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08/30/2023
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Data valuation, a critical aspect of data-centric ML research, aims to q...
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08/20/2023
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Current literature, aiming to surpass the "Chain-of-Thought" approach, o...
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06/18/2023
2D-Shapley: A Framework for Fragmented Data Valuation
Data valuation – quantifying the contribution of individual data sources...
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06/04/2023
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pr...
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04/28/2023
LAVA: Data Valuation without Pre-Specified Learning Algorithms
Traditionally, data valuation is posed as a problem of equitably splitti...
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04/09/2023
A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"
Data valuation is a growing research field that studies the influence of...
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02/22/2023
A Note on "Towards Efficient Data Valuation Based on the Shapley Value”
The Shapley value (SV) has emerged as a promising method for data valuat...
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02/22/2023
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms
Backdoor data detection is traditionally studied in an end-to-end superv...
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12/02/2022
On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
We study the expressibility and learnability of convex optimization solu...
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10/30/2022
Robust Data Valuation via Variance Reduced Data Shapley
Data valuation, especially quantifying data value in algorithmic predict...
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10/17/2022
Private Data Valuation and Fair Payment in Data Marketplaces
Data valuation is an essential task in a data marketplace. It aims at fa...
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10/12/2022
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Given the volume of data needed to train modern machine learning models,...
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09/19/2022
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Previous works have validated that text generation APIs can be stolen th...
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06/14/2022
Turning a Curse Into a Blessing: Enabling Clean-Data-Free Defenses by Model Inversion
It is becoming increasingly common to utilize pre-trained models provide...
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05/30/2022
Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity
This paper studies the robustness of data valuation to noisy model perfo...
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04/15/2022
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
With the increasing adoption of NLP models in real-world products, it be...
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04/11/2022
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Backdoor attacks insert malicious data into a training set so that, duri...
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03/03/2022
Label-Only Model Inversion Attacks via Boundary Repulsion
Recent studies show that the state-of-the-art deep neural networks are v...
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11/24/2021
Learning to Refit for Convex Learning Problems
Machine learning (ML) models need to be frequently retrained on changing...
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10/07/2021
Adversarial Unlearning of Backdoors via Implicit Hypergradient
We propose a minimax formulation for removing backdoors from a given poi...
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08/30/2021
Selective Differential Privacy for Language Modeling
With the increasing adoption of language models in applications involvin...
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07/14/2021
Zero-Round Active Learning
Active learning (AL) aims at reducing labeling effort by identifying the...
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07/13/2021
Learnability of Learning Performance and Its Application to Data Valuation
For most machine learning (ML) tasks, evaluating learning performance on...
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06/10/2021
A Unified Framework for Task-Driven Data Quality Management
High-quality data is critical to train performant Machine Learning (ML) ...
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04/23/2021
One-Round Active Learning
Active learning has been a main solution for reducing data labeling cost...
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04/07/2021
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective
Backdoor attacks have been considered a severe security threat to deep l...
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03/02/2021
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Deep learning techniques have achieved remarkable performance in wide-ra...
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10/08/2020
Improved Techniques for Model Inversion Attacks
Model inversion (MI) attacks in the whitebox setting are aimed at recons...
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10/05/2020
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
Large-scale language models such as BERT have achieved state-of-the-art ...
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09/14/2020
A Principled Approach to Data Valuation for Federated Learning
Federated learning (FL) is a popular technique to train machine learning...
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09/11/2020
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
This paper studies defense mechanisms against model inversion (MI) attac...
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06/22/2020
Private Distributed Mean Estimation
Ever since its proposal, differential privacy has become the golden stan...
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11/17/2019
REFIT: a Unified Watermark Removal Framework for Deep Learning Systems with Limited Data
Deep neural networks (DNNs) have achieved tremendous success in various ...
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11/17/2019
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
This paper studies model-inversion attacks, in which the access to a mod...
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11/17/2019
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms
This paper focuses on valuating training data for supervised learning ta...
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11/16/2019
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy
Outlier detection and novelty detection are two important topics for ano...
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08/22/2019
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
Given a data set D containing millions of data points and a data consume...
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02/27/2019
Towards Efficient Data Valuation Based on the Shapley Value
"How much is my data worth?" is an increasingly common question posed by...
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10/23/2018
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Despite the great success achieved in machine learning (ML), adversarial...
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07/10/2018
The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity
This paper proposes a fundamental answer to a frequently asked question ...
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06/22/2014