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09/21/2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
We study the problem of in-context learning (ICL) with large language mo...
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05/29/2023
Membership Inference Attacks against Language Models via Neighbourhood Comparison
Membership Inference attacks (MIAs) aim to predict whether a data sample...
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05/17/2023
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
With the advent of fluent generative language models that can produce co...
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12/20/2022
Privacy-Preserving Domain Adaptation of Semantic Parsers
Task-oriented dialogue systems often assist users with personal or confi...
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09/13/2022
Non-Parametric Temporal Adaptation for Social Media Topic Classification
User-generated social media data is constantly changing as new trends in...
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06/03/2022
Differentially Private Model Compression
Recent papers have shown that large pre-trained language models (LLMs) s...
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05/25/2022
Memorization in NLP Fine-tuning Methods
Large language models are shown to present privacy risks through memoriz...
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03/24/2022
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models
Recent work on controlled text generation has either required attribute-...
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03/08/2022
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
The wide adoption and application of Masked language models (MLMs) on se...
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02/11/2022
What Does it Mean for a Language Model to Preserve Privacy?
Natural language reflects our private lives and identities, making its p...
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10/01/2021
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Global models are trained to be as generalizable as possible, with user ...
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09/10/2021
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness
Text style can reveal sensitive attributes of the author (e.g. race or a...
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08/09/2021
Efficient Hyperparameter Optimization for Differentially Private Deep Learning
Tuning the hyperparameters in the differentially private stochastic grad...
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06/26/2021
Benchmarking Differential Privacy and Federated Learning for BERT Models
Natural Language Processing (NLP) techniques can be applied to help with...
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06/24/2021
When Differential Privacy Meets Interpretability: A Case Study
Given the increase in the use of personal data for training Deep Neural ...
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06/22/2021
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Recent advances in differentially private deep learning have demonstrate...
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03/12/2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Neural language models are known to have a high capacity for memorizatio...
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01/14/2021
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
Deep Neural Networks (DNNs) are widely used for decision making in a myr...
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09/10/2020
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Deployment of deep learning in different fields and industries is growin...
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03/26/2020
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference
INFerence-as-a-Service (INFaaS) in the cloud has enabled the prevalent u...
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02/29/2020
Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
As deep neural networks make their ways into different domains, their co...
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05/26/2019
