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ORCIDVahid Tarokh
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- affiliation: Duke University, Durham, NC, USA
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2020 – today
- 2024
[j136]Bowen Li
, Suya Wu, Erin E. Tripp, Ali Pezeshki, Vahid Tarokh:
Recursive Least Squares With Minimax Concave Penalty Regularization for Adaptive System Identification. IEEE Access 12: 66993-67004 (2024)- [j135]Enmao Diao, Taposh Banerjee, Vahid Tarokh:
Large Deviation Analysis of Score-Based Hypothesis Testing. IEEE Access 12: 117691-117700 (2024) - [j134]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) - [c164]Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh:
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes. AISTATS 2024: 262-270 - [c163]Hao-Lun Hsu, Haocheng Meng, Shaocheng Luo, Juncheng Dong, Vahid Tarokh, Miroslav Pajic:
REFORMA: Robust REinFORceMent Learning via Adaptive Adversary for Drones Flying under Disturbances. ICRA 2024: 5169-5175 - [i104]Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh:
Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramér-Rao Bound. CoRR abs/2401.11176 (2024) - [i103]Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh:
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes. CoRR abs/2404.09402 (2024) - [i102]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) - [i101]Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh:
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications. CoRR abs/2406.09638 (2024) - [i100]Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh:
Base Models for Parabolic Partial Differential Equations. CoRR abs/2407.12234 (2024) - [i99]Patrick K. Kuiper, Sirui Lin, Jose H. Blanchet, Vahid Tarokh:
Generative Learning for Simulation of Vehicle Faults. CoRR abs/2407.17654 (2024) - [i98]Patrick K. Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose H. Blanchet, Vahid Tarokh:
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions. CoRR abs/2408.00131 (2024) - 2023
- [j133]Ilya Soloveychik, Vahid Tarokh:
Region selection in Markov random fields: Gaussian case. J. Multivar. Anal. 196: 105178 (2023) - [c162]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Score-based Quickest Change Detection for Unnormalized Models. AISTATS 2023: 10546-10565 - [c161]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. ICLR 2023 - [c160]Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equation. ICLR 2023 - [c159]Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh:
PASTA: Pessimistic Assortment Optimization. ICML 2023: 8276-8295 - [c158]Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh, Min Chi, Miroslav Pajic:
Off-Policy Evaluation for Human Feedback. NeurIPS 2023 - [c157]Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh:
Transfer learning for individual treatment effect estimation. UAI 2023: 56-66 - [c156]Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh:
Inference and sampling of point processes from diffusion excursions. UAI 2023: 839-848 - [c155]Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh:
Robust Quickest Change Detection for Unnormalized Models. UAI 2023: 2314-2323 - [i97]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Quickest Change Detection for Unnormalized Statistical Models. CoRR abs/2302.00250 (2023) - [i96]Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson:
Domain Adaptation via Rebalanced Sub-domain Alignment. CoRR abs/2302.02009 (2023) - [i95]Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh:
PASTA: Pessimistic Assortment Optimization. CoRR abs/2302.03821 (2023) - [i94]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) - [i93]Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh:
Subspace Perturbation Analysis for Data-Driven Radar Target Localization. CoRR abs/2303.08241 (2023) - [i92]Junrong Lin, Mahmudul Hasan, Pinar Acar, José H. Blanchet, Vahid Tarokh:
Neural Network Accelerated Process Design of Polycrystalline Microstructures. CoRR abs/2305.00003 (2023) - [i91]Cat P. Le, Juncheng Dong, Ahmed Aloui, Vahid Tarokh:
Few-Shot Continual Learning for Conditional Generative Adversarial Networks. CoRR abs/2305.11400 (2023) - [i90]Rixi Peng, Juncheng Dong, Jordan M. Malof, Willie J. Padilla, Vahid Tarokh:
Deep Generalized Green's Functions. CoRR abs/2306.02925 (2023) - [i89]Juncheng Dong, Hao-Lun Hsu, Qitong Gao, Vahid Tarokh, Miroslav Pajic:
Robust Reinforcement Learning through Efficient Adversarial Herding. CoRR abs/2306.07408 (2023) - [i88]Ziyang Jiang, Yiling Liu, Michael Hunter Klein, Ahmed Aloui, Yiman Ren, Keyu Li, Vahid Tarokh, David E. Carlson:
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators. CoRR abs/2306.07918 (2023) - [i87]Ahmed Aloui, Ali Hasan, Yuting Ng, Miroslav Pajic, Vahid Tarokh:
Individual Treatment Effects in Extreme Regimes. CoRR abs/2306.11697 (2023) - [i86]Cat P. Le, Chris Cannella, Ali Hasan, Yuting Ng, Vahid Tarokh:
PrACTiS: Perceiver-Attentional Copulas for Time Series. CoRR abs/2310.01720 (2023) - [i85]Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh, Min Chi, Miroslav Pajic:
Off-Policy Evaluation for Human Feedback. CoRR abs/2310.07123 (2023) - [i84]Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh:
Counterfactual Data Augmentation with Contrastive Learning. CoRR abs/2311.03630 (2023) - [i83]Shyam Venkatasubramanian, Ahmed Aloui, Vahid Tarokh:
Random Linear Projections Loss for Hyperplane-Based Optimization in Regression Neural Networks. CoRR abs/2311.12356 (2023) - 2022
- [j132]Cat P. Le, Mohammadreza Soltani, Juncheng Dong, Vahid Tarokh:
Fisher Task Distance and its Application in Neural Architecture Search. IEEE Access 10: 47235-47249 (2022) - [j131]Suya Wu, Enmao Diao, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Score-Based Hypothesis Testing for Unnormalized Models. IEEE Access 10: 71936-71950 (2022) - [j130]Ali Hasan, João M. Pereira, Sina Farsiu, Vahid Tarokh:
Identifying Latent Stochastic Differential Equations. IEEE Trans. Signal Process. 70: 89-104 (2022) - [c154]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 - [c153]Juncheng Dong, Suya Wu, Mohammadreza Soltani, Vahid Tarokh:
Multi-Agent Adversarial Attacks for Multi-Channel Communications. AAMAS 2022: 1580-1582 - [c152]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CVMI 2022: 109-121 - [c151]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. DCC 2022: 1-10 - [c150]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 - [c149]Juncheng Dong, Simiao Ren, Yang Deng, Omar Khatib, Jordan M. Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh:
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions. ICLR 2022 - [c148]Cat Phuoc Le, Juncheng Dong, Mohammadreza Soltani, Vahid Tarokh:
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning. ICLR 2022 - [c147]Chris Cannella, Vahid Tarokh:
Semi-Empirical Objective Functions for MCMC Proposal Optimization. ICPR 2022: 4758-4764 - [c146]Enmao Diao, Jie Ding, Vahid Tarokh:
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. NeurIPS 2022 - [c145]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. NeurIPS 2022 - [c144]Yuting Ng, Ali Hasan, Vahid Tarokh:
Inference and Sampling for Archimax Copulas. NeurIPS 2022 - [c143]Ali Hasan, Khalil Elkhalil, Yuting Ng, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh:
Modeling extremes with d-max-decreasing neural networks. UAI 2022: 759-768 - [c142]Shun Kojima, Yi Feng, Kazuki Maruta, Kanemitsu Ootsu, Takashi Yokota, Chang-Jun Ahn, Vahid Tarokh:
Towards Deep Learning-Guided Multiuser SNR and Doppler Shift Detection for Next-Generation Wireless Systems. VTC Spring 2022: 1-6 - [i82]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. CoRR abs/2201.03617 (2022) - [i81]Juncheng Dong, Suya Wu, Mohammadreza Soltani, Vahid Tarokh:
Multi-Agent Adversarial Attacks for Multi-Channel Communications. CoRR abs/2201.09149 (2022) - [i80]Shyam Venkatasubramanian, Chayut Wongkamthong, Mohammadreza Soltani, Bosung Kang, Sandeep Gogineni, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh:
Toward Data-Driven STAP Radar. CoRR abs/2201.10712 (2022) - [i79]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) - [i78]Yuting Ng, Ali Hasan, Vahid Tarokh:
Inference and Sampling for Archimax Copulas. CoRR abs/2205.14025 (2022) - [i77]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) - [i76]Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh:
Toward Data-Driven Radar STAP. CoRR abs/2209.02890 (2022) - [i75]Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh:
Causal Knowledge Transfer from Task Affinity. CoRR abs/2210.00380 (2022) - [i74]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
- [j129]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) - [j128]Shun Kojima, Kazuki Maruta, Yi Feng, Chang-Jun Ahn, Vahid Tarokh:
CNN-Based Joint SNR and Doppler Shift Classification Using Spectrogram Images for Adaptive Modulation and Coding. IEEE Trans. Commun. 69(8): 5152-5167 (2021) - [j127]Chin Hei Chan, Vahid Tarokh, Maosheng Xiong:
Convergence Rate of Empirical Spectral Distribution of Random Matrices From Linear Codes. IEEE Trans. Inf. Theory 67(2): 1080-1087 (2021) - [j126]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
On Statistical Efficiency in Learning. IEEE Trans. Inf. Theory 67(4): 2488-2506 (2021) - [c141]Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. AISTATS 2021: 352-360 - [c140]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 - [c139]Cat P. Le, Mohammadreza Soltani, Robert J. Ravier, Vahid Tarokh:
Task-Aware Neural Architecture Search. ICASSP 2021: 4090-4094 - [c138]Chris Cannella, Mohammadreza Soltani, Vahid Tarokh:
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows. ICLR 2021 - [c137]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. ICLR 2021 - [c136]Yang Deng, Juncheng Dong, Simiao Ren, Omar Khatib, Mohammadreza Soltani, Vahid Tarokh, Willie Padilla, Jordan M. Malof:
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials. NeurIPS Datasets and Benchmarks 2021 - [c135]Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh:
Generative Archimedean copulas. UAI 2021: 643-653 - [c134]Shun Kojima, Yi Feng, Kazuki Maruta, Kanemitsu Ootsu, Takashi Yokota, Chang-Jun Ahn, Vahid Tarokh:
Investigation of Input Signal Representation to CNN for Improving SNR Classification Accuracy. VTC Fall 2021: 1-5 - [i73]Ali Hasan, Khalil Elkhalil, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh:
Deep Extreme Value Copulas for Estimation and Sampling. CoRR abs/2102.09042 (2021) - [i72]Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh:
Generative Archimedean Copulas. CoRR abs/2102.11351 (2021) - [i71]Cat P. Le, Mohammadreza Soltani, Robert J. Ravier, Vahid Tarokh:
Neural Architecture Search From Task Similarity Measure. CoRR abs/2103.00241 (2021) - [i70]Jiali Xing, David Fischer, Nitya Labh, Ryan Piersma, Benjamin C. Lee, Yu Amy Xia, Tuhin Sahai, Vahid Tarokh:
Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond. CoRR abs/2103.02260 (2021) - [i69]Cat P. Le, Mohammadreza Soltani, Robert J. Ravier, Trevor Standley, Silvio Savarese, Vahid Tarokh:
Neural Architecture Search From Fréchet Task Distance. CoRR abs/2103.12827 (2021) - [i68]Anna K. Yanchenko, Mohammadreza Soltani, Robert J. Ravier, Sayan Mukherjee, Vahid Tarokh:
Towards Explainable Convolutional Features for Music Audio Modeling. CoRR abs/2106.00110 (2021) - [i67]Enmao Diao, Jie Ding, Vahid Tarokh:
Gradient Assisted Learning. CoRR abs/2106.01425 (2021) - [i66]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. CoRR abs/2106.01432 (2021) - [i65]Chris Cannella, Vahid Tarokh:
Semi-Empirical Objective Functions for MCMC Proposal Optimization. CoRR abs/2106.02104 (2021) - [i64]Cat P. Le, Juncheng Dong, Mohammadreza Soltani, Vahid Tarokh:
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning. CoRR abs/2110.02399 (2021) - [i63]Enmao Diao, Vahid Tarokh, Jie Ding:
Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders. CoRR abs/2110.13340 (2021) - [i62]Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh:
Characteristic Neural Ordinary Differential Equations. CoRR abs/2111.13207 (2021) - [i61]Juncheng Dong, Simiao Ren, Yang Deng, Omar Khatib, Jordan M. Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh:
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions. CoRR abs/2111.13311 (2021) - [i60]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models. CoRR abs/2112.03469 (2021) - 2020
- [j125]Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh:
Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study. IEEE Access 8: 36728-36740 (2020) - [c133]Enmao Diao, Jie Ding, Vahid Tarokh:
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC 2020: 3-12 - [c132]Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh:
Deep Clustering of Compressed Variational Embeddings. DCC 2020: 399 - [c131]Marko Angjelichinoski, Mohammadreza Soltani, John S. Choi, Bijan Pesaran, Vahid Tarokh:
Deep James-Stein Neural Networks For Brain-Computer Interfaces. ICASSP 2020: 1339-1343 - [c130]Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh:
Supervised Encoding for Discrete Representation Learning. ICASSP 2020: 3447-3451 - [c129]Yuting Ng, João M. Pereira, Denis Garagic, Vahid Tarokh:
Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means. ICASSP 2020: 3757-3761 - [c128]Ali Hasan, João M. Pereira, Robert J. Ravier, Sina Farsiu, Vahid Tarokh:
Learning Partial Differential Equations From Data Using Neural Networks. ICASSP 2020: 3962-3966 - [c127]Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh:
Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP 2020: 4022-4026 - [c126]Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh:
Speech Emotion Recognition with Dual-Sequence LSTM Architecture. ICASSP 2020: 6474-6478 - [c125]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 - [c124]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI 2020: 1445-1451 - [i59]Yuting Ng, João M. Pereira, Denis Garagic, Vahid Tarokh:
Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means. CoRR abs/2001.00564 (2020) - [i58]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CoRR abs/2002.02572 (2020) - [i57]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. CoRR abs/2002.11582 (2020) - [i56]Ali Hasan, João M. Pereira, Sina Farsiu, Vahid Tarokh:
Learning latent stochastic differential equations with variational auto-encoders. CoRR abs/2007.06075 (2020) - [i55]Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh:
Fisher Auto-Encoders. CoRR abs/2007.06120 (2020) - [i54]Chris Cannella, Mohammadreza Soltani, Vahid Tarokh:
Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows. CoRR abs/2007.06140 (2020) - [i53]Marko Angjelichinoski, Bijan Pesaran, Vahid Tarokh:
Deep Cross-Subject Mapping of Neural Activity. CoRR abs/2007.06407 (2020) - [i52]Robert J. Ravier, Mohammadreza Soltani, Miguel Antunes Dias Alfaiate, Denis Garagic, Vahid Tarokh:
An Interpretable Baseline for Time Series Classification Without Intensive Learning. CoRR abs/2007.06682 (2020) - [i51]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. CoRR abs/2010.01264 (2020) - [i50]Cat P. Le, Mohammadreza Soltani, Robert J. Ravier, Vahid Tarokh:
Task-Aware Neural Architecture Search. CoRR abs/2010.13962 (2020) - [i49]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
On Statistical Efficiency in Learning. CoRR abs/2012.13307 (2020)
2010 – 2019
- 2019
- [j124]Jie Ding, Jiawei Zhou, Vahid Tarokh:
Asymptotically Optimal Prediction for Time-Varying Data Generating Processes. IEEE Trans. Inf. Theory 65(5): 3034-3067 (2019) - [j123]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) - [c123]Enmao Diao, Jie Ding, Vahid Tarokh:
Restricted Recurrent Neural Networks. IEEE BigData 2019: 56-63 - [c122]Yi Feng, Yi Zhou, Vahid Tarokh:
Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. IEEE BigData 2019: 2240-2246 - [c121]Yan Zhang, Robert J. Ravier, Michael M. Zavlanos, Vahid Tarokh:
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret. CDC 2019: 2449-2454 - [c120]Robert J. Ravier, A. Robert Calderbank, Vahid Tarokh:
Prediction in Online Convex Optimization for Parametrizable Objective Functions. CDC 2019: 2455-2460 - [c119]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. ICLR (Poster) 2019 - [c118]Yi Zhou, Yi Feng, Vahid Tarokh, Vadas Gintautas, Jessee McClelland, Denis Garagic:
Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation. MLSP 2019: 1-6 - [c117]Taposh Banerjee, Stephen A. Allsop, Kay M. Tye, Demba E. Ba, Vahid Tarokh:
Sequential Detection of Regime Changes in Neural Data. NER 2019: 139-142 - [c116]Jie Ding, A. Robert Calderbank, Vahid Tarokh:
Gradient Information for Representation and Modeling. NeurIPS 2019: 2393-2402 - [c115]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. NeurIPS 2019: 2403-2413 - [i48]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. CoRR abs/1901.00451 (2019) - [i47]Marko Angjelichinoski, Taposh Banerjee, John S. Choi, Bijan Pesaran, Vahid Tarokh:
Minimax-optimal decoding of movement goals from local field potentials using complex spectral features. CoRR abs/1901.10397 (2019) - [i46]