Search Results for author: Jiachen T. Wang

Found 16 papers, 7 papers with code

Efficient Data Shapley for Weighted Nearest Neighbor Algorithms

no code implementations20 Jan 2024 Jiachen T. Wang, Prateek Mittal, Ruoxi Jia

This work aims to address an open problem in data valuation literature concerning the efficient computation of Data Shapley for weighted $K$ nearest neighbor algorithm (WKNN-Shapley).

Computational Efficiency Data Valuation +1

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

1 code implementation27 Nov 2023 Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang

To ensure that the prompts do not leak private information, we introduce the first private prompt generation mechanism, by a differentially-private (DP) ensemble of in-context learning with private demonstrations.

In-Context Learning Language Modelling +3

Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation

no code implementations30 Aug 2023 Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal

Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research.

Data Valuation

BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection

no code implementations23 Aug 2023 Tinghao Xie, Xiangyu Qi, Ping He, Yiming Li, Jiachen T. Wang, Prateek Mittal

We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs), wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs.

Privacy-Preserving In-Context Learning for Large Language Models

no code implementations2 May 2023 Tong Wu, Ashwinee Panda, Jiachen T. Wang, Prateek Mittal

Based on the general paradigm of DP-ICL, we instantiate several techniques showing how to privatize ICL for text classification and language generation.

In-Context Learning Privacy Preserving +3

LAVA: Data Valuation without Pre-Specified Learning Algorithms

1 code implementation28 Apr 2023 Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia

(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.

Data Valuation

A Randomized Approach for Tight Privacy Accounting

no code implementations17 Apr 2023 Jiachen T. Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal

In this paper, we propose a new differential privacy paradigm called estimate-verify-release (EVR), which addresses the challenges of providing a strict upper bound for privacy parameter in DP compositions by converting an estimate of privacy parameter into a formal guarantee.

Privacy Preserving

A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"

1 code implementation9 Apr 2023 Jiachen T. Wang, Ruoxi Jia

In this note, we revisit the work of Jia et al. (2019) and propose a more natural and interpretable utility function that better reflects the performance of KNN models.

Data Valuation

A Note on "Towards Efficient Data Valuation Based on the Shapley Value''

no code implementations22 Feb 2023 Jiachen T. Wang, Ruoxi Jia

Our analysis and insights contribute to a better understanding of the challenges in developing efficient SV estimation algorithms for data valuation.

Data Valuation

Uncovering Adversarial Risks of Test-Time Adaptation

no code implementations29 Jan 2023 Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

Recently, test-time adaptation (TTA) has been proposed as a promising solution for addressing distribution shifts.

Test-time Adaptation

Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning

no code implementations16 Sep 2022 Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang, Ruoxi Jia, Prateek Mittal

As an application of our analysis, we show that PTR and our theoretical results can be used to design differentially private variants for byzantine robust training algorithms that use robust statistics for gradients aggregation.

Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion

no code implementations14 Jun 2022 Si Chen, Yi Zeng, Jiachen T. Wang, Won Park, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, Ruoxi Jia

Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.

Data Banzhaf: A Robust Data Valuation Framework for Machine Learning

2 code implementations30 May 2022 Jiachen T. Wang, Ruoxi Jia

To address this challenge, we introduce the concept of safety margin, which measures the robustness of a data value notion.

Data Valuation

Towards A Proactive ML Approach for Detecting Backdoor Poison Samples

2 code implementations26 May 2022 Xiangyu Qi, Tinghao Xie, Jiachen T. Wang, Tong Wu, Saeed Mahloujifar, Prateek Mittal

First, we uncover a post-hoc workflow underlying most prior work, where defenders passively allow the attack to proceed and then leverage the characteristics of the post-attacked model to uncover poison samples.

ModelPred: A Framework for Predicting Trained Model from Training Data

1 code implementation24 Nov 2021 Yingyan Zeng, Jiachen T. Wang, Si Chen, Hoang Anh Just, Ran Jin, Ruoxi Jia

In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model.

Data Valuation Memorization

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