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ORCIDJie Ding 0002
Person information
- affiliation: University of Minnesota, School of Statistics, Minneapolis, MN, USA
- affiliation: Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA
- affiliation (PhD 2017): Harvard University, Cambridge, MA, USA
Other persons with the same name
- Jie Ding — disambiguation page
- Jie Ding 0001
![[0000-0001-9927-5415]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-9927-5415")
— Huazhong University of Science and Technology, School of Electronic Information and Communications, Wuhan, China (and 1 more) - Jie Ding 0003 — Harbin Engineering University, College of Underwater Acoustic Engineering and the Acoustic Science and Technology Laboratory, China
- Jie Ding 0004 — Sichuan University, Chengdu, Business School, China
- Jie Ding 0005 — Yangzhou University, School of Information Engineering, China
- Jie Ding 0006 — Nanjing University of Posts and Telecommunications, School of Automation, China (and 1 more)
- Jie Ding 0007 — Fudan University, School of Information Science and Engineering, Electronic Engineering Department, Research Center of Smart Networks and Systems, Shanghai, China (and 2 more)
- Jie Ding 0008 — Shanghai Maritime University, Institute of FTZ Supply Chain, China (and 1 more)
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2020 – today
- 2024
[j19]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Quickest Change Detection for Unnormalized Statistical Models. IEEE Trans. Inf. Theory 70(2): 1220-1232 (2024)- [i45]Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding:
RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees. CoRR abs/2403.18774 (2024) - 2023
- [j18]Gen Li, Ganghua Wang, Jie Ding:
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. IEEE Trans. Inf. Theory 69(9): 5921-5935 (2023) - [j17]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. 2023 (2023) - [j16]Gen Li, Jie Ding:
Towards Understanding Variation-Constrained Deep Neural Networks. IEEE Trans. Signal Process. 71: 631-640 (2023) - [c45]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Score-based Quickest Change Detection for Unnormalized Models. AISTATS 2023: 10546-10565 - [c44]Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang:
Exploring Gradient Oscillation in Deep Neural Network Training. Allerton 2023: 1-7 - [c43]Christophe Dupuy, Jimit Majmudar, Jixuan Wang, Tanya G. Roosta, Rahul Gupta, Clement Chung, Jie Ding, Salman Avestimehr:
Quantifying Catastrophic Forgetting in Continual Federated Learning. ICASSP 2023: 1-5 - [c42]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. ICLR 2023 - [c41]Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equation. ICLR 2023 - [c40]Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding:
Understanding Backdoor Attacks through the Adaptability Hypothesis. ICML 2023: 37952-37976 - [c39]Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr:
Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. ISBI 2023: 1-5 - [c38]Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou:
Assisted Unsupervised Domain Adaptation. ISIT 2023: 2482-2487 - [c37]Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding:
A Unified Detection Framework for Inference-Stage Backdoor Defenses. NeurIPS 2023 - [c36]Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang:
Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML 2023: 134-145 - [c35]Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh:
Robust Quickest Change Detection for Unnormalized Models. UAI 2023: 2314-2323 - [i44]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Quickest Change Detection for Unnormalized Statistical Models. CoRR abs/2302.00250 (2023) - [i43]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. CoRR abs/2302.05601 (2023) - [i42]Ahmad Faraz Khan, Xinran Wang, Qi Le, Azal Ahmad Khan, Haider Ali, Jie Ding, Ali Raza Butt, Ali Anwar:
PI-FL: Personalized and Incentivized Federated Learning. CoRR abs/2304.07514 (2023) - [i41]Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr:
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. CoRR abs/2304.09327 (2023) - [i40]Gen Li, Ganghua Wang, Jie Ding:
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. CoRR abs/2305.04267 (2023) - [i39]Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar:
A Framework for Incentivized Collaborative Learning. CoRR abs/2305.17052 (2023) - [i38]Ganghua Wang, Xun Xian, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding:
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. CoRR abs/2310.10780 (2023) - 2022
- [j15]Suya Wu, Enmao Diao, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Score-Based Hypothesis Testing for Unnormalized Models. IEEE Access 10: 71936-71950 (2022) - [j14]Gen Li, Yuantao Gu, Jie Ding:
$\ell _1$ Regularization in Two-Layer Neural Networks. IEEE Signal Process. Lett. 29: 135-139 (2022) - [j13]Jie Ding, Bangjun Ding:
Interval Privacy: A Framework for Privacy-Preserving Data Collection. IEEE Trans. Signal Process. 70: 2443-2459 (2022) - [j12]Xinran Wang, Jiawei Zhang, Mingyi Hong, Yuhong Yang, Jie Ding:
Parallel Assisted Learning. IEEE Trans. Signal Process. 70: 5848-5858 (2022) - [c34]Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding:
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. IEEECONF 2022: 1157-1163 - [c33]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CVMI 2022: 109-121 - [c32]Mohammadreza Soltani, Suya Wu, Yuerong Li, Jie Ding, Vahid Tarokh:
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections. DCC 2022: 482 - [c31]Xun Xian, Mingyi Hong, Jie Ding:
Mismatched Supervised Learning. ICASSP 2022: 4228-4232 - [c30]Erum Mushtaq, Jie Ding, Salman Avestimehr:
What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened. ICMLA 2022: 1740-1747 - [c29]Enmao Diao, Jie Ding, Vahid Tarokh:
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. NeurIPS 2022 - [c28]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. NeurIPS 2022 - [c27]Xun Xian, Mingyi Hong, Jie Ding:
Understanding Model Extraction Games. TPS-ISA 2022: 285-294 - [i37]Mohammadreza Soltani, Suya Wu, Yuerong Li, Jie Ding, Vahid Tarokh:
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections. CoRR abs/2201.11209 (2022) - [i36]Wenjing Yang, Ganghua Wang, Enmao Diao, Vahid Tarokh, Jie Ding, Yuhong Yang:
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation. CoRR abs/2206.05604 (2022) - [i35]Xun Xian, Mingyi Hong, Jie Ding:
A Framework for Understanding Model Extraction Attack and Defense. CoRR abs/2206.11480 (2022) - [i34]Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding:
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. CoRR abs/2212.08779 (2022) - 2021
- [j11]Yunxiang Lu, Min Xiao, Jinling Liang, Jie Ding, Ying Zhou, Youhong Wan, Chunxia Fan:
Hybrid Control Synthesis for Turing Instability and Hopf Bifurcation of Marine Planktonic Ecosystems With Diffusion. IEEE Access 9: 111326-111335 (2021) - [j10]Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh:
Model Linkage Selection for Cooperative Learning. J. Mach. Learn. Res. 22: 256:1-256:44 (2021) - [j9]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
On Statistical Efficiency in Learning. IEEE Trans. Inf. Theory 67(4): 2488-2506 (2021) - [c26]Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. AISTATS 2021: 352-360 - [c25]Mohammadreza Soltani, Suya Wu, Yuerong Li, Robert J. Ravier, Jie Ding, Vahid Tarokh:
Compressing Deep Networks Using Fisher Score of Feature Maps. DCC 2021: 371 - [c24]Jiaying Zhou, Xun Xian, Na Li, Jie Ding:
Assisted Learning: Cooperative AI with Autonomy. ICASSP 2021: 3130-3134 - [c23]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. ICLR 2021 - [c22]Xinran Wang, Yu Xiang, Jun Gao, Jie Ding:
Information Laundering for Model Privacy. ICLR 2021 - [i33]Enmao Diao, Jie Ding, Vahid Tarokh:
Gradient Assisted Learning. CoRR abs/2106.01425 (2021) - [i32]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. CoRR abs/2106.01432 (2021) - [i31]Jie Ding, Bangjun Ding:
Interval Privacy: A Framework for Data Collection. CoRR abs/2106.09565 (2021) - [i30]Gen Li, Yuantao Gu, Jie Ding:
The Rate of Convergence of Variation-Constrained Deep Neural Networks. CoRR abs/2106.12068 (2021) - [i29]Ganghua Wang, Jie Ding:
Subset Privacy: Draw from an Obfuscated Urn. CoRR abs/2107.02013 (2021) - [i28]Jiawei Zhang, Jie Ding, Yuhong Yang:
Targeted Cross-Validation. CoRR abs/2109.06949 (2021) - [i27]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Data. CoRR abs/2109.09307 (2021) - [i26]Enmao Diao, Vahid Tarokh, Jie Ding:
Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders. CoRR abs/2110.13340 (2021) - [i25]Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equations. CoRR abs/2111.13207 (2021) - [i24]Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr:
SPIDER: Searching Personalized Neural Architecture for Federated Learning. CoRR abs/2112.13939 (2021) - 2020
- [c21]Jie Ding, Bangjun Ding:
"To Tell You the Truth" by Interval-Private Data. IEEE BigData 2020: 25-32 - [c20]Tianyang Xie, Jie Ding:
Forecasting with Multiple Seasonality. IEEE BigData 2020: 240-245 - [c19]Enmao Diao, Jie Ding, Vahid Tarokh:
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC 2020: 3-12 - [c18]Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh:
Deep Clustering of Compressed Variational Embeddings. DCC 2020: 399 - [c17]Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh:
Supervised Encoding for Discrete Representation Learning. ICASSP 2020: 3447-3451 - [c16]Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh:
Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP 2020: 4022-4026 - [c15]Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh:
Speech Emotion Recognition with Dual-Sequence LSTM Architecture. ICASSP 2020: 6474-6478 - [c14]Mohammadreza Soltani, Suya Wu, Jie Ding, Robert J. Ravier, Vahid Tarokh:
On the Information of Feature Maps and Pruning of Deep Neural Networks. ICPR 2020: 6988-6995 - [c13]Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan:
Assisted Learning: A Framework for Multi-Organization Learning. NeurIPS 2020 - [i23]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CoRR abs/2002.02572 (2020) - [i22]Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan:
Assisted Learning and Imitation Privacy. CoRR abs/2004.00566 (2020) - [i21]Chenglong Ye, Jie Ding, Reza Ghanadan:
Meta Clustering for Collaborative Learning. CoRR abs/2006.00082 (2020) - [i20]Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. CoRR abs/2007.06120 (2020) - [i19]Tianyang Xie, Jie Ding:
Forecasting with Multiple Seasonality. CoRR abs/2008.12340 (2020) - [i18]Xun Xian, Xinran Wang, Mingyi Hong, Jie Ding, Reza Ghanadan:
Imitation Privacy. CoRR abs/2009.00442 (2020) - [i17]Xinran Wang, Yu Xiang, Jun Gao, Jie Ding:
Information Laundering for Model Privacy. CoRR abs/2009.06112 (2020) - [i16]Jun Gao, Jie Ding:
Large Deviation Principle for the Whittaker 2d Growth Model. CoRR abs/2009.12907 (2020) - [i15]Gen Li, Yuantao Gu, Jie Ding:
The Efficacy of L1s Regularization in Two-Layer Neural Networks. CoRR abs/2010.01048 (2020) - [i14]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. CoRR abs/2010.01264 (2020) - [i13]Jiaying Zhou, Xun Xian, Na Li, Jie Ding:
ASCII: ASsisted Classification with Ignorance Interchange. CoRR abs/2010.10747 (2020) - [i12]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
On Statistical Efficiency in Learning. CoRR abs/2012.13307 (2020)
2010 – 2019
- 2019
- [j8]Jie Ding, Jiawei Zhou, Vahid Tarokh:
Asymptotically Optimal Prediction for Time-Varying Data Generating Processes. IEEE Trans. Inf. Theory 65(5): 3034-3067 (2019) - [j7]Yu Xiang, Jie Ding, Vahid Tarokh:
Estimation of the Evolutionary Spectra With Application to Stationarity Test. IEEE Trans. Signal Process. 67(5): 1353-1365 (2019) - [c12]Enmao Diao, Jie Ding, Vahid Tarokh:
Restricted Recurrent Neural Networks. IEEE BigData 2019: 56-63 - [c11]Jie Ding, A. Robert Calderbank, Vahid Tarokh:
Gradient Information for Representation and Modeling. NeurIPS 2019: 2393-2402 - [i11]Enmao Diao, Jie Ding, Vahid Tarokh:
Distributed Lossy Image Compression with Recurrent Networks. CoRR abs/1903.09887 (2019) - [i10]Enmao Diao, Jie Ding, Vahid Tarokh:
Restricted Recurrent Neural Networks. CoRR abs/1908.07724 (2019) - [i9]Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh:
Speech Emotion Recognition with Dual-Sequence LSTM Architecture. CoRR abs/1910.08874 (2019) - [i8]Chris Cannella, Jie Ding, Mohammadreza Soltani, Vahid Tarokh:
Perception-Distortion Trade-off with Restricted Boltzmann Machines. CoRR abs/1910.09122 (2019) - [i7]Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh:
Deep Clustering of Compressed Variational Embeddings. CoRR abs/1910.10341 (2019) - [i6]Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh:
Supervised Encoding for Discrete Representation Learning. CoRR abs/1910.11067 (2019) - [i5]Jiawei Zhang, Jie Ding, Yuhong Yang:
A Binary Regression Adaptive Goodness-of-fit Test (BAGofT). CoRR abs/1911.03063 (2019) - 2018
- [j6]Jie Ding, Vahid Tarokh, Yuhong Yang:
Model Selection Techniques: An Overview. IEEE Signal Process. Mag. 35(6): 16-34 (2018) - [j5]Shahin Shahrampour, Mohammad Noshad, Jie Ding, Vahid Tarokh:
Online Learning for Multimodal Data Fusion With Application to Object Recognition. IEEE Trans. Circuits Syst. II Express Briefs 65-II(9): 1259-1263 (2018) - [j4]Jie Ding, Vahid Tarokh, Jing-Yu Yang:
Bridging AIC and BIC: A New Criterion for Autoregression. IEEE Trans. Inf. Theory 64(6): 4024-4043 (2018) - [j3]Jie Ding, Shahin Shahrampour, Kathryn Heal, Vahid Tarokh:
Analysis of Multistate Autoregressive Models. IEEE Trans. Signal Process. 66(9): 2429-2440 (2018) - [c10]Yu Xiang, Jie Ding, Vahid Tarokh:
Evolutionary Spectra Based on the Multitaper Method with Application To Stationarity Test. ICASSP 2018: 3994-3998 - [c9]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
A Penalized Method for the Predictive Limit of Learning. ICASSP 2018: 4414-4418 - [i4]Jie Ding, Vahid Tarokh, Yuhong Yang:
Model Selection Techniques - An Overview. CoRR abs/1810.09583 (2018) - 2017
- [j2]Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh:
Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Trans. Signal Process. 65(17): 4495-4510 (2017) - [j1]Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh:
SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Trans. Signal Process. 65(19): 4994-5005 (2017) - [c8]Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh:
Modeling nonlinearity in multi-dimensional dependent data. GlobalSIP 2017: 206-210 - [c7]Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh:
Detecting structural changes in dependent data. GlobalSIP 2017: 750-754 - [c6]Jie Ding, Jiawei Zhou, Vahid Tarokh:
Optimal prediction of data with unknown abrupt change points. GlobalSIP 2017: 928-932 - [c5]Zhun Deng, Jie Ding, Kathryn Heal, Vahid Tarokh:
The number of independent sets in hexagonal graphs. ISIT 2017: 2910-2914 - 2015
- [c4]Jie Ding, Mohammad Noshad, Vahid Tarokh:
Data-driven learning of the number of states in multi-state autoregressive models. Allerton 2015: 418-425 - [c3]Jie Ding, Mohammad Noshad, Vahid Tarokh:
Order Selection of Autoregressive Processes Using Bridge Criterion. ICDM Workshops 2015: 615-622 - [c2]Jie Ding, Mohammad Noshad, Vahid Tarokh:
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. ICDM Workshops 2015: 1441-1446 - [c1]Mohammad Noshad, Jie Ding, Vahid Tarokh:
Sequential learning of multi-state autoregressive time series. RACS 2015: 44-51 - [i3]Jie Ding, Mohammad Noshad, Vahid Tarokh:
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models. CoRR abs/1506.02107 (2015) - [i2]Jie Ding, Mohammad Noshad, Vahid Tarokh:
Complementary Lattice Arrays for Coded Aperture Imaging. CoRR abs/1506.02160 (2015) - [i1]Jie Ding, Abdelmadjid Bouabdallah, Vahid Tarokh:
Key Pre-Distributions From Graph-Based Block Designs. CoRR abs/1506.07466 (2015)
Coauthor Index
[j19] [c45] [c42] [c41] [c35] [i44] [i43] [j15] [c34] [c33] [c32] [c29] [c28] [i37] [i36] [i34] [j10] [j9] [c26] [c25] [c23] [i33] [i32] [i26] [i25] [c19] [c18] [c17] [c16] [c15] [c14] [i23] [i20] [i14] [i12] [j8] [j7] [c12] [c11] [i11] [i10] [i9] [i8] [i7] [i6] [j6] [j5] [j4] [j3] [c10] [c9] [i4] [j2] [j1] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1] [i3] [i2] [i1]
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