Data-driven learning of the number of states in multi-state autoregressive models. - Jie Ding 002; Mohammad Noshad; Vahid Tarokh (Allerton, 2015)

Order Selection of Autoregressive Processes Using Bridge Criterion. - Jie Ding 002; Mohammad Noshad; Vahid Tarokh (ICDM Workshops, 2015)

Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. - Jie Ding 002; Mohammad Noshad; Vahid Tarokh (ICDM Workshops, 2015)

Sequential learning of multi-state autoregressive time series. - Mohammad Noshad; Jie Ding 002; Vahid Tarokh (RACS, 2015)

Key Pre-Distributions From Graph-Based Block Designs. - Jie Ding 002; Abdelmadjid Bouabdallah; Vahid Tarokh (CoRR, 2015)

Complementary Lattice Arrays for Coded Aperture Imaging. - Jie Ding 002; Mohammad Noshad; Vahid Tarokh (CoRR, 2015)

Detecting structural changes in dependent data. - Jie Ding 002; Yu Xiang 004; Lu Shen; Vahid Tarokh (GlobalSIP, 2017)

Optimal prediction of data with unknown abrupt change points. - Jie Ding 002; Jiawei Zhou; Vahid Tarokh (GlobalSIP, 2017)

Modeling nonlinearity in multi-dimensional dependent data. - Qiuyi Han; Jie Ding 002; Edoardo M. Airoldi; Vahid Tarokh (GlobalSIP, 2017)

The number of independent sets in hexagonal graphs. - Zhun Deng; Jie Ding 002; Kathryn Heal; Vahid Tarokh (ISIT, 2017)

Multiple Change Point Analysis: Fast Implementation and Strong Consistency. - Jie Ding 002; Yu Xiang 004; Lu Shen; Vahid Tarokh (IEEE Trans. Signal Process., 2017)

SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. - Qiuyi Han; Jie Ding 002; Edoardo M. Airoldi; Vahid Tarokh (IEEE Trans. Signal Process., 2017)

A Penalized Method for the Predictive Limit of Learning. - Jie Ding 002; Enmao Diao; Jiawei Zhou; Vahid Tarokh (ICASSP, 2018)

Evolutionary Spectra Based on the Multitaper Method with Application To Stationarity Test. - Yu Xiang 004; Jie Ding 002; Vahid Tarokh (ICASSP, 2018)

Model Selection Techniques: An Overview. - Jie Ding 002; Vahid Tarokh; Yuhong Yang 002 (IEEE Signal Process. Mag., 2018)

Online Learning for Multimodal Data Fusion With Application to Object Recognition. - Shahin Shahrampour; Mohammad Noshad; Jie Ding 002; Vahid Tarokh (IEEE Trans. Circuits Syst. II Express Briefs, 2018)

Bridging AIC and BIC: A New Criterion for Autoregression. - Jie Ding 002; Vahid Tarokh; Jing-Yu Yang (IEEE Trans. Inf. Theory, 2018)

Analysis of Multistate Autoregressive Models. - Jie Ding 002; Shahin Shahrampour; Kathryn Heal; Vahid Tarokh (IEEE Trans. Signal Process., 2018)

Restricted Recurrent Neural Networks. - Enmao Diao; Jie Ding 002; Vahid Tarokh (IEEE BigData, 2019)

Gradient Information for Representation and Modeling. - Jie Ding 002; A. Robert Calderbank; Vahid Tarokh (NeurIPS, 2019)

Distributed Lossy Image Compression with Recurrent Networks. - Enmao Diao; Jie Ding 002; Vahid Tarokh (CoRR, 2019)

Perception-Distortion Trade-off with Restricted Boltzmann Machines. - Chris Cannella; Jie Ding 002; Mohammadreza Soltani; Vahid Tarokh (CoRR, 2019)

A Binary Regression Adaptive Goodness-of-fit Test (BAGofT). - Jiawei Zhang 007; Jie Ding 002; Yuhong Yang 002 (CoRR, 2019)

Asymptotically Optimal Prediction for Time-Varying Data Generating Processes. - Jie Ding 002; Jiawei Zhou; Vahid Tarokh (IEEE Trans. Inf. Theory, 2019)

Estimation of the Evolutionary Spectra With Application to Stationarity Test. - Yu Xiang 004; Jie Ding 002; Vahid Tarokh (IEEE Trans. Signal Process., 2019)

"To Tell You the Truth" by Interval-Private Data. - Jie Ding 002; Bangjun Ding (IEEE BigData, 2020)

Forecasting with Multiple Seasonality. - Tianyang Xie; Jie Ding 002 (IEEE BigData, 2020)

DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. - Enmao Diao; Jie Ding 002; Vahid Tarokh (DCC, 2020)

Deep Clustering of Compressed Variational Embeddings. - Suya Wu; Enmao Diao; Jie Ding 002; Vahid Tarokh (DCC, 2020)

Perception-Distortion Trade-Off with Restricted Boltzmann Machines. - Chris Cannella; Jie Ding 002; Mohammadreza Soltani; Yi Zhou 017; Vahid Tarokh (ICASSP, 2020)

Supervised Encoding for Discrete Representation Learning. - Cat P. Le; Yi Zhou 017; Jie Ding 002; Vahid Tarokh (ICASSP, 2020)

Speech Emotion Recognition with Dual-Sequence LSTM Architecture. - Jianyou Wang; Michael Xue; Ryan Culhane; Enmao Diao; Jie Ding 002; Vahid Tarokh (ICASSP, 2020)

On the Information of Feature Maps and Pruning of Deep Neural Networks. - Mohammadreza Soltani; Suya Wu; Jie Ding 002; Robert J. Ravier; Vahid Tarokh (ICPR, 2020)

Assisted Learning: A Framework for Multi-Organization Learning. - Xun Xian; Xinran Wang; Jie Ding 002; Reza Ghanadan (NeurIPS, 2020)

Assisted Learning and Imitation Privacy. - Xun Xian; Xinran Wang; Jie Ding 002; Reza Ghanadan (CoRR, 2020)

Imitation Privacy. - Xun Xian; Xinran Wang; Mingyi Hong 001; Jie Ding 002; Reza Ghanadan (CoRR, 2020)

Large Deviation Principle for the Whittaker 2d Growth Model. - Jun Gao; Jie Ding 002 (CoRR, 2020)

The Efficacy of L1s Regularization in Two-Layer Neural Networks. - Gen Li 005; Yuantao Gu; Jie Ding 002 (CoRR, 2020)

ASCII: ASsisted Classification with Ignorance Interchange. - Jiaying Zhou; Xun Xian; Na Li 002; Jie Ding 002 (CoRR, 2020)

Fisher Auto-Encoders. - Khalil Elkhalil; Ali Hasan; Jie Ding 002; Sina Farsiu; Vahid Tarokh (AISTATS, 2021)

Compressing Deep Networks Using Fisher Score of Feature Maps. - Mohammadreza Soltani; Suya Wu; Yuerong Li; Robert J. Ravier; Jie Ding 002; Vahid Tarokh (DCC, 2021)

Assisted Learning: Cooperative AI with Autonomy. - Jiaying Zhou; Xun Xian; Na Li 002; Jie Ding 002 (ICASSP, 2021)

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. - Enmao Diao; Jie Ding 002; Vahid Tarokh (ICLR, 2021)

Information Laundering for Model Privacy. - Xinran Wang; Yu Xiang 004; Jun Gao; Jie Ding 002 (ICLR, 2021)

Hybrid Control Synthesis for Turing Instability and Hopf Bifurcation of Marine Planktonic Ecosystems With Diffusion. - Yunxiang Lu; Min Xiao 001; Jinling Liang; Jie Ding 002; Ying Zhou; Youhong Wan; Chunxia Fan (IEEE Access, 2021)

Gradient Assisted Learning. - Enmao Diao; Jie Ding 002; Vahid Tarokh (CoRR, 2021)

SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. - Enmao Diao; Jie Ding 002; Vahid Tarokh (CoRR, 2021)

Interval Privacy: A Framework for Data Collection. - Jie Ding 002; Bangjun Ding (CoRR, 2021)

The Rate of Convergence of Variation-Constrained Deep Neural Networks. - Gen Li 005; Yuantao Gu; Jie Ding 002 (CoRR, 2021)

Subset Privacy: Draw from an Obfuscated Urn. - Ganghua Wang; Jie Ding 002 (CoRR, 2021)

Targeted Cross-Validation. - Jiawei Zhang 007; Jie Ding 002; Yuhong Yang 002 (CoRR, 2021)

Assisted Learning for Organizations with Limited Data. - Cheng Chen; Jiaying Zhou; Jie Ding 002; Yi Zhou 017 (CoRR, 2021)

Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders. - Enmao Diao; Vahid Tarokh; Jie Ding 002 (CoRR, 2021)

Characteristic Neural Ordinary Differential Equations. - Xingzi Xu; Ali Hasan; Khalil Elkhalil; Jie Ding 002; Vahid Tarokh (CoRR, 2021)

SPIDER: Searching Personalized Neural Architecture for Federated Learning. - Erum Mushtaq; Chaoyang He 001; Jie Ding 002; Salman Avestimehr (CoRR, 2021)

Model Linkage Selection for Cooperative Learning. - Jiaying Zhou; Jie Ding 002; Kean Ming Tan; Vahid Tarokh (J. Mach. Learn. Res., 2021)

On Statistical Efficiency in Learning. - Jie Ding 002; Enmao Diao; Jiawei Zhou; Vahid Tarokh (IEEE Trans. Inf. Theory, 2021)

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. - Qi Le; Enmao Diao; Xinran Wang; Ali Anwar 001; Vahid Tarokh; Jie Ding 002 (IEEECONF, 2022)

Multimodal Controller for Generative Models. - Enmao Diao; Jie Ding 002; Vahid Tarokh (CVMI, 2022)

On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections. - Mohammadreza Soltani; Suya Wu; Yuerong Li; Jie Ding 002; Vahid Tarokh (DCC, 2022)

Federated Learning Challenges and Opportunities: An Outlook. - Jie Ding 002; Eric W. Tramel; Anit Kumar Sahu; Shuang Wu; Salman Avestimehr; Tao Zhang (ICASSP, 2022)

Mismatched Supervised Learning. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (ICASSP, 2022)

What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened. - Erum Mushtaq; Jie Ding 002; Salman Avestimehr (ICMLA, 2022)

Self-Aware Personalized Federated Learning. - Huili Chen; Jie Ding 002; Eric W. Tramel; Shuang Wu; Anit Kumar Sahu; Salman Avestimehr; Tao Zhang (NeurIPS, 2022)

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. - Enmao Diao; Jie Ding 002; Vahid Tarokh (NeurIPS, 2022)

SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. - Enmao Diao; Jie Ding 002; Vahid Tarokh (NeurIPS, 2022)

Understanding Model Extraction Games. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (TPS-ISA, 2022)

Score-Based Hypothesis Testing for Unnormalized Models. - Suya Wu; Enmao Diao; Khalil Elkhalil; Jie Ding 002; Vahid Tarokh (IEEE Access, 2022)

A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation. - Wenjing Yang 003; Ganghua Wang; Enmao Diao; Vahid Tarokh; Jie Ding 002; Yuhong Yang 002 (CoRR, 2022)

A Framework for Understanding Model Extraction Attack and Defense. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (CoRR, 2022)

$ ell _1$ Regularization in Two-Layer Neural Networks. - Gen Li 005; Yuantao Gu; Jie Ding 002 (IEEE Signal Process. Lett., 2022)

Interval Privacy: A Framework for Privacy-Preserving Data Collection. - Jie Ding 002; Bangjun Ding (IEEE Trans. Signal Process., 2022)

Parallel Assisted Learning. - Xinran Wang; Jiawei Zhang 007; Mingyi Hong 001; Yuhong Yang 002; Jie Ding 002 (IEEE Trans. Signal Process., 2022)

Score-based Quickest Change Detection for Unnormalized Models. - Suya Wu; Enmao Diao; Taposh Banerjee; Jie Ding 002; Vahid Tarokh (AISTATS, 2023)

Exploring Gradient Oscillation in Deep Neural Network Training. - Chedi Morchdi; Yi Zhou 017; Jie Ding 002; Bei Wang 001 (Allerton, 2023)

Quantifying Catastrophic Forgetting in Continual Federated Learning. - Christophe Dupuy; Jimit Majmudar; Jixuan Wang; Tanya G. Roosta; Rahul Gupta 001; Clement Chung; Jie Ding 002; Salman Avestimehr (ICASSP, 2023)

Pruning Deep Neural Networks from a Sparsity Perspective. - Enmao Diao; Ganghua Wang; Jiawei Zhang 007; Yuhong Yang 002; Jie Ding 002; Vahid Tarokh (ICLR, 2023)

Characteristic Neural Ordinary Differential Equation. - Xingzi Xu; Ali Hasan; Khalil Elkhalil; Jie Ding 002; Vahid Tarokh (ICLR, 2023)

Semi-Supervised Federated Learning for Keyword Spotting. - Enmao Diao; Eric W. Tramel; Jie Ding 002; Tao Zhang (ICME Workshops, 2023)

Understanding Backdoor Attacks through the Adaptability Hypothesis. - Xun Xian; Ganghua Wang; Jayanth Srinivasa; Ashish Kundu; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (ICML, 2023)

Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. - Erum Mushtaq; Yavuz Faruk Bakman; Jie Ding 002; Salman Avestimehr (ISBI, 2023)

Assisted Unsupervised Domain Adaptation. - Cheng Chen; Jiawei Zhang 007; Jie Ding 002; Yi Zhou 017 (ISIT, 2023)

A Unified Detection Framework for Inference-Stage Backdoor Defenses. - Xun Xian; Ganghua Wang; Jayanth Srinivasa; Ashish Kundu; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (NeurIPS, 2023)

Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. - Youjia Zhou; Yi Zhou 017; Jie Ding 002; Bei Wang 001 (TAG-ML, 2023)

Robust Quickest Change Detection for Unnormalized Models. - Suya Wu; Enmao Diao; Jie Ding 002; Taposh Banerjee; Vahid Tarokh (UAI, 2023)

PI-FL: Personalized and Incentivized Federated Learning. - Ahmad Faraz Khan 001; Xinran Wang; Qi Le; Azal Ahmad Khan; Haider Ali; Jie Ding 002; Ali Raza Butt; Ali Anwar 001 (CoRR, 2023)

A Framework for Incentivized Collaborative Learning. - Xinran Wang; Qi Le; Ahmad Faraz Khan 001; Jie Ding 002; Ali Anwar 001 (CoRR, 2023)

Meta Clustering for Collaborative Learning. - Chenglong Ye; Reza Ghanadan; Jie Ding 002 (J. Comput. Graph. Stat., 2023)

Assisted Learning for Organizations with Limited Imbalanced Data. - Cheng Chen; Jiaying Zhou; Jie Ding 002; Yi Zhou 017 (Trans. Mach. Learn. Res., 2023)

Distributed Architecture Search Over Heterogeneous Distributions. - Erum Mushtaq; Chaoyang He 001; Jie Ding 002; Salman Avestimehr (Trans. Mach. Learn. Res., 2023)

Towards Understanding Variation-Constrained Deep Neural Networks. - Gen Li 005; Jie Ding 002 (IEEE Trans. Signal Process., 2023)

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. - Gen Li 005; Ganghua Wang; Jie Ding 002 (IEEE Trans. Inf. Theory, 2023)

DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. - Qi Le; Enmao Diao; Xinran Wang; Ahmad Faraz Khan 001; Vahid Tarokh; Jie Ding 002; Ali Anwar 001 (IEEE Big Data, 2024)

ICL: An Incentivized Collaborative Learning Framework. - Xinran Wang; Qi Le; Ahmad Faraz Khan 001; Jie Ding 002; Ali Anwar 001 (IEEE Big Data, 2024)

CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning. - Erum Mushtaq; Duygu Nur Yaldiz; Yavuz Faruk Bakman; Jie Ding 002; Chenyang Tao; Dimitrios Dimitriadis; Salman Avestimehr (ECCV (80), 2024)

Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. - Ganghua Wang; Xun Xian; Ashish Kundu; Jayanth Srinivasa; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (ICLR, 2024)

Building Large Models from Small Distributed Models: A Layer Matching Approach. - Xinwei Zhang 001; Bingqing Song; Mehrdad Honarkhah; Jie Ding 002; Mingyi Hong 001 (SAM, 2024)

Unraveling the Gradient Descent Dynamics of Transformers. - Bingqing Song; Boran Han; Shuai Zhang 007; Jie Ding 002; Mingyi Hong 001 (NeurIPS, 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 001; Jie Ding 002 (NeurIPS, 2024)

SHARE: A Distributed Learning Framework For Multivariate Time-Series Forecasting. - Wei Ye 009; Prashant Khanduri; Jiangweizhi Peng; Feng Tian; Jun Gao; Jie Ding 002; Zhi-Li Zhang; Mingyi Hong 001 (SPAWC, 2024)

Base Models for Parabolic Partial Differential Equations. - Xingzi Xu; Ali Hasan; Jie Ding 002; Vahid Tarokh (UAI, 2024)

ColA: Collaborative Adaptation with Gradient Learning. - Enmao Diao; Qi Le; Suya Wu; Xinran Wang; Ali Anwar 001; Jie Ding 002; Vahid Tarokh (CoRR, 2024)

Additive-Effect Assisted Learning. - Jiawei Zhang 007; Yuhong Yang 002; Jie Ding 002 (CoRR, 2024)

DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. - Qi Le; Enmao Diao; Xinran Wang; Vahid Tarokh; Jie Ding 002; Ali Anwar 001 (CoRR, 2024)

Drift to Remember. - Jin Du; Xinhe Zhang; Hao Shen; Xun Xian; Ganghua Wang; Jiawei Zhang 007; Yuhong Yang 002; Na Li 002; Jia Liu 002; Jie Ding 002 (CoRR, 2024)

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. - Xun Xian; Ganghua Wang; Xuan Bi; Jayanth Srinivasa; Ashish Kundu; Charles Fleming; Mingyi Hong 001; Jie Ding 002 (CoRR, 2024)

Quickest Change Detection for Unnormalized Statistical Models. - Suya Wu; Enmao Diao; Taposh Banerjee; Jie Ding 002; Vahid Tarokh (IEEE Trans. Inf. Theory, 2024)

Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing. - Qi Le; Enmao Diao; Ziyan Wang; Xinran Wang; Jie Ding 002; Li Yang; Ali Anwar 001 (ICLR, 2025)

MAP: Multi-Human-Value Alignment Palette. - Xinran Wang; Qi Le; Ammar Ahmed; Enmao Diao; Yi Zhou 015; Nathalie Baracaldo; Jie Ding 002; Ali Anwar 001 (ICLR, 2025)

IP-FL: Incentive-Driven Personalization in Federated Learning. - Ahmad Faraz Khan 001; Xinran Wang; Qi Le; Zain ul Abdeen; Azal Ahmad Khan; Haider Ali; Ming Jin 002; Jie Ding 002; Ali Raza Butt; Ali Anwar 001 (IPDPS, 2025)

AID: Adaptive Integration of Detectors for Safe AI with Language Models. - Xinran Wang; Enmao Diao; Qi Le; Jie Ding 002; Ali Anwar 001 (NAACL (Long Papers), 2025)

Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses. - Ganghua Wang; Yuhong Yang 002; Jie Ding 002 (CoRR, 2025)

Safety Aware Task Planning via Large Language Models in Robotics. - Azal Ahmad Khan; Michael Andrev; Muhammad Ali Murtaza; Sergio Aguilera; Rui Zhang 028; Jie Ding 002; Seth Hutchinson 001; Ali Anwar 001 (CoRR, 2025)

Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference. - Jin Du; Li Chen; Xun Xian; An Luo; Fangqiao Tian; Ganghua Wang; Charles Doss; Xiaotong Shen; Jie Ding 002 (CoRR, 2025)

An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems. - Fangqiao Tian; An Luo; Jin Du; Xun Xian; Robert Specht; Ganghua Wang; Xuan Bi; Jiawei Zhou; Jayanth Srinivasa; Ashish Kundu; Charles Fleming; Rui Zhang 028; et al. (CoRR, 2025)

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science. - An Luo; Xun Xian; Jin Du; Fangqiao Tian; Ganghua Wang; Ming Zhong; Shengchun Zhao; Xuan Bi; Zirui Liu 001; Jiawei Zhou; Jayanth Srinivasa; Ashish Kundu; et al. (CoRR, 2025)

Robust Score-Based Quickest Change Detection. - Sean Moushegian; Suya Wu; Enmao Diao; Jie Ding 002; Taposh Banerjee; Vahid Tarokh (IEEE Trans. Inf. Theory, 2025)