Jie Ding - RankMe

154 papers A* 17A 6B 9C 1Misc 9Journal 88Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science. CoRR An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding
2025	—	`conf`	AID: Adaptive Integration of Detectors for Safe AI with Language Models. NAACL (Long Papers) Xinran Wang, Enmao Diao, Qi Le, Jie Ding, Ali Anwar
2025	J	`jnl`	An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems. CoRR Fangqiao Tian, An Luo, Jin Du, Xun Xian, Robert Specht, Ganghua Wang, Xuan Bi, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Rui Zhang, Zirui Liu, Mingyi Hong, Jie Ding
2025	—	`conf`	AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science. EMNLP (Findings) An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
2025	J	`jnl`	AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science. CoRR An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
2025	—	`conf`	Can Agentic AI Match the Performance of Human Data Scientists? CAMSAP An Luo, Jin Du, Fangqiao Tian, Xun Xian, Robert Specht, Ganghua Wang, Xuan Bi, Charles Fleming, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding
2025	J	`jnl`	Can Agentic AI Match the Performance of Human Data Scientists? CoRR An Luo, Jin Du, Fangqiao Tian, Xun Xian, Robert Specht, Ganghua Wang, Xuan Bi, Charles Fleming, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding
2025	J	`jnl`	Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction. Trans. Mach. Learn. Res. Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong
2025	—	`conf`	DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains. EMNLP (Findings) Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang
2025	A	`conf`	IP-FL: Incentive-Driven Personalization in Federated Learning. IPDPS Ahmad Faraz Khan, Xinran Wang, Qi Le, Zain ul Abdeen, Azal Ahmad Khan, Haider Ali, Ming Jin, Jie Ding, Ali Raza Butt, Ali Anwar
2025	J	`jnl`	Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference. CoRR Jin Du, Li Chen, Xun Xian, An Luo, Fangqiao Tian, Ganghua Wang, Charles Doss, Xiaotong Shen, Jie Ding
2025	A*	`conf`	MAP: Multi-Human-Value Alignment Palette. ICLR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2025	J	`jnl`	Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses. CoRR Ganghua Wang, Yuhong Yang, Jie Ding
2025	A*	`conf`	On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. ICML Xun Xian, Ganghua Wang, Xuan Bi, Rui Zhang, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
2025	A*	`conf`	Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing. ICLR Qi Le, Enmao Diao, Ziyan Wang, Xinran Wang, Jie Ding, Li Yang, Ali Anwar
2025	J	`jnl`	Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing. CoRR Qi Le, Enmao Diao, Ziyan Wang, Xinran Wang, Jie Ding, Li Yang, Ali Anwar
2025	J	`jnl`	Robust Score-Based Quickest Change Detection. IEEE Trans. Inf. Theory Sean Moushegian, Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh
2025	A	`conf`	Safety Aware Task Planning via Large Language Models in Robotics. IROS Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza, Sergio Aguilera, Rui Zhang, Jie Ding, Seth Hutchinson, Ali Anwar
2025	J	`jnl`	Safety Aware Task Planning via Large Language Models in Robotics. CoRR Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza, Sergio Aguilera, Rui Zhang, Jie Ding, Seth Hutchinson, Ali Anwar
2024	J	`jnl`	Additive-Effect Assisted Learning. CoRR Jiawei Zhang, Yuhong Yang, Jie Ding
2024	A	`conf`	Base Models for Parabolic Partial Differential Equations. UAI Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh
2024	J	`jnl`	Base Models for Parabolic Partial Differential Equations. CoRR Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh
2024	—	`conf`	Building Large Models from Small Distributed Models: A Layer Matching Approach. SAM Xinwei Zhang, Bingqing Song, Mehrdad Honarkhah, Jie Ding, Mingyi Hong
2024	J	`jnl`	ColA: Collaborative Adaptation with Gradient Learning. CoRR Enmao Diao, Qi Le, Suya Wu, Xinran Wang, Ali Anwar, Jie Ding, Vahid Tarokh
2024	—	`conf`	CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning. ECCV (80) Erum Mushtaq, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Jie Ding, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
2024	J	`jnl`	CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning. CoRR Erum Mushtaq, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Jie Ding, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
2024	A*	`conf`	Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. ICLR Ganghua Wang, Xun Xian, Ashish Kundu, Jayanth Srinivasa, Xuan Bi, Mingyi Hong, Jie Ding
2024	J	`jnl`	Drift to Remember. CoRR Jin Du, Xinhe Zhang, Hao Shen, Xun Xian, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Na Li, Jia Liu, Jie Ding
2024	B	`conf`	DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. IEEE Big Data Qi Le, Enmao Diao, Xinran Wang, Ahmad Faraz Khan, Vahid Tarokh, Jie Ding, Ali Anwar
2024	J	`jnl`	DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. CoRR Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar
2024	B	`conf`	ICL: An Incentivized Collaborative Learning Framework. IEEE Big Data Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar
2024	J	`jnl`	MAP: Multi-Human-Value Alignment Palette. CoRR Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
2024	J	`jnl`	On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. CoRR Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
2024	J	`jnl`	Quickest Change Detection for Unnormalized Statistical Models. IEEE Trans. Inf. Theory Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh
2024	A*	`conf`	RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees. NeurIPS Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding
2024	J	`jnl`	RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees. CoRR Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding
2024	—	`conf`	SHARE: A Distributed Learning Framework For Multivariate Time-Series Forecasting. SPAWC Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong
2024	A*	`conf`	Unraveling the Gradient Descent Dynamics of Transformers. NeurIPS Bingqing Song, Boran Han, Shuai Zhang, Jie Ding, Mingyi Hong
2024	J	`jnl`	Unraveling the Gradient Descent Dynamics of Transformers. CoRR Bingqing Song, Boran Han, Shuai Zhang, Jie Ding, Mingyi Hong
2023	J	`jnl`	A Framework for Incentivized Collaborative Learning. CoRR Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar
2023	A*	`conf`	A Unified Detection Framework for Inference-Stage Backdoor Defenses. NeurIPS Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding
2023	J	`jnl`	Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou
2023	B	`conf`	Assisted Unsupervised Domain Adaptation. ISIT Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou
2023	A*	`conf`	Characteristic Neural Ordinary Differential Equation. ICLR Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh
2023	J	`jnl`	Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. CoRR Ganghua Wang, Xun Xian, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding
2023	J	`jnl`	Distributed Architecture Search Over Heterogeneous Distributions. Trans. Mach. Learn. Res. Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr
2023	—	`conf`	Exploring Gradient Oscillation in Deep Neural Network Training. Allerton Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang
2023	J	`jnl`	Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. CoRR Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr
2023	—	`conf`	Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging. ISBI Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr
2023	J	`jnl`	Meta Clustering for Collaborative Learning. J. Comput. Graph. Stat. Chenglong Ye, Reza Ghanadan, Jie Ding
2023	J	`jnl`	PI-FL: Personalized and Incentivized Federated Learning. CoRR Ahmad Faraz Khan, Xinran Wang, Qi Le, Azal Ahmad Khan, Haider Ali, Jie Ding, Ali Raza Butt, Ali Anwar
2023	J	`jnl`	Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. CoRR Gen Li, Ganghua Wang, Jie Ding
2023	J	`jnl`	Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. IEEE Trans. Inf. Theory Gen Li, Ganghua Wang, Jie Ding
2023	A*	`conf`	Pruning Deep Neural Networks from a Sparsity Perspective. ICLR Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh
2023	J	`jnl`	Pruning Deep Neural Networks from a Sparsity Perspective. CoRR Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh
2023	Misc	`conf`	Quantifying Catastrophic Forgetting in Continual Federated Learning. ICASSP Christophe Dupuy, Jimit Majmudar, Jixuan Wang, Tanya G. Roosta, Rahul Gupta, Clement Chung, Jie Ding, Salman Avestimehr
2023	J	`jnl`	Quickest Change Detection for Unnormalized Statistical Models. CoRR Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh
2023	A	`conf`	Robust Quickest Change Detection for Unnormalized Models. UAI Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh
2023	A	`conf`	Score-based Quickest Change Detection for Unnormalized Models. AISTATS Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh
2023	—	`conf`	Semi-Supervised Federated Learning for Keyword Spotting. ICME Workshops Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang
2023	J	`jnl`	Semi-Supervised Federated Learning for Keyword Spotting. CoRR Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang
2023	J	`jnl`	Towards Understanding Variation-Constrained Deep Neural Networks. IEEE Trans. Signal Process. Gen Li, Jie Ding
2023	A*	`conf`	Understanding Backdoor Attacks through the Adaptability Hypothesis. ICML Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding
2023	—	`conf`	Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang
2022	J	`jnl`	$\ell _1$ Regularization in Two-Layer Neural Networks. IEEE Signal Process. Lett. Gen Li, Yuantao Gu, Jie Ding
2022	J	`jnl`	A Framework for Understanding Model Extraction Attack and Defense. CoRR Xun Xian, Mingyi Hong, Jie Ding
2022	J	`jnl`	A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation. CoRR Wenjing Yang, Ganghua Wang, Enmao Diao, Vahid Tarokh, Jie Ding, Yuhong Yang
2022	Misc	`conf`	Federated Learning Challenges and Opportunities: An Outlook. ICASSP Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang
2022	J	`jnl`	Federated Learning Challenges and Opportunities: An Outlook. CoRR Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang
2022	A*	`conf`	GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. NeurIPS Enmao Diao, Jie Ding, Vahid Tarokh
2022	J	`jnl`	Interval Privacy: A Framework for Privacy-Preserving Data Collection. IEEE Trans. Signal Process. Jie Ding, Bangjun Ding
2022	Misc	`conf`	Mismatched Supervised Learning. ICASSP Xun Xian, Mingyi Hong, Jie Ding
2022	—	`conf`	Multimodal Controller for Generative Models. CVMI Enmao Diao, Jie Ding, Vahid Tarokh
2022	B	`conf`	On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections. DCC Mohammadreza Soltani, Suya Wu, Yuerong Li, Jie Ding, Vahid Tarokh
2022	J	`jnl`	On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections. CoRR Mohammadreza Soltani, Suya Wu, Yuerong Li, Jie Ding, Vahid Tarokh
2022	J	`jnl`	Parallel Assisted Learning. IEEE Trans. Signal Process. Xinran Wang, Jiawei Zhang, Mingyi Hong, Yuhong Yang, Jie Ding
2022	—	`conf`	Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. IEEECONF Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding
2022	J	`jnl`	Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. CoRR Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding
2022	J	`jnl`	Score-Based Hypothesis Testing for Unnormalized Models. IEEE Access Suya Wu, Enmao Diao, Khalil Elkhalil, Jie Ding, Vahid Tarokh
2022	A*	`conf`	Self-Aware Personalized Federated Learning. NeurIPS Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
2022	J	`jnl`	Self-Aware Personalized Federated Learning. CoRR Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
2022	A*	`conf`	SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. NeurIPS Enmao Diao, Jie Ding, Vahid Tarokh
2022	—	`conf`	Understanding Model Extraction Games. TPS-ISA Xun Xian, Mingyi Hong, Jie Ding
2022	C	`conf`	What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened. ICMLA Erum Mushtaq, Jie Ding, Salman Avestimehr
2021	J	`jnl`	Assisted Learning for Organizations with Limited Data. CoRR Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou
2021	Misc	`conf`	Assisted Learning: Cooperative AI with Autonomy. ICASSP Jiaying Zhou, Xun Xian, Na Li, Jie Ding
2021	J	`jnl`	Characteristic Neural Ordinary Differential Equations. CoRR Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh
2021	B	`conf`	Compressing Deep Networks Using Fisher Score of Feature Maps. DCC Mohammadreza Soltani, Suya Wu, Yuerong Li, Robert J. Ravier, Jie Ding, Vahid Tarokh
2021	A	`conf`	Fisher Auto-Encoders. AISTATS Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh
2021	J	`jnl`	Gradient Assisted Learning. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2021	A*	`conf`	HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. ICLR Enmao Diao, Jie Ding, Vahid Tarokh
2021	J	`jnl`	Hybrid Control Synthesis for Turing Instability and Hopf Bifurcation of Marine Planktonic Ecosystems With Diffusion. IEEE Access Yunxiang Lu, Min Xiao, Jinling Liang, Jie Ding, Ying Zhou, Youhong Wan, Chunxia Fan
2021	A*	`conf`	Information Laundering for Model Privacy. ICLR Xinran Wang, Yu Xiang, Jun Gao, Jie Ding
2021	J	`jnl`	Interval Privacy: A Framework for Data Collection. CoRR Jie Ding, Bangjun Ding
2021	J	`jnl`	Model Linkage Selection for Cooperative Learning. J. Mach. Learn. Res. Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh
2021	J	`jnl`	On Statistical Efficiency in Learning. IEEE Trans. Inf. Theory Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh
2021	J	`jnl`	Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders. CoRR Enmao Diao, Vahid Tarokh, Jie Ding
2021	J	`jnl`	SPIDER: Searching Personalized Neural Architecture for Federated Learning. CoRR Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr
2021	J	`jnl`	SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2021	J	`jnl`	Subset Privacy: Draw from an Obfuscated Urn. CoRR Ganghua Wang, Jie Ding
2021	J	`jnl`	Targeted Cross-Validation. CoRR Jiawei Zhang, Jie Ding, Yuhong Yang
2021	J	`jnl`	The Rate of Convergence of Variation-Constrained Deep Neural Networks. CoRR Gen Li, Yuantao Gu, Jie Ding
2020	—	`conf`	"To Tell You the Truth" by Interval-Private Data. IEEE BigData Jie Ding, Bangjun Ding
2020	J	`jnl`	ASCII: ASsisted Classification with Ignorance Interchange. CoRR Jiaying Zhou, Xun Xian, Na Li, Jie Ding
2020	J	`jnl`	Assisted Learning and Imitation Privacy. CoRR Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan
2020	A*	`conf`	Assisted Learning: A Framework for Multi-Organization Learning. NeurIPS Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan
2020	B	`conf`	DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC Enmao Diao, Jie Ding, Vahid Tarokh
2020	B	`conf`	Deep Clustering of Compressed Variational Embeddings. DCC Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh
2020	J	`jnl`	Fisher Auto-Encoders. CoRR Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh
2020	—	`conf`	Forecasting with Multiple Seasonality. IEEE BigData Tianyang Xie, Jie Ding
2020	J	`jnl`	Forecasting with Multiple Seasonality. CoRR Tianyang Xie, Jie Ding
2020	J	`jnl`	HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2020	J	`jnl`	Imitation Privacy. CoRR Xun Xian, Xinran Wang, Mingyi Hong, Jie Ding, Reza Ghanadan
2020	J	`jnl`	Information Laundering for Model Privacy. CoRR Xinran Wang, Yu Xiang, Jun Gao, Jie Ding
2020	J	`jnl`	Large Deviation Principle for the Whittaker 2d Growth Model. CoRR Jun Gao, Jie Ding
2020	J	`jnl`	Meta Clustering for Collaborative Learning. CoRR Chenglong Ye, Jie Ding, Reza Ghanadan
2020	J	`jnl`	Multimodal Controller for Generative Models. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2020	J	`jnl`	On Statistical Efficiency in Learning. CoRR Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh
2020	B	`conf`	On the Information of Feature Maps and Pruning of Deep Neural Networks. ICPR Mohammadreza Soltani, Suya Wu, Jie Ding, Robert J. Ravier, Vahid Tarokh
2020	Misc	`conf`	Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh
2020	Misc	`conf`	Speech Emotion Recognition with Dual-Sequence LSTM Architecture. ICASSP Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh
2020	Misc	`conf`	Supervised Encoding for Discrete Representation Learning. ICASSP Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh
2020	J	`jnl`	The Efficacy of L CoRR Gen Li, Yuantao Gu, Jie Ding
2019	J	`jnl`	A Binary Regression Adaptive Goodness-of-fit Test (BAGofT). CoRR Jiawei Zhang, Jie Ding, Yuhong Yang
2019	J	`jnl`	Asymptotically Optimal Prediction for Time-Varying Data Generating Processes. IEEE Trans. Inf. Theory Jie Ding, Jiawei Zhou, Vahid Tarokh
2019	J	`jnl`	Deep Clustering of Compressed Variational Embeddings. CoRR Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh
2019	J	`jnl`	Distributed Lossy Image Compression with Recurrent Networks. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2019	J	`jnl`	Estimation of the Evolutionary Spectra With Application to Stationarity Test. IEEE Trans. Signal Process. Yu Xiang, Jie Ding, Vahid Tarokh
2019	A*	`conf`	Gradient Information for Representation and Modeling. NeurIPS Jie Ding, A. Robert Calderbank, Vahid Tarokh
2019	J	`jnl`	Perception-Distortion Trade-off with Restricted Boltzmann Machines. CoRR Chris Cannella, Jie Ding, Mohammadreza Soltani, Vahid Tarokh
2019	—	`conf`	Restricted Recurrent Neural Networks. IEEE BigData Enmao Diao, Jie Ding, Vahid Tarokh
2019	J	`jnl`	Restricted Recurrent Neural Networks. CoRR Enmao Diao, Jie Ding, Vahid Tarokh
2019	J	`jnl`	Speech Emotion Recognition with Dual-Sequence LSTM Architecture. CoRR Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh
2019	J	`jnl`	Supervised Encoding for Discrete Representation Learning. CoRR Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh
2018	Misc	`conf`	A Penalized Method for the Predictive Limit of Learning. ICASSP Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh
2018	J	`jnl`	Analysis of Multistate Autoregressive Models. IEEE Trans. Signal Process. Jie Ding, Shahin Shahrampour, Kathryn Heal, Vahid Tarokh
2018	J	`jnl`	Bridging AIC and BIC: A New Criterion for Autoregression. IEEE Trans. Inf. Theory Jie Ding, Vahid Tarokh, Jing-Yu Yang
2018	Misc	`conf`	Evolutionary Spectra Based on the Multitaper Method with Application To Stationarity Test. ICASSP Yu Xiang, Jie Ding, Vahid Tarokh
2018	J	`jnl`	Model Selection Techniques - An Overview. CoRR Jie Ding, Vahid Tarokh, Yuhong Yang
2018	J	`jnl`	Model Selection Techniques: An Overview. IEEE Signal Process. Mag. Jie Ding, Vahid Tarokh, Yuhong Yang
2018	J	`jnl`	Online Learning for Multimodal Data Fusion With Application to Object Recognition. IEEE Trans. Circuits Syst. II Express Briefs Shahin Shahrampour, Mohammad Noshad, Jie Ding, Vahid Tarokh
2017	—	`conf`	Detecting structural changes in dependent data. GlobalSIP Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh
2017	—	`conf`	Modeling nonlinearity in multi-dimensional dependent data. GlobalSIP Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh
2017	J	`jnl`	Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Trans. Signal Process. Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh
2017	—	`conf`	Optimal prediction of data with unknown abrupt change points. GlobalSIP Jie Ding, Jiawei Zhou, Vahid Tarokh
2017	J	`jnl`	SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Trans. Signal Process. Qiuyi Han, Jie Ding, Edoardo M. Airoldi, Vahid Tarokh
2017	B	`conf`	The number of independent sets in hexagonal graphs. ISIT Zhun Deng, Jie Ding, Kathryn Heal, Vahid Tarokh
2015	J	`jnl`	Complementary Lattice Arrays for Coded Aperture Imaging. CoRR Jie Ding, Mohammad Noshad, Vahid Tarokh
2015	J	`jnl`	Data-Driven Learning of the Number of States in Multi-State Autoregressive Models. CoRR Jie Ding, Mohammad Noshad, Vahid Tarokh
2015	—	`conf`	Data-driven learning of the number of states in multi-state autoregressive models. Allerton Jie Ding, Mohammad Noshad, Vahid Tarokh
2015	J	`jnl`	Key Pre-Distributions From Graph-Based Block Designs. CoRR Jie Ding, Abdelmadjid Bouabdallah, Vahid Tarokh
2015	—	`conf`	Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. ICDM Workshops Jie Ding, Mohammad Noshad, Vahid Tarokh
2015	—	`conf`	Order Selection of Autoregressive Processes Using Bridge Criterion. ICDM Workshops Jie Ding, Mohammad Noshad, Vahid Tarokh
2015	—	`conf`	Sequential learning of multi-state autoregressive time series. RACS Mohammad Noshad, Jie Ding, Vahid Tarokh