b'Tianhao Wang'

research

∙ 09/13/2023

DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass

Differentially private stochastic gradient descent (DP-SGD) adds noise t...

0 Minxin Du, et al. ∙

research

∙ 07/27/2023

The Marginal Value of Momentum for Small Learning Rate SGD

Momentum is known to accelerate the convergence of gradient descent in s...

0 Runzhe Wang, et al. ∙

research

∙ 07/03/2023

Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems

With the emergence of large foundational models, model-serving systems a...

0 Debopam Sanyal, et al. ∙

research

∙ 06/14/2023

Differentially Private Wireless Federated Learning Using Orthogonal Sequences

We propose a novel privacy-preserving uplink over-the-air computation (A...

0 Xizixiang Wei, et al. ∙

research

∙ 06/01/2023

Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features

The black-box nature of complex Neural Network (NN)-based models has hin...

0 Kunal Mukherjee, et al. ∙

research

∙ 05/10/2023

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

We study multi-agent reinforcement learning in the setting of episodic M...

3 Yifei Min, et al. ∙

research

∙ 04/15/2023

Practical Differentially Private and Byzantine-resilient Federated Learning

Privacy and Byzantine resilience are two indispensable requirements for ...

5 Zihang Xiang, et al. ∙

research

∙ 03/02/2023

GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces

In this paper we focus on the problem of generating high-quality, privat...

0 Josephine Lamp, et al. ∙

research

∙ 10/06/2022

Federated Boosted Decision Trees with Differential Privacy

There is great demand for scalable, secure, and efficient privacy-preser...

0 Samuel Maddock, et al. ∙

research

∙ 08/02/2022

Differentially Private Vertical Federated Clustering

In many applications, multiple parties have private data regarding the s...

3 Zitao Li, et al. ∙

research

∙ 07/08/2022

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

As part of the effort to understand implicit bias of gradient descent in...

8 Zhiyuan Li, et al. ∙

research

∙ 07/07/2022

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

We study federated contextual linear bandits, where M agents cooperate w...

6 Jiafan He, et al. ∙

research

∙ 06/27/2022

Universality of Approximate Message Passing algorithms and tensor networks

Approximate Message Passing (AMP) algorithms provide a valuable tool for...

0 Tianhao Wang, et al. ∙

research

∙ 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...

6 Tianhao Wang, et al. ∙

research

∙ 05/25/2022

Memorization in NLP Fine-tuning Methods

Large language models are shown to present privacy risks through memoriz...

0 Fatemehsadat Mireshghallah, et al. ∙

research

∙ 03/07/2022

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

We study a Markov matching market involving a planner and a set of strat...

2 Yifei Min, et al. ∙

research

∙ 02/21/2022

Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision

Proper communication is key to the adoption and implementation of differ...

0 Aiping Xiong, et al. ∙

research

∙ 12/07/2021

Locally Differentially Private Sparse Vector Aggregation

Vector mean estimation is a central primitive in federated analytics. In...

0 Mingxun Zhou, et al. ∙

research

∙ 11/24/2021

Learning to Refit for Convex Learning Problems

Machine learning (ML) models need to be frequently retrained on changing...

0 Yingyan Zeng, et al. ∙

research

∙ 10/25/2021

Learning Stochastic Shortest Path with Linear Function Approximation

We study the stochastic shortest path (SSP) problem in reinforcement lea...

11 Yifei Min, et al. ∙

research

∙ 10/13/2021

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

Understanding the implicit bias of Stochastic Gradient Descent (SGD) is ...

30 Zhiyuan Li, et al. ∙

research

∙ 10/05/2021

Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization

We study a class of Approximate Message Passing (AMP) algorithms for sym...

0 Xinyi Zhong, et al. ∙

research

∙ 07/14/2021

Zero-Round Active Learning

Active learning (AL) aims at reducing labeling effort by identifying the...

0 Si Chen, et al. ∙

research

∙ 07/13/2021

Learnability of Learning Performance and Its Application to Data Valuation

For most machine learning (ML) tasks, evaluating learning performance on...

1 Tianhao Wang, et al. ∙

research

∙ 07/02/2021

Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM

Motivated by applications to single-particle cryo-electron microscopy (c...

0 Zhou Fan, et al. ∙

research

∙ 06/24/2021

DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges

We summarize the experience of participating in two differential privacy...

0 Ninghui Li, et al. ∙

research

∙ 06/22/2021

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

We study the off-policy evaluation (OPE) problem in reinforcement learni...

7 Yifei Min, et al. ∙

research

∙ 06/10/2021

A Unified Framework for Task-Driven Data Quality Management

High-quality data is critical to train performant Machine Learning (ML) ...

0 Tianhao Wang, et al. ∙

research

∙ 06/02/2021

Differential Privacy for Text Analytics via Natural Text Sanitization

Texts convey sophisticated knowledge. However, texts also convey sensiti...

0 Xiang Yue, et al. ∙

research

∙ 05/30/2021

Concurrent Composition of Differential Privacy

We initiate a study of the composition properties of interactive differe...

0 Salil Vadhan, et al. ∙

research

∙ 04/23/2021

One-Round Active Learning

Active learning has been a main solution for reducing data labeling cost...

0 Tianhao Wang, et al. ∙

research

∙ 03/27/2021

Graph Unlearning

The right to be forgotten states that a data subject has the right to er...

23 Min Chen, et al. ∙

research

∙ 03/02/2021

DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

Deep learning techniques have achieved remarkable performance in wide-ra...

0 Wenxiao Wang, et al. ∙

research

∙ 01/06/2021

Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints

We study reinforcement learning (RL) with linear function approximation ...

37 Tianhao Wang, et al. ∙

research

∙ 12/30/2020

PrivSyn: Differentially Private Data Synthesis

In differential privacy (DP), a challenging problem is to generate synth...

0 Zhikun Zhang, et al. ∙

research

∙ 12/17/2020

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

Many proximity-based tracing (PCT) protocols have been proposed and depl...

0 Fabrizio Cicala, et al. ∙

research

∙ 10/23/2020

Analysis and Verification of Relation between Digitizer's Sampling Properties and Energy Resolution of HPGe Detectors

The CDEX (China Dark matter Experiment) aims at detection of WIMPs (Weak...

0 Jinfu Zhu, et al. ∙

research

∙ 09/14/2020

Answering Multi-Dimensional Range Queries under Local Differential Privacy

In this paper, we tackle the problem of answering multi-dimensional rang...

0 Jianyu Yang, et al. ∙

research

∙ 09/14/2020

A Principled Approach to Data Valuation for Federated Learning

Federated learning (FL) is a popular technique to train machine learning...

25 Tianhao Wang, et al. ∙

research

∙ 09/11/2020

Improving Robustness to Model Inversion Attacks via Mutual Information Regularization

This paper studies defense mechanisms against model inversion (MI) attac...

0 Tianhao Wang, et al. ∙

research

∙ 05/24/2020

Continuous Release of Data Streams under both Centralized and Local Differential Privacy

In this paper, we study the problem of publishing a stream of real-value...

0 Tianhao Wang, et al. ∙

research

∙ 05/05/2020

When Machine Unlearning Jeopardizes Privacy

The right to be forgotten states that a data owner has the right to eras...

10 Min Chen, et al. ∙

research

∙ 03/31/2020

Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model

We study the non-convex optimization landscape for maximum likelihood es...

0 Zhou Fan, et al. ∙

research

∙ 03/31/2020

Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension

Differential privacy protects an individual's privacy by perturbing data...

0 Aiping Xiong, et al. ∙

research

∙ 12/02/2019

Estimating Numerical Distributions under Local Differential Privacy

When collecting information, local differential privacy (LDP) relieves t...

0 Zitao Li, et al. ∙

research

∙ 10/31/2019

Robust and Undetectable White-Box Watermarks for Deep Neural Networks

Training deep neural networks (DNN) is expensive in terms of computation...

0 Tianhao Wang, et al. ∙

research

∙ 08/30/2019

MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

0 Tianhao Wang, et al. ∙

research

∙ 08/30/2019

Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

0 Tianhao Wang, et al. ∙

research

∙ 05/20/2019

Consistent and Accurate Frequency Oracles under Local Differential Privacy

Local Differential Privacy (LDP) protects user privacy from the data col...

0 Tianhao Wang, et al. ∙

Tianhao Wang

Featured Co-authors