182 citations · 625 across the 44 of their papers we have counts for
45 papers
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang +2
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. However, d…
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar +2
Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to external modi operandi involving halting, modifying, and then resuming the generation proces…
2D-Shapley: A Framework for Fragmented Data Valuation
Zhihong Liu, Hoang Anh Just, Xiangyu Chang +2
Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of mach…
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu +1
Traditionally, data selection has been studied in settings where all samples from prospective sources are fully revealed to a machine learning developer. However, in practical data…
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Junyuan Hong, Yi Zeng, Shuyang Yu +3
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access…
LAVA: Data Valuation without Pre-Specified Learning Algorithms
Hoang Anh Just, Feiyang Kang, Jiachen T. Wang +4
Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calcula…
A Randomized Approach for Tight Privacy Accounting
Jiachen T. Wang, Saeed Mahloujifar, Tong Wu +2
Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or ) is often easy to estima…
A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"
Jiachen T. Wang, Ruoxi Jia
Data valuation is a growing research field that studies the influence of individual data points for machine learning (ML) models. Data Shapley, inspired by cooperative game theory…
A Note on "Towards Efficient Data Valuation Based on the Shapley Value''
Jiachen T. Wang, Ruoxi Jia
The Shapley value (SV) has emerged as a promising method for data valuation. However, computing or estimating the SV is often computationally expensive. To overcome this challenge,…
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms
Minzhou Pan, Yi Zeng, Lingjuan Lyu +2
Backdoor data detection is traditionally studied in an end-to-end supervised learning (SL) setting. However, recent years have seen the proliferating adoption of self-supervised le…
On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
Ming Jin, Vanshaj Khattar, Harshal Kaushik +2
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of…
Variance reduced Shapley value estimation for trustworthy data valuation
Mengmeng Wu, Ruoxi Jia, Changle Lin +2
Data valuation, especially quantifying data value in algorithmic prediction and decision-making, is a fundamental problem in data trading scenarios. The most widely used method is…
Private Data Valuation and Fair Payment in Data Marketplaces
Zhihua Tian, Jian Liu, Jingyu Li +5
Data valuation is an essential task in a data marketplace. It aims at fairly compensating data owners for their contribution. There is increasing recognition in the machine learnin…
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Yi Zeng, Minzhou Pan, Himanshu Jahagirdar +3
Given the volume of data needed to train modern machine learning models, external suppliers are increasingly used. However, incorporating external data poses data poisoning risks,…
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Xuanli He, Qiongkai Xu, Yi Zeng +4
Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, a recent…
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning
Jiachen T. Wang, Saeed Mahloujifar, Shouda Wang +2
Propose-Test-Release (PTR) is a differential privacy framework that works with local sensitivity of functions, instead of their global sensitivity. This framework is typically used…
Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion
Si Chen, Yi Zeng, Jiachen T. Wang +5
Many backdoor removal techniques in machine learning models require clean in-distribution data, which may not always be available due to proprietary datasets. Model inversion techn…
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning
Jiachen T. Wang, Ruoxi Jia
Data valuation has wide use cases in machine learning, including improving data quality and creating economic incentives for data sharing. This paper studies the robustness of data…
IDEAL: Query-Efficient Data-Free Learning from Black-box Models
Jie Zhang, Chen Chen, Lingjuan Lyu
Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to e…
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Yi Zeng, Minzhou Pan, Hoang Anh Just +3
Backdoor attacks insert malicious data into a training set so that, during inference time, it misclassifies inputs that have been patched with a backdoor trigger as the malware spe…
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Weiyan Shi, Ryan Shea, Si Chen +3
Protecting large language models from privacy leakage is becoming increasingly crucial with their wide adoption in real-world products. Yet applying differential privacy (DP), a ca…
Label-Only Model Inversion Attacks via Boundary Repulsion
Mostafa Kahla, Si Chen, Hoang Anh Just +1
Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training d…
ModelPred: A Framework for Predicting Trained Model from Training Data
Yingyan Zeng, Jiachen T. Wang, Si Chen +3
In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model. This is critical for building trust in various s…
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Yi Zeng, Si Chen, Won Park +3
We propose a minimax formulation for removing backdoors from a given poisoned model based on a small set of clean data. This formulation encompasses much of prior work on backdoor…