default search action
Jie 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
— Huazhong University of Science and Technology, School of Electronic Information and Communications, Wuhan, China (and 1 more)
- Jie Ding 0003
- Jie Ding 0004
- Jie Ding 0006
- Jie Ding 0007
- Jie Ding 0008
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j21]Suya Wu
Quickest Change Detection for Unnormalized Statistical Models. IEEE Trans. Inf. Theory 70(2): 1220-1232 (2024) - [c51]Erum Mushtaq, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Jie Ding
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning. ECCV (80) 2024: 311-328 - [c50]Ganghua Wang, Xun Xian, Ashish Kundu, Jayanth Srinivasa, Xuan Bi, Mingyi Hong, Jie Ding:
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. ICLR 2024 - [c49]Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong:
SHARE: A Distributed Learning Framework For Multivariate Time-Series Forecasting. SPAWC 2024: 76-80 - [i56]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) - [i55]Enmao Diao, Qi Le, Suya Wu, Xinran Wang, Ali Anwar, Jie Ding, Vahid Tarokh:
ColA: Collaborative Adaptation with Gradient Learning. CoRR abs/2404.13844 (2024) - [i54]Jiawei Zhang, Yuhong Yang, Jie Ding:
Additive-Effect Assisted Learning. CoRR abs/2405.08235 (2024) - [i53]Erum Mushtaq, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Jie Ding, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr:
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning. CoRR abs/2407.12188 (2024) - [i52]Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh:
Base Models for Parabolic Partial Differential Equations. CoRR abs/2407.12234 (2024) - [i51]Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar:
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. CoRR abs/2409.04986 (2024) - [i50]Jin Du, Xinhe Zhang, Hao Shen, Xun Xian, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Na Li, Jia Liu, Jie Ding:
Drift to Remember. CoRR abs/2409.13997 (2024) - [i49]Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding:
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. CoRR abs/2409.17275 (2024) - [i48]Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar:
MAP: Multi-Human-Value Alignment Palette. CoRR abs/2410.19198 (2024) - 2023
- [j20]Chenglong Ye, Reza Ghanadan, Jie Ding:
Meta Clustering for Collaborative Learning. J. Comput. Graph. Stat. 32(3): 1160-1169 (2023) - [j19]Gen Li, Ganghua Wang
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. IEEE Trans. Inf. Theory 69(9): 5921-5935 (2023) - [j18]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. 2023 (2023) - [j17]Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr:
Distributed Architecture Search Over Heterogeneous Distributions. 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) - [c48]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Score-based Quickest Change Detection for Unnormalized Models. AISTATS 2023: 10546-10565 - [c47]Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang:
Exploring Gradient Oscillation in Deep Neural Network Training. Allerton 2023: 1-7 - [c46]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 - [c45]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. ICLR 2023 - [c44]Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equation. ICLR 2023 - [c43]Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang:
Semi-Supervised Federated Learning for Keyword Spotting. ICME Workshops 2023: 466-469 - [c42]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 - [c41]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 - [c40]Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou:
Assisted Unsupervised Domain Adaptation. ISIT 2023: 2482-2487 - [c39]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 - [c38]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 - [c37]Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh:
Robust Quickest Change Detection for Unnormalized Models. UAI 2023: 2314-2323 - [i47]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Quickest Change Detection for Unnormalized Statistical Models. CoRR abs/2302.00250 (2023) - [i46]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) - [i45]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) - [i44]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) - [i43]Gen Li, Ganghua Wang, Jie Ding:
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. CoRR abs/2305.04267 (2023) - [i42]Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang:
Semi-Supervised Federated Learning for Keyword Spotting. CoRR abs/2305.05110 (2023) - [i41]Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar:
A Framework for Incentivized Collaborative Learning. CoRR abs/2305.17052 (2023) - [i40]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
Score-Based Hypothesis Testing for Unnormalized Models. IEEE Access 10: 71936-71950 (2022) - [j14]Gen Li, Yuantao Gu
$\ell _1$ Regularization in Two-Layer Neural Networks. IEEE Signal Process. Lett. 29: 135-139 (2022) - [j13]Jie Ding
Interval Privacy: A Framework for Privacy-Preserving Data Collection. IEEE Trans. Signal Process. 70: 2443-2459 (2022) - [j12]Xinran Wang
Parallel Assisted Learning. IEEE Trans. Signal Process. 70: 5848-5858 (2022) - [c36]Qi Le, Enmao Diao, Xinran Wang, Ali Anwar
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. IEEECONF 2022: 1157-1163 - [c35]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CVMI 2022: 109-121 - [c34]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 - [c33]Xun Xian, Mingyi Hong, Jie Ding:
Mismatched Supervised Learning. ICASSP 2022: 4228-4232 - [c32]Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang:
Federated Learning Challenges and Opportunities: An Outlook. ICASSP 2022: 8752-8756 - [c31]Erum Mushtaq, Jie Ding, Salman Avestimehr:
What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened. ICMLA 2022: 1740-1747 - [c30]Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang:
Self-Aware Personalized Federated Learning. NeurIPS 2022 - [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 - [i39]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) - [i38]Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang:
Federated Learning Challenges and Opportunities: An Outlook. CoRR abs/2202.00807 (2022) - [i37]Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang:
Self-Aware Personalized Federated Learning. CoRR abs/2204.08069 (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
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. CoRR abs/2212.08779 (2022) - 2021
- [j11]Yunxiang Lu
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
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
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC 2020: 3-12 - [c18]Suya Wu, Enmao Diao
Deep Clustering of Compressed Variational Embeddings. DCC 2020: 399 - [c17]Cat P. Le
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
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
Asymptotically Optimal Prediction for Time-Varying Data Generating Processes. IEEE Trans. Inf. Theory 65(5): 3034-3067 (2019) - [j7]Yu Xiang
Estimation of the Evolutionary Spectra With Application to Stationarity Test. IEEE Trans. Signal Process. 67(5): 1353-1365 (2019) - [c12]Enmao Diao
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
Deep Clustering of Compressed Variational Embeddings. CoRR abs/1910.10341 (2019) - [i6]Cat P. Le
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
Model Selection Techniques: An Overview. IEEE Signal Process. Mag. 35(6): 16-34 (2018) - [j5]Shahin Shahrampour
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
Bridging AIC and BIC: A New Criterion for Autoregression. IEEE Trans. Inf. Theory 64(6): 4024-4043 (2018) - [j3]Jie Ding
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
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
Amir Salman Avestimehr
aka: Salman Avestimehr
aka: Salman Avestimehr
[j21] [i55] [i52] [i51] [c48] [c45] [c44] [c37] [i47] [i46] [j15] [c36] [c35] [c34] [c29] [c28] [i39] [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]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-03 20:29 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: