Search Results for author: Tianhao Wang

Found 30 papers, 8 papers with code

Differentially Private Vertical Federated Clustering

no code implementations2 Aug 2022 Zitao Li, Tianhao Wang, Ninghui Li

To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data.

Federated Learning

Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation

no code implementations22 Jul 2022 Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

Our attack can be easily deployed in the real world since it only requires rotating the object, as we show in both image classification and object detection applications.

Data Augmentation Image Classification +2

Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent

no code implementations8 Jul 2022 Zhiyuan Li, Tianhao Wang, JasonD. Lee, Sanjeev Arora

Conversely, continuous mirror descent with any Legendre function can be viewed as gradient flow with a related commuting parametrization.

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

no code implementations7 Jul 2022 Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu

To the best of our knowledge, this is the first provably efficient algorithm that allows fully asynchronous communication for federated contextual linear bandits, while achieving the same regret guarantee as in the single-agent setting.

Data Banzhaf: A Data Valuation Framework with Maximal Robustness to Learning Stochasticity

no code implementations30 May 2022 Tianhao Wang, Ruoxi Jia

This paper studies the robustness of data valuation to noisy model performance scores.

Memorization in NLP Fine-tuning Methods

no code implementations25 May 2022 FatemehSadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick

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.

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

no code implementations7 Mar 2022 Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang

We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market.

reinforcement-learning

Learning to Refit for Convex Learning Problems

no code implementations24 Nov 2021 Yingyan Zeng, Tianhao Wang, Si Chen, Hoang Anh Just, Ran Jin, Ruoxi Jia

Machine learning (ML) models need to be frequently retrained on changing datasets in a wide variety of application scenarios, including data valuation and uncertainty quantification.

Learning Stochastic Shortest Path with Linear Function Approximation

no code implementations25 Oct 2021 Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

To the best of our knowledge, this is the first algorithm with a sublinear regret guarantee for learning linear mixture SSP.

What Happens after SGD Reaches Zero Loss? --A Mathematical Framework

no code implementations ICLR 2022 Zhiyuan Li, Tianhao Wang, Sanjeev Arora

Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function $L$ can form a manifold.

Towards General Robustness to Bad Training Data

no code implementations29 Sep 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.

Data Summarization

Zero-Round Active Learning

no code implementations14 Jul 2021 Si Chen, Tianhao Wang, Ruoxi Jia

Our algorithm does not rely on any feedback from annotators in the target domain and hence, can be used to perform zero-round active learning or warm-start existing multi-round active learning strategies.

Active Learning Domain Adaptation

Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning

no code implementations13 Jul 2021 Tianhao Wang, Yu Yang, Ruoxi Jia

The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems.

Active Learning

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

no code implementations NeurIPS 2021 Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

We study the off-policy evaluation (OPE) problem in reinforcement learning with linear function approximation, which aims to estimate the value function of a target policy based on the offline data collected by a behavior policy.

reinforcement-learning

A Unified Framework for Task-Driven Data Quality Management

no code implementations10 Jun 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).

Data Summarization Management

Differential Privacy for Text Analytics via Natural Text Sanitization

1 code implementation Findings (ACL) 2021 Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, Sherman S. M. Chow

The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility.

Language Modelling Natural Language Processing +1

One-Round Active Learning

no code implementations23 Apr 2021 Tianhao Wang, Si Chen, Ruoxi Jia

In this work, we initiate the study of one-round active learning, which aims to select a subset of unlabeled data points that achieve the highest model performance after being labeled with only the information from initially labeled data points.

Active Learning

Graph Unlearning

1 code implementation27 Mar 2021 Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang

In the context of machine learning (ML), it requires the ML model provider to remove the data subject's data from the training set used to build the ML model, a process known as \textit{machine unlearning}.

Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints

no code implementations NeurIPS 2021 Tianhao Wang, Dongruo Zhou, Quanquan Gu

In specific, for the batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $\tilde O(\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature mapping, $H$ is the episode length, $T$ is the number of interactions and $B$ is the number of batches.

reinforcement-learning

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols

no code implementations17 Dec 2020 Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang

Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19.

Computers and Society C.3; H.4; J.3; J.7; K.4; K.6.5

A Principled Approach to Data Valuation for Federated Learning

no code implementations14 Sep 2020 Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, Dawn Song

The federated SV preserves the desirable properties of the canonical SV while it can be calculated without incurring extra communication cost and is also able to capture the effect of participation order on data value.

Data Summarization Federated Learning

Improving Robustness to Model Inversion Attacks via Mutual Information Regularization

no code implementations11 Sep 2020 Tianhao Wang, Yuheng Zhang, Ruoxi Jia

This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model.

When Machine Unlearning Jeopardizes Privacy

1 code implementation5 May 2020 Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang

More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive effects on privacy.

Inference Attack Membership Inference Attack

Estimating Numerical Distributions under Local Differential Privacy

2 code implementations2 Dec 2019 Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg, Boris Skoric, Ninghui Li

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator.

RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks

1 code implementation31 Oct 2019 Tianhao Wang, Florian Kerschbaum

White-box watermarking algorithms have the advantage that they do not impact the accuracy of the watermarked model.

Inference Attack

Improving Utility and Security of the Shuffler-based Differential Privacy

1 code implementation30 Aug 2019 Tianhao Wang, Bolin Ding, Min Xu, Zhicong Huang, Cheng Hong, Jingren Zhou, Ninghui Li, Somesh Jha

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator.

Locally Differentially Private Frequency Estimation with Consistency

1 code implementation20 May 2019 Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, Ninghui Li

In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values.

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions

no code implementations ICML 2018 Pan Xu, Tianhao Wang, Quanquan Gu

We provide a second-order stochastic differential equation (SDE), which characterizes the continuous-time dynamics of accelerated stochastic mirror descent (ASMD) for strongly convex functions.

Stochastic Optimization

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