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ORCIDDzung T. Phan
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2020 – today
- 2024
[c23]Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam, Chandra Reddy:
Multi-polytope Machine for Classification. SDM 2024: 109-117- [i17]Tsuyoshi Idé, Dzung T. Phan, Rudy Raymond:
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis. CoRR abs/2404.01270 (2024) - [i16]Quan M. Tran, Suong N. Hoang, Lam M. Nguyen, Dzung T. Phan, Hoang Thanh Lam:
TabularFM: An Open Framework For Tabular Foundational Models. CoRR abs/2406.09837 (2024) - 2023
- [c22]Vinícius Lima, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam:
Optimal Control via Linearizable Deep Learning. ACC 2023: 100-105 - [i15]Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung T. Phan, Roman Vaculín, Jayant Kalagnanam:
An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR abs/2306.00778 (2023) - 2022
- [j15]Lam M. Nguyen
, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam:
Finite-sum smooth optimization with SARAH. Comput. Optim. Appl. 82(3): 561-593 (2022) - [j14]Jayant Kalagnanam, Dzung T. Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, Dharmashankar Subramanian, Raju Pavuluri, Xiang Ma, Crystal Lui, Giovane Cesar Da Silva:
AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. INFORMS J. Appl. Anal. 52(4): 363-378 (2022) - [j13]Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen:
A hybrid stochastic optimization framework for composite nonconvex optimization. Math. Program. 191(2): 1005-1071 (2022) - [c21]Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy:
Interpretable Clustering via Multi-Polytope Machines. AAAI 2022: 7309-7316 - [c20]Dzung T. Phan, Hongsheng Liu, Lam M. Nguyen:
StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. SDM 2022: 477-485 - [i14]Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe:
Cardinality-Regularized Hawkes-Granger Model. CoRR abs/2208.10671 (2022) - 2021
- [j12]Dzung T. Phan, Matt Menickelly:
On the Solution of ℓ0-Constrained Sparse Inverse Covariance Estimation Problems. INFORMS J. Comput. 33(2): 531-550 (2021) - [j11]Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk:
A Unified Convergence Analysis for Shuffling-Type Gradient Methods. J. Mach. Learn. Res. 22: 207:1-207:44 (2021) - [c19]Dzung T. Phan, Lam M. Nguyen, Pavankumar Murali, Nhan H. Pham, Hongsheng Liu, Jayant R. Kalagnanam:
Regression Optimization for System-level Production Control. ACC 2021: 5023-5028 - [c18]Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung T. Phan, Sang (Peter) Chin, Trac D. Tran:
A Scale Invariant Measure of Flatness for Deep Network Minima. ICASSP 2021: 1680-1684 - [c17]Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe:
Cardinality-Regularized Hawkes-Granger Model. NeurIPS 2021: 2682-2694 - [c16]Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo:
Ensembling Graph Predictions for AMR Parsing. NeurIPS 2021: 8495-8505 - [c15]Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen:
FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. NeurIPS 2021: 30326-30338 - [i13]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh:
Federated Learning with Randomized Douglas-Rachford Splitting Methods. CoRR abs/2103.03452 (2021) - [i12]Hoang Thanh Lam, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam M. Nguyen, Dzung T. Phan, Vanessa López, Ramón Fernandez Astudillo:
Ensembling Graph Predictions for AMR Parsing. CoRR abs/2110.09131 (2021) - [i11]Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung T. Phan, Chandra Reddy:
Interpretable Clustering via Multi-Polytope Machines. CoRR abs/2112.05653 (2021) - 2020
- [j10]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh:
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. J. Mach. Learn. Res. 21: 110:1-110:48 (2020) - [c14]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh:
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. AISTATS 2020: 374-385 - [c13]Dzung T. Phan, Lam M. Nguyen, Nam H. Nguyen, Jayant R. Kalagnanam:
Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization. ICDM 2020: 1214-1219 - [c12]Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam:
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. NeurIPS 2020 - [i10]Lam M. Nguyen, Quoc Tran-Dinh, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk:
A Unified Convergence Analysis for Shuffling-Type Gradient Methods. CoRR abs/2002.08246 (2020) - [i9]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh:
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. CoRR abs/2003.00430 (2020) - [i8]Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam:
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. CoRR abs/2011.03375 (2020)
2010 – 2019
- 2019
- [c11]Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan:
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. ICML 2019: 6392-6400 - [c10]Tsuyoshi Idé, Rudy Raymond, Dzung T. Phan:
Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks. IJCAI 2019: 2585-2591 - [c9]Dzung T. Phan, Tsuyoshi Idé:
ℓ0-Regularized Sparsity for Probabilistic Mixture Models. SDM 2019: 172-180 - [i7]Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam:
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. CoRR abs/1901.07648 (2019) - [i6]Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung T. Phan, Sang H. Chin, Trac D. Tran:
A Scale Invariant Flatness Measure for Deep Network Minima. CoRR abs/1902.02434 (2019) - [i5]Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh:
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. CoRR abs/1902.05679 (2019) - [i4]Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan, Lam M. Nguyen:
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. CoRR abs/1907.03793 (2019) - 2018
- [i3]Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg:
When Does Stochastic Gradient Algorithm Work Well? CoRR abs/1801.06159 (2018) - [i2]Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan:
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. CoRR abs/1810.04100 (2018) - 2017
- [c8]Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam:
Multi-task Multi-modal Models for Collective Anomaly Detection. ICDM 2017: 177-186 - [c7]Dzung T. Phan, Tsuyoshi Idé, Jayant Kalagnanam, Matt Menickelly, Katya Scheinberg:
A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization. ICDM Workshops 2017: 830-833 - 2016
- [j9]Kevin Warren, Ron F. Ambrosio, Bei Chen, Yuhui Fu, Soumyadip Ghosh, Dzung T. Phan, Mathieu Sinn, Chunhua Tian, Chandu Visweswariah:
Managing uncertainty in electricity generation and demand forecasting. IBM J. Res. Dev. 60(1) (2016) - [j8]William W. Hager, Dzung T. Phan, Jiajie Zhu:
Projection algorithms for nonconvex minimization with application to sparse principal component analysis. J. Glob. Optim. 65(4): 657-676 (2016) - [c6]Dzung T. Phan, Soumyadip Ghosh:
Predicting and mitigating congestion for an electric power system under load and renewable uncertainty. ACC 2016: 6791-6796 - [c5]Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam:
Change Detection Using Directional Statistics. IJCAI 2016: 1613-1619 - 2015
- [j7]Dzung T. Phan, Yada Zhu:
Multi-stage optimization for periodic inspection planning of geo-distributed infrastructure systems. Eur. J. Oper. Res. 245(3): 797-804 (2015) - [c4]Jinjun Xiong, Dzung T. Phan, David Kung:
A Resource Supply-Demand based Approach for Automatic MapReduce Job Optimization. HPCC/CSS/ICESS 2015: 740-745 - 2014
- [j6]Dzung T. Phan, Soumyadip Ghosh:
Two-stage stochastic optimization for optimal power flow under renewable generation uncertainty. ACM Trans. Model. Comput. Simul. 24(1): 2:1-2:22 (2014) - 2013
- [j5]William W. Hager, Dzung T. Phan, Hongchao Zhang:
An exact algorithm for graph partitioning. Math. Program. 137(1-2): 531-556 (2013) - 2012
- [j4]Dzung T. Phan:
Lagrangian Duality and Branch-and-Bound Algorithms for Optimal Power Flow. Oper. Res. 60(2): 275-285 (2012) - [j3]Yunmei Chen, William W. Hager, Feng Huang, Dzung T. Phan, Xiaojing Ye, Wotao Yin:
Fast Algorithms for Image Reconstruction with Application to Partially Parallel MR Imaging. SIAM J. Imaging Sci. 5(1): 90-118 (2012) - [c3]Dzung T. Phan, Jinjun Xiong, Soumyadip Ghosh:
A distributed scheme for fair EV charging under transmission constraints. ACC 2012: 1053-1058 - [c2]Dzung T. Phan, Jayant Kalagnanam:
Distributed methods for solving the security-constrained optimal power flow problem. ISGT 2012: 1-7 - 2011
- [j2]William W. Hager, Dzung T. Phan, Hongchao Zhang:
Gradient-Based Methods for Sparse Recovery. SIAM J. Imaging Sci. 4(1): 146-165 (2011) - [c1]Dzung T. Phan, Soumyadip Ghosh:
A two-stage non-linear program for optimal electrical grid power balance under uncertainty. WSC 2011: 4227-4238
2000 – 2009
- 2009
- [j1]William W. Hager, Dzung T. Phan:
An Ellipsoidal Branch and Bound Algorithm for Global Optimization. SIAM J. Optim. 20(2): 740-758 (2009) - [i1]William W. Hager, Dzung T. Phan, Hongchao Zhang:
An exact algorithm for graph partitioning. CoRR abs/0912.1664 (2009)
Coauthor Index
aka: Jayant R. Kalagnanam
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last updated on 2024-09-26 00:57 CEST by the dblp team
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