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2020 – today
- 2024
[c44]Sina Baharlouei, Shivam Patel, Meisam Razaviyayn:
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization. ICLR 2024- [c43]Yinbin Han, Meisam Razaviyayn, Renyuan Xu:
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization. ICLR 2024 - [c42]Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn:
Optimal Differentially Private Model Training with Public Data. ICML 2024 - [c41]James Flemings, Meisam Razaviyayn, Murali Annavaram:
Differentially Private Next-Token Prediction of Large Language Models. NAACL-HLT 2024: 4390-4404 - [i53]Yinbin Han, Meisam Razaviyayn, Renyuan Xu:
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization. CoRR abs/2401.15604 (2024) - [i52]James Flemings, Meisam Razaviyayn, Murali Annavaram:
Differentially Private Next-Token Prediction of Large Language Models. CoRR abs/2403.15638 (2024) - [i51]Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn:
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. CoRR abs/2408.13460 (2024) - [i50]James Flemings, Meisam Razaviyayn, Murali Annavaram:
Adaptively Private Next-Token Prediction of Large Language Models. CoRR abs/2410.02016 (2024) - [i49]Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni:
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. CoRR abs/2410.03883 (2024) - 2023
- [j33]Zhongruo Wang
, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn:
Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. J. Glob. Optim. 87(2): 709-740 (2023) - [j32]Sina Baharlouei, Sze-Chuan Suen, Meisam Razaviyayn:
RIFLE: Imputation and Robust Inference from Low Order Marginals. Trans. Mach. Learn. Res. 2023 (2023) - [j31]Tianjian Huang, Shaunak Ashish Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami:
Robustness through Data Augmentation Loss Consistency. Trans. Mach. Learn. Res. 2023 (2023) - [c40]Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn:
Private Non-Convex Federated Learning Without a Trusted Server. AISTATS 2023: 5749-5786 - [c39]Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter:
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. AISTATS 2023: 11059-11078 - [c38]Andrew Lowy, Meisam Razaviyayn:
Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. ALT 2023: 986-1054 - [c37]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Noise amplifiation of momentum-based optimization algorithms. ACC 2023: 849-854 - [c36]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. ICLR 2023 - [c35]Andrew Lowy, Meisam Razaviyayn:
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. ICLR 2023 - [c34]Daniel Lundström, Meisam Razaviyayn:
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions. ICML 2023: 23005-23032 - [i48]Yinbin Han, Meisam Razaviyayn, Renyuan Xu:
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators. CoRR abs/2303.08431 (2023) - [i47]Daniel Lundström, Meisam Razaviyayn:
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability. CoRR abs/2305.03100 (2023) - [i46]Daniel Lundström, Meisam Razaviyayn:
Four Axiomatic Characterizations of the Integrated Gradients Attribution Method. CoRR abs/2306.13753 (2023) - [i45]Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn:
Optimal Differentially Private Learning with Public Data. CoRR abs/2306.15056 (2023) - [i44]Sina Baharlouei, Meisam Razaviyayn:
Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework. CoRR abs/2309.11682 (2023) - [i43]Ali Ghafelebashi, Meisam Razaviyayn, Maged M. Dessouky:
Incentive Systems for Fleets of New Mobility Services. CoRR abs/2312.02341 (2023) - [i42]Sina Baharlouei, Shivam Patel, Meisam Razaviyayn:
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization. CoRR abs/2312.03259 (2023) - 2022
- [j30]Maher Nouiehed, Meisam Razaviyayn:
Learning Deep Models: Critical Points and Local Openness. INFORMS J. Optim. 4(2): 148-173 (2022) - [j29]S. Karen Khatamifard, Zamshed I. Chowdhury, Nakul Pande, Meisam Razaviyayn, Chris H. Kim, Ulya R. Karpuzcu:
GeNVoM: Read Mapping Near Non-Volatile Memory. IEEE ACM Trans. Comput. Biol. Bioinform. 19(6): 3482-3496 (2022) - [j28]Zamshed I. Chowdhury, S. Karen Khatamifard, Salonik Resch, M. Hüsrev Cilasun, Zhengyang Zhao, Masoud Zabihi, Meisam Razaviyayn, Jian-Ping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu:
CRAM-Seq: Accelerating RNA-Seq Abundance Quantification Using Computational RAM. IEEE Trans. Emerg. Top. Comput. 10(4): 2055-2071 (2022) - [j27]Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami:
A Stochastic Optimization Framework for Fair Risk Minimization. Trans. Mach. Learn. Res. 2022 (2022) - [c33]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. AFCP 2022: 86-119 - [c32]Daniel Lundström, Tianjian Huang, Meisam Razaviyayn:
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions. ICML 2022: 14485-14508 - [c31]Sina Baharlouei, Meisam Razaviyayn, Elizabeth Tseng, David Tse:
I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads. PKDD/ECML Workshops (2) 2022: 339-363 - [i41]Daniel Lundström, Tianjian Huang, Meisam Razaviyayn:
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions. CoRR abs/2202.11912 (2022) - [i40]Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn:
Private Non-Convex Federated Learning Without a Trusted Server. CoRR abs/2203.06735 (2022) - [i39]Andrew Lowy, Meisam Razaviyayn:
Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. CoRR abs/2209.07403 (2022) - [i38]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms. CoRR abs/2209.11920 (2022) - [i37]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. CoRR abs/2210.08781 (2022) - [i36]Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter:
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. CoRR abs/2210.14410 (2022) - 2021
- [j26]Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn:
Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems. SIAM J. Optim. 31(4): 2508-2538 (2021) - [j25]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems. IEEE Trans. Autom. Control. 66(6): 2480-2495 (2021) - [j24]Songtao Lu, Jason D. Lee, Meisam Razaviyayn, Mingyi Hong:
Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. IEEE Trans. Signal Process. 69: 4859-4874 (2021) - [c30]Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn:
Alternating Direction Method of Multipliers for Quantization. AISTATS 2021: 208-216 - [i35]Andrew Lowy, Meisam Razaviyayn:
Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training. CoRR abs/2102.04704 (2021) - [i34]Andrew Lowy, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami:
FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. CoRR abs/2102.12586 (2021) - [i33]Babak Barazandeh, Ali Ghafelebashi, Meisam Razaviyayn, Ram Sriharsha:
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models. CoRR abs/2105.05953 (2021) - [i32]Andrew Lowy, Meisam Razaviyayn:
Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. CoRR abs/2106.09779 (2021) - [i31]Sina Baharlouei, Kelechi Ogudu, Sze-Chuan Suen, Meisam Razaviyayn:
RIFLE: Robust Inference from Low Order Marginals. CoRR abs/2109.00644 (2021) - [i30]Dmitrii M. Ostrovskii, Babak Barazandeh, Meisam Razaviyayn:
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain. CoRR abs/2110.03950 (2021) - [i29]Tianjian Huang, Shaunak Ashish Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami:
DAIR: Data Augmented Invariant Regularization. CoRR abs/2110.11205 (2021) - 2020
- [j23]Mingyi Hong, Tsung-Hui Chang, Xiangfeng Wang, Meisam Razaviyayn, Shiqian Ma, Zhi-Quan Luo:
A Block Successive Upper-Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization. Math. Oper. Res. 45(3): 833-861 (2020) - [j22]Maher Nouiehed, Meisam Razaviyayn:
A Trust Region Method for Finding Second-Order Stationarity in Linearly Constrained Nonconvex Optimization. SIAM J. Optim. 30(3): 2501-2529 (2020) - [j21]Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong:
Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. IEEE Signal Process. Mag. 37(5): 55-66 (2020) - [c29]Babak Barazandeh, Meisam Razaviyayn:
Solving Non-Convex Non-Differentiable Min-Max Games Using Proximal Gradient Method. ICASSP 2020: 3162-3166 - [c28]Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn:
Rényi Fair Inference. ICLR 2020 - [c27]Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. NeurIPS 2020 - [i28]Zhongruo Wang, Krishnakumar Balasubramanian, Shiqian Ma, Meisam Razaviyayn:
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. CoRR abs/2001.07819 (2020) - [i27]Babak Barazandeh, Meisam Razaviyayn:
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method. CoRR abs/2003.08093 (2020) - [i26]Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong:
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. CoRR abs/2006.08141 (2020) - [i25]Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn:
Alternating Direction Method of Multipliers for Quantization. CoRR abs/2009.03482 (2020)
2010 – 2019
- 2019
- [j20]Maher Nouiehed, Jong-Shi Pang, Meisam Razaviyayn:
On the pervasiveness of difference-convexity in optimization and statistics. Math. Program. 174(1-2): 195-222 (2019) - [j19]Maher Nouiehed, Jong-Shi Pang, Meisam Razaviyayn:
Correction to: On the pervasiveness of difference-convexity in optimization and statistics. Math. Program. 174(1-2): 223-224 (2019) - [j18]Meisam Razaviyayn, Mingyi Hong, Navid Reyhanian, Zhi-Quan Luo:
A linearly convergent doubly stochastic Gauss-Seidel algorithm for solving linear equations and a certain class of over-parameterized optimization problems. Math. Program. 176(1-2): 465-496 (2019) - [c26]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Performance of noisy Nesterov's accelerated method for strongly convex optimization problems. ACC 2019: 3426-3431 - [c25]Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi:
Training Generative Networks Using Random Discriminators. DSW 2019: 327-332 - [c24]Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn:
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. NeurIPS 2019: 14905-14916 - [i24]Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn:
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. CoRR abs/1902.08297 (2019) - [i23]Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi:
Training generative networks using random discriminators. CoRR abs/1904.09775 (2019) - [i22]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Robustness of accelerated first-order algorithms for strongly convex optimization problems. CoRR abs/1905.11011 (2019) - [i21]Sina Baharlouei, Maher Nouiehed, Meisam Razaviyayn:
Rényi Fair Inference. CoRR abs/1906.12005 (2019) - [i20]Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems. CoRR abs/1907.04450 (2019) - [i19]Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn, Jason D. Lee:
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima? CoRR abs/1911.09815 (2019) - 2018
- [j17]Augusto Aubry, Antonio De Maio, Alessio Zappone, Meisam Razaviyayn, Zhi-Quan Luo:
A New Sequential Optimization Procedure and Its Applications to Resource Allocation for Wireless Systems. IEEE Trans. Signal Process. 66(24): 6518-6533 (2018) - [c23]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
On the stability of gradient flow dynamics for a rank-one matrix approximation problem. ACC 2018: 4533-4538 - [c22]Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanovic:
Variance Amplification of Accelerated First-Order Algorithms for Strongly Convex Quadratic Optimization Problems. CDC 2018: 5753-5758 - [c21]Babak Barazandeh, Meisam Razaviyayn:
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models. GlobalSIP 2018: 61-65 - [c20]Maher Nouiehed, Meisam Razaviyayn:
Learning Deep Models: Critical Points and Local Openness. ICLR (Workshop) 2018 - [c19]Mingyi Hong, Meisam Razaviyayn, Jason D. Lee:
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks. ICML 2018: 2014-2023 - [c18]Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee:
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport. NeurIPS 2018: 7091-7101 - [i18]Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee:
Solving Approximate Wasserstein GANs to Stationarity. CoRR abs/1802.08249 (2018) - [i17]Mingyi Hong, Jason D. Lee, Meisam Razaviyayn:
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization. CoRR abs/1802.08941 (2018) - [i16]Babak Barazandeh, Meisam Razaviyayn:
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models. CoRR abs/1809.08705 (2018) - [i15]Maziar Sanjabi, Meisam Razaviyayn, Jason D. Lee:
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition. CoRR abs/1812.02878 (2018) - [i14]Zamshed I. Chowdhury, S. Karen Khatamifard, Zhengyang Zhao, Masoud Zabihi, Salonik Resch, Meisam Razaviyayn, Jianping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu:
Computational RAM to Accelerate String Matching at Scale. CoRR abs/1812.08918 (2018) - 2017
- [j16]Jong-Shi Pang, Meisam Razaviyayn, Alberth Alvarado:
Computing B-Stationary Points of Nonsmooth DC Programs. Math. Oper. Res. 42(1): 95-118 (2017) - [j15]Mingyi Hong, Xiangfeng Wang, Meisam Razaviyayn, Zhi-Quan Luo:
Iteration complexity analysis of block coordinate descent methods. Math. Program. 163(1-2): 85-114 (2017) - [c17]Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh:
On Optimal Generalizability in Parametric Learning. NIPS 2017: 3455-3465 - [i13]S. Karen Khatamifard, Zamshed I. Chowdhury, Nakul Pande, Meisam Razaviyayn, Chris H. Kim, Ulya R. Karpuzcu:
A Non-volatile Near-Memory Read Mapping Accelerator. CoRR abs/1709.02381 (2017) - [i12]Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh:
On Optimal Generalizability in Parametric Learning. CoRR abs/1711.05323 (2017) - 2016
- [j14]Meisam Razaviyayn, Maziar Sanjabi, Zhi-Quan Luo:
A Stochastic Successive Minimization Method for Nonsmooth Nonconvex Optimization with Applications to Transceiver Design in Wireless Communication Networks. Math. Program. 157(2): 515-545 (2016) - [j13]Mingyi Hong, Zhi-Quan Luo, Meisam Razaviyayn:
Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems. SIAM J. Optim. 26(1): 337-364 (2016) - [j12]Mingyi Hong, Meisam Razaviyayn, Zhi-Quan Luo, Jong-Shi Pang:
A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing. IEEE Signal Process. Mag. 33(1): 57-77 (2016) - [j11]Qingjiang Shi, Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
SINR Constrained Beamforming for a MIMO Multi-User Downlink System: Algorithms and Convergence Analysis. IEEE Trans. Signal Process. 64(11): 2920-2933 (2016) - [p1]Jong-Shi Pang, Meisam Razaviyayn:
A unified distributed algorithm for non-cooperative games. Big Data over Networks 2016: 101-134 - [i11]Qingjiang Shi, Haoran Sun, Songtao Lu, Mingyi Hong, Meisam Razaviyayn:
Inexact Block Coordinate Descent Methods For Symmetric Nonnegative Matrix Factorization. CoRR abs/1607.03092 (2016) - 2015
- [c16]Mingyi Hong, Zhi-Quan Luo, Meisam Razaviyayn:
Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems. ICASSP 2015: 3836-3840 - [c15]Farzan Farnia, Meisam Razaviyayn, Sreeram Kannan, David Tse:
Minimum HGR correlation principle: From marginals to joint distribution. ISIT 2015: 1377-1381 - [c14]Meisam Razaviyayn, Farzan Farnia, David Tse:
Discrete Rényi Classifiers. NIPS 2015: 3276-3284 - [i10]Farzan Farnia, Meisam Razaviyayn, Sreeram Kannan, David Tse:
Minimum HGR Correlation Principle: From Marginals to Joint Distribution. CoRR abs/1504.06010 (2015) - [i9]Qingjiang Shi, Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
SINR Constrained Beamforming for a MIMO Multi-user Downlink System. CoRR abs/1507.07115 (2015) - [i8]Meisam Razaviyayn, Farzan Farnia, David N. C. Tse:
Discrete Rényi Classifiers. CoRR abs/1511.01764 (2015) - [i7]Meisam Razaviyayn, Hung-Wei Tseng, Zhi-Quan Luo:
Computational Intractability of Dictionary Learning for Sparse Representation. CoRR abs/1511.01776 (2015) - 2014
- [j10]Hadi Baligh, Mingyi Hong, Wei-Cheng Liao, Zhi-Quan Luo, Meisam Razaviyayn, Maziar Sanjabi, Ruoyu Sun:
Cross-Layer Provision of Future Cellular Networks: A WMMSE-based approach. IEEE Signal Process. Mag. 31(6): 56-68 (2014) - [j9]Meisam Razaviyayn, Mohammad Hadi Baligh, Aaron Callard, Zhi-Quan Luo:
Joint User Grouping and Transceiver Design in a MIMO Interfering Broadcast Channel. IEEE Trans. Signal Process. 62(1): 85-94 (2014) - [j8]Maziar Sanjabi, Meisam Razaviyayn, Zhi-Quan Luo:
Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks. IEEE Trans. Signal Process. 62(8): 1950-1961 (2014) - [c13]Meisam Razaviyayn, Hung-Wei Tseng, Zhi-Quan Luo:
Dictionary learning for sparse representation: Complexity and algorithms. ICASSP 2014: 5247-5251 - [c12]Mingyi Hong, Tsung-Hui Chang, Xiangfeng Wang, Meisam Razaviyayn, Shiqian Ma, Zhi-Quan Luo:
A block coordinate descent method of multipliers: Convergence analysis and applications. ICASSP 2014: 7689-7693 - [c11]Xiangfeng Wang, Mingyi Hong, Tsung-Hui Chang, Meisam Razaviyayn, Zhi-Quan Luo:
Joint day-ahead power procurement and load scheduling using stochastic alternating direction method of multipliers. ICASSP 2014: 7754-7758 - [c10]Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang:
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization. NIPS 2014: 1440-1448 - [c9]Maziar Sanjabi, Mingyi Hong, Meisam Razaviyayn, Zhi-Quan Luo:
Joint base station clustering and beamformer design for partial coordinated transmission using statistical channel state information. SPAWC 2014: 359-363 - [i6]Hadi Baligh, Mingyi Hong, Wei-Cheng Liao, Zhi-Quan Luo, Meisam Razaviyayn, Maziar Sanjabi, Ruoyu Sun:
Cross Layer Provision of Future Cellular Networks. CoRR abs/1407.1424 (2014) - 2013
- [j7]Mingyi Hong, Zi Xu, Meisam Razaviyayn, Zhi-Quan Luo:
Joint User Grouping and Linear Virtual Beamforming: Complexity, Algorithms and Approximation Bounds. IEEE J. Sel. Areas Commun. 31(10): 2013-2027 (2013) - [j6]Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization. SIAM J. Optim. 23(2): 1126-1153 (2013) - [j5]Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
Linear transceiver design for a MIMO interfering broadcast channel achieving max-min fairness. Signal Process. 93(12): 3327-3340 (2013) - [c8]Meisam Razaviyayn, Maziar Sanjabi Boroujeni, Zhi-Quan Luo:
A stochastic weighted MMSE approach to sum rate maximization for a MIMO interference channel. SPAWC 2013: 325-329 - [i5]Meisam Razaviyayn, Maziar Sanjabi, Zhi-Quan Luo:
A Stochastic Successive Minimization Method for Nonsmooth Nonconvex Optimization with Applications to Transceiver Design in Wireless Communication Networks. CoRR abs/1307.4457 (2013) - 2012
- [j4]Meisam Razaviyayn, Maziar Sanjabi, Zhi-Quan Luo:
Linear Transceiver Design for Interference Alignment: Complexity and Computation. IEEE Trans. Inf. Theory 58(5): 2896-2910 (2012) - [j3]Meisam Razaviyayn, Gennady Lyubeznik, Zhi-Quan Luo:
On the Degrees of Freedom Achievable Through Interference Alignment in a MIMO Interference Channel. IEEE Trans. Signal Process. 60(2): 812-821 (2012) - [c7]Mingyi Hong, Meisam Razaviyayn, Ruoyu Sun, Zhi-Quan Luo:
Joint transceiver design and base station clustering for heterogeneous networks. ACSCC 2012: 574-578 - [c6]Qingjiang Shi, Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
SINR constrained beamforming for a MIMO multi-user downlink system. ACSCC 2012: 1991-1995 - [c5]Maziar Sanjabi, Meisam Razaviyayn, Zhi-Quan Luo:
Optimal joint base station assignment and downlink beamforming for heterogeneous networks. ICASSP 2012: 2821-2824 - [i4]Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
Linear Transceiver Design for a MIMO Interfering Broadcast Channel Achieving Max-Min Fairness. CoRR abs/1208.6357 (2012) - [i3]Mingyi Hong, Zi Xu, Meisam Razaviyayn, Zhi-Quan Luo:
Joint User Grouping and Linear Virtual Beamforming: Complexity, Algorithms and Approximation Bounds. CoRR abs/1209.4683 (2012) - 2011
- [j2]Meisam Razaviyayn, Zhi-Quan Luo, Paul Tseng, Jong-Shi Pang:
A stackelberg game approach to distributed spectrum management. Math. Program. 129(2): 197-224 (2011) - [j1]Qingjiang Shi, Meisam Razaviyayn, Zhi-Quan Luo, Chen He:
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel. IEEE Trans. Signal Process. 59(9): 4331-4340 (2011) - [c4]Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo:
Linear transceiver design for a MIMO interfering broadcast channel achieving max-min fairness. ACSCC 2011: 1309-1313 - [c3]Meisam Razaviyayn, Hadi Baligh, Aaron Callard, Zhi-Quan Luo:
Joint transceiver design and user grouping in a MIMO interfering broadcast channel. CISS 2011: 1-6 - [c2]Qingjiang Shi, Meisam Razaviyayn, Zhi-Quan Luo, Chen He:
An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel. ICASSP 2011: 3060-3063 - [i2]Meisam Razaviyayn, Gennady Lyubeznik, Zhi-Quan Luo:
On the Degrees of Freedom Achievable Through Interference Alignment in a MIMO Interference Channel. CoRR abs/1104.0992 (2011) - 2010
- [c1]Meisam Razaviyayn, Yao Morin, Zhi-Quan Luo:
A Stackelberg game approach to distributed spectrum management. ICASSP 2010: 3006-3009 - [i1]Meisam Razaviyayn, Maziar Sanjabi, Zhi-Quan Luo:
Linear Transceiver Design for Interference Alignment: Complexity and Computation. CoRR abs/1009.3481 (2010)
Coauthor Index
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