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries
Fnu Suya, Jianfeng Chi, David Evans +1
We study adversarial examples in a black-box setting where the adversary only has API access to the target model and each query is expensive. Prior work on black-box adversarial ex…