Jie Ding

According to our database, Jie Ding authored at least 91 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Quickest Change Detection for Unnormalized Statistical Models.

Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh

IEEE Trans. Inf. Theory, February, 2024

RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees.

Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding

CoRR, 2024

2023

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization.

Gen Li, Ganghua Wang, Jie Ding

IEEE Trans. Inf. Theory, September, 2023

Towards Understanding Variation-Constrained Deep Neural Networks.

Gen Li, Jie Ding

IEEE Trans. Signal Process., 2023

Assisted Learning for Organizations with Limited Imbalanced Data.

Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

Trans. Mach. Learn. Res., 2023

Demystifying Poisoning Backdoor Attacks from a Statistical Perspective.

Ganghua Wang, Xun Xian, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

CoRR, 2023

A Framework for Incentivized Collaborative Learning.

Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar

CoRR, 2023

PI-FL: Personalized and Incentivized Federated Learning.

Ahmad Faraz Khan, Xinran Wang, Qi Le, Azal Ahmad Khan, Haider Ali, Jie Ding, Ali Raza Butt, Ali Anwar

CoRR, 2023

Robust Quickest Change Detection for Unnormalized Models.

Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training.

Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang

Proceedings of the Topological, 2023

A Unified Detection Framework for Inference-Stage Backdoor Defenses.

Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Assisted Unsupervised Domain Adaptation.

Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou

Proceedings of the IEEE International Symposium on Information Theory, 2023

Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging.

Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Understanding Backdoor Attacks through the Adaptability Hypothesis.

Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding

Proceedings of the International Conference on Machine Learning, 2023

Characteristic Neural Ordinary Differential Equation.

Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Pruning Deep Neural Networks from a Sparsity Perspective.

Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Quantifying Catastrophic Forgetting in Continual Federated Learning.

Christophe Dupuy, Jimit Majmudar, Jixuan Wang, Tanya G. Roosta, Rahul Gupta, Clement Chung, Jie Ding, Salman Avestimehr

Proceedings of the IEEE International Conference on Acoustics, 2023

Exploring Gradient Oscillation in Deep Neural Network Training.

Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang

Proceedings of the 59th Annual Allerton Conference on Communication, 2023

Score-based Quickest Change Detection for Unnormalized Models.

Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Parallel Assisted Learning.

Xinran Wang, Jiawei Zhang, Mingyi Hong, Yuhong Yang, Jie Ding

IEEE Trans. Signal Process., 2022

Interval Privacy: A Framework for Privacy-Preserving Data Collection.

Jie Ding, Bangjun Ding

IEEE Trans. Signal Process., 2022

$\ell _1$ Regularization in Two-Layer Neural Networks.

Gen Li, Yuantao Gu, Jie Ding

IEEE Signal Process. Lett., 2022

A Framework for Understanding Model Extraction Attack and Defense.

Xun Xian, Mingyi Hong, Jie Ding

CoRR, 2022

A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation.

Wenjing Yang, Ganghua Wang, Enmao Diao, Vahid Tarokh, Jie Ding, Yuhong Yang

CoRR, 2022

Score-Based Hypothesis Testing for Unnormalized Models.

Suya Wu, Enmao Diao, Khalil Elkhalil, Jie Ding, Vahid Tarokh

IEEE Access, 2022

Understanding Model Extraction Games.

Xun Xian, Mingyi Hong, Jie Ding

Proceedings of the 4th IEEE International Conference on Trust, 2022

SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened.

Erum Mushtaq, Jie Ding, Salman Avestimehr

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Mismatched Supervised Learning.

Xun Xian, Mingyi Hong, Jie Ding

Proceedings of the IEEE International Conference on Acoustics, 2022

On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections.

Mohammadreza Soltani, Suya Wu, Yuerong Li, Jie Ding, Vahid Tarokh

Proceedings of the Data Compression Conference, 2022

Multimodal Controller for Generative Models.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the Computer Vision and Machine Intelligence, 2022

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders.

Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding

Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2022, Pacific Grove, CA, USA, October 31, 2022

2021

On Statistical Efficiency in Learning.

Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh

IEEE Trans. Inf. Theory, 2021

Model Linkage Selection for Cooperative Learning.

Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh

J. Mach. Learn. Res., 2021

SPIDER: Searching Personalized Neural Architecture for Federated Learning.

Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr

CoRR, 2021

Characteristic Neural Ordinary Differential Equations.

Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh

CoRR, 2021

Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders.

Enmao Diao, Vahid Tarokh, Jie Ding

CoRR, 2021

Assisted Learning for Organizations with Limited Data.

Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

CoRR, 2021

Targeted Cross-Validation.

Jiawei Zhang, Jie Ding, Yuhong Yang

CoRR, 2021

Subset Privacy: Draw from an Obfuscated Urn.

Ganghua Wang, Jie Ding

CoRR, 2021

The Rate of Convergence of Variation-Constrained Deep Neural Networks.

Gen Li, Yuantao Gu, Jie Ding

CoRR, 2021

Interval Privacy: A Framework for Data Collection.

Jie Ding, Bangjun Ding

CoRR, 2021

SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients.

Enmao Diao, Jie Ding, Vahid Tarokh

CoRR, 2021

Gradient Assisted Learning.

Enmao Diao, Jie Ding, Vahid Tarokh

CoRR, 2021

Hybrid Control Synthesis for Turing Instability and Hopf Bifurcation of Marine Planktonic Ecosystems With Diffusion.

Yunxiang Lu, Min Xiao, Jinling Liang, Jie Ding, Ying Zhou, Youhong Wan, Chunxia Fan

IEEE Access, 2021

Information Laundering for Model Privacy.

Xinran Wang, Yu Xiang, Jun Gao, Jie Ding

Proceedings of the 9th International Conference on Learning Representations, 2021

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the 9th International Conference on Learning Representations, 2021

Assisted Learning: Cooperative AI with Autonomy.

Jiaying Zhou, Xun Xian, Na Li, Jie Ding

Proceedings of the IEEE International Conference on Acoustics, 2021

Compressing Deep Networks Using Fisher Score of Feature Maps.

Mohammadreza Soltani, Suya Wu, Yuerong Li, Robert J. Ravier, Jie Ding, Vahid Tarokh

Proceedings of the 31st Data Compression Conference, 2021

Fisher Auto-Encoders.

Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

ASCII: ASsisted Classification with Ignorance Interchange.

Jiaying Zhou, Xun Xian, Na Li, Jie Ding

CoRR, 2020

The Efficacy of L_1s Regularization in Two-Layer Neural Networks.

Gen Li, Yuantao Gu, Jie Ding

CoRR, 2020

Large Deviation Principle for the Whittaker 2d Growth Model.

Jun Gao, Jie Ding

CoRR, 2020

Imitation Privacy.

Xun Xian, Xinran Wang, Mingyi Hong, Jie Ding, Reza Ghanadan

CoRR, 2020

Meta Clustering for Collaborative Learning.

Chenglong Ye, Jie Ding, Reza Ghanadan

CoRR, 2020

Assisted Learning and Imitation Privacy.

Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

CoRR, 2020

Assisted Learning: A Framework for Multi-Organization Learning.

Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Information of Feature Maps and Pruning of Deep Neural Networks.

Mohammadreza Soltani, Suya Wu, Jie Ding, Robert J. Ravier, Vahid Tarokh

Proceedings of the 25th International Conference on Pattern Recognition, 2020

Speech Emotion Recognition with Dual-Sequence LSTM Architecture.

Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Supervised Encoding for Discrete Representation Learning.

Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Perception-Distortion Trade-Off with Restricted Boltzmann Machines.

Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Deep Clustering of Compressed Variational Embeddings.

Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the Data Compression Conference, 2020

DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the Data Compression Conference, 2020

Forecasting with Multiple Seasonality.

Tianyang Xie, Jie Ding

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

"To Tell You the Truth" by Interval-Private Data.

Jie Ding, Bangjun Ding

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Estimation of the Evolutionary Spectra With Application to Stationarity Test.

Yu Xiang, Jie Ding, Vahid Tarokh

IEEE Trans. Signal Process., 2019

Asymptotically Optimal Prediction for Time-Varying Data Generating Processes.

Jie Ding, Jiawei Zhou, Vahid Tarokh

IEEE Trans. Inf. Theory, 2019

A Binary Regression Adaptive Goodness-of-fit Test (BAGofT).

Jiawei Zhang, Jie Ding, Yuhong Yang

CoRR, 2019

Perception-Distortion Trade-off with Restricted Boltzmann Machines.

Chris Cannella, Jie Ding, Mohammadreza Soltani, Vahid Tarokh

CoRR, 2019

Distributed Lossy Image Compression with Recurrent Networks.

Enmao Diao, Jie Ding, Vahid Tarokh

CoRR, 2019

Gradient Information for Representation and Modeling.

Jie Ding, A. Robert Calderbank, Vahid Tarokh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Restricted Recurrent Neural Networks.

Enmao Diao, Jie Ding, Vahid Tarokh

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Analysis of Multistate Autoregressive Models.

Jie Ding, Shahin Shahrampour, Kathryn Heal, Vahid Tarokh

IEEE Trans. Signal Process., 2018

Bridging AIC and BIC: A New Criterion for Autoregression.

Jie Ding, Vahid Tarokh, Jing-Yu Yang

IEEE Trans. Inf. Theory, 2018

Online Learning for Multimodal Data Fusion With Application to Object Recognition.

Shahin Shahrampour, Mohammad Noshad, Jie Ding, Vahid Tarokh

IEEE Trans. Circuits Syst. II Express Briefs, 2018

Model Selection Techniques: An Overview.

Jie Ding, Vahid Tarokh, Yuhong Yang

IEEE Signal Process. Mag., 2018

Evolutionary Spectra Based on the Multitaper Method with Application To Stationarity Test.

Yu Xiang, Jie Ding, Vahid Tarokh

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

A Penalized Method for the Predictive Limit of Learning.

Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series.

Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh

IEEE Trans. Signal Process., 2017

Multiple Change Point Analysis: Fast Implementation and Strong Consistency.

Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh

IEEE Trans. Signal Process., 2017

The number of independent sets in hexagonal graphs.

Zhun Deng, Jie Ding, Kathryn Heal, Vahid Tarokh

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

Modeling nonlinearity in multi-dimensional dependent data.

Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Optimal prediction of data with unknown abrupt change points.

Jie Ding, Jiawei Zhou, Vahid Tarokh

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Detecting structural changes in dependent data.

Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

2015

Complementary Lattice Arrays for Coded Aperture Imaging.

Jie Ding, Mohammad Noshad, Vahid Tarokh

CoRR, 2015

Key Pre-Distributions From Graph-Based Block Designs.

Jie Ding, Abdelmadjid Bouabdallah, Vahid Tarokh

CoRR, 2015

Sequential learning of multi-state autoregressive time series.

Mohammad Noshad, Jie Ding, Vahid Tarokh

Proceedings of the 2015 Conference on research in adaptive and convergent systems, 2015

Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics.

Jie Ding, Mohammad Noshad, Vahid Tarokh

Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

Order Selection of Autoregressive Processes Using Bridge Criterion.

Jie Ding, Mohammad Noshad, Vahid Tarokh

Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

Data-driven learning of the number of states in multi-state autoregressive models.

Jie Ding, Mohammad Noshad, Vahid Tarokh

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015