A Stackelberg game approach to distributed spectrum management. - Meisam Razaviyayn; Yao Morin; Zhi-Quan Luo (ICASSP, 2010)

Joint transceiver design and user grouping in a MIMO interfering broadcast channel. - Meisam Razaviyayn; Hadi Baligh; Aaron Callard; Zhi-Quan Luo (CISS, 2011)

A stackelberg game approach to distributed spectrum management. - Meisam Razaviyayn; Zhi-Quan Luo; Paul Tseng; Jong-Shi Pang (Math. Program., 2011)

An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel. - Qingjiang Shi; Meisam Razaviyayn; Zhi-Quan Luo; Chen He 001 (IEEE Trans. Signal Process., 2011)

Joint transceiver design and base station clustering for heterogeneous networks. - Mingyi Hong 001; Meisam Razaviyayn; Ruoyu Sun 001; Zhi-Quan Luo (ACSCC, 2012)

SINR constrained beamforming for a MIMO multi-user downlink system. - Qingjiang Shi; Meisam Razaviyayn; Mingyi Hong 001; Zhi-Quan Luo (ACSCC, 2012)

Optimal joint base station assignment and downlink beamforming for heterogeneous networks. - Maziar Sanjabi; Meisam Razaviyayn; Zhi-Quan Luo (ICASSP, 2012)

Linear Transceiver Design for Interference Alignment: Complexity and Computation. - Meisam Razaviyayn; Maziar Sanjabi; Zhi-Quan Luo (IEEE Trans. Inf. Theory, 2012)

On the Degrees of Freedom Achievable Through Interference Alignment in a MIMO Interference Channel. - Meisam Razaviyayn; Gennady Lyubeznik; Zhi-Quan Luo (IEEE Trans. Signal Process., 2012)

A stochastic weighted MMSE approach to sum rate maximization for a MIMO interference channel. - Meisam Razaviyayn; Maziar Sanjabi Boroujeni; Zhi-Quan Luo (SPAWC, 2013)

Joint User Grouping and Linear Virtual Beamforming: Complexity, Algorithms and Approximation Bounds. - Mingyi Hong 001; Zi Xu; Meisam Razaviyayn; Zhi-Quan Luo (IEEE J. Sel. Areas Commun., 2013)

A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization. - Meisam Razaviyayn; Mingyi Hong 001; Zhi-Quan Luo (SIAM J. Optim., 2013)

Linear transceiver design for a MIMO interfering broadcast channel achieving max-min fairness. - Meisam Razaviyayn; Mingyi Hong 001; Zhi-Quan Luo (Signal Process., 2013)

A block coordinate descent method of multipliers: Convergence analysis and applications. - Mingyi Hong 001; Tsung-Hui Chang; Xiangfeng Wang; Meisam Razaviyayn; Shiqian Ma; Zhi-Quan Luo (ICASSP, 2014)

Dictionary learning for sparse representation: Complexity and algorithms. - Meisam Razaviyayn; Hung-Wei Tseng 004; Zhi-Quan Luo (ICASSP, 2014)

Joint day-ahead power procurement and load scheduling using stochastic alternating direction method of multipliers. - Xiangfeng Wang; Mingyi Hong 001; Tsung-Hui Chang; Meisam Razaviyayn; Zhi-Quan Luo (ICASSP, 2014)

Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization. - Meisam Razaviyayn; Mingyi Hong 001; Zhi-Quan Luo; Jong-Shi Pang (NIPS, 2014)

Joint base station clustering and beamformer design for partial coordinated transmission using statistical channel state information. - Maziar Sanjabi; Mingyi Hong 001; Meisam Razaviyayn; Zhi-Quan Luo (SPAWC, 2014)

Cross Layer Provision of Future Cellular Networks. - Hadi Baligh; Mingyi Hong 001; Wei-Cheng Liao; Zhi-Quan Luo; Meisam Razaviyayn; Maziar Sanjabi; Ruoyu Sun 001 (CoRR, 2014)

Cross-Layer Provision of Future Cellular Networks: A WMMSE-based approach. - Hadi Baligh; Mingyi Hong 001; Wei-Cheng Liao; Zhi-Quan Luo; Meisam Razaviyayn; Maziar Sanjabi; Ruoyu Sun 001 (IEEE Signal Process. Mag., 2014)

Joint User Grouping and Transceiver Design in a MIMO Interfering Broadcast Channel. - Meisam Razaviyayn; Mohammad Hadi Baligh; Aaron Callard; Zhi-Quan Luo (IEEE Trans. Signal Process., 2014)

Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks. - Maziar Sanjabi; Meisam Razaviyayn; Zhi-Quan Luo (IEEE Trans. Signal Process., 2014)

Minimum HGR correlation principle: From marginals to joint distribution. - Farzan Farnia; Meisam Razaviyayn; Sreeram Kannan; David Tse (ISIT, 2015)

Discrete Rényi Classifiers. - Meisam Razaviyayn; Farzan Farnia; David Tse (NIPS, 2015)

Computational Intractability of Dictionary Learning for Sparse Representation. - Meisam Razaviyayn; Hung-Wei Tseng 004; Zhi-Quan Luo (CoRR, 2015)

A unified distributed algorithm for non-cooperative games. - Jong-Shi Pang; Meisam Razaviyayn (Big Data over Networks, 2016)

Inexact Block Coordinate Descent Methods For Symmetric Nonnegative Matrix Factorization. - Qingjiang Shi; Haoran Sun; Songtao Lu; Mingyi Hong 001; Meisam Razaviyayn (CoRR, 2016)

A Stochastic Successive Minimization Method for Nonsmooth Nonconvex Optimization with Applications to Transceiver Design in Wireless Communication Networks. - Meisam Razaviyayn; Maziar Sanjabi; Zhi-Quan Luo (Math. Program., 2016)

Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems. - Mingyi Hong 001; Zhi-Quan Luo; Meisam Razaviyayn (SIAM J. Optim., 2016)

A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing. - Mingyi Hong 001; Meisam Razaviyayn; Zhi-Quan Luo; Jong-Shi Pang (IEEE Signal Process. Mag., 2016)

SINR Constrained Beamforming for a MIMO Multi-User Downlink System: Algorithms and Convergence Analysis. - Qingjiang Shi; Meisam Razaviyayn; Mingyi Hong 001; Zhi-Quan Luo (IEEE Trans. Signal Process., 2016)

On Optimal Generalizability in Parametric Learning. - Ahmad Beirami; Meisam Razaviyayn; Shahin Shahrampour; Vahid Tarokh (NIPS, 2017)

A Non-volatile Near-Memory Read Mapping Accelerator. - S. Karen Khatamifard; Zamshed I. Chowdhury; Nakul Pande; Meisam Razaviyayn; Chris H. Kim; Ulya R. Karpuzcu (CoRR, 2017)

Computing B-Stationary Points of Nonsmooth DC Programs. - Jong-Shi Pang; Meisam Razaviyayn; Alberth Alvarado (Math. Oper. Res., 2017)

Iteration complexity analysis of block coordinate descent methods. - Mingyi Hong 001; Xiangfeng Wang; Meisam Razaviyayn; Zhi-Quan Luo (Math. Program., 2017)

On the stability of gradient flow dynamics for a rank-one matrix approximation problem. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (ACC, 2018)

Variance Amplification of Accelerated First-Order Algorithms for Strongly Convex Quadratic Optimization Problems. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (CDC, 2018)

On the Behavior of the Expectation-Maximization Algorithm for Mixture Models. - Babak Barazandeh; Meisam Razaviyayn (GlobalSIP, 2018)

Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks. - Mingyi Hong 001; Meisam Razaviyayn; Jason D. Lee (ICML, 2018)

On the Convergence and Robustness of Training GANs with Regularized Optimal Transport. - Maziar Sanjabi; Jimmy Ba; Meisam Razaviyayn; Jason D. Lee (NeurIPS, 2018)

Solving Approximate Wasserstein GANs to Stationarity. - Maziar Sanjabi; Jimmy Ba; Meisam Razaviyayn; Jason D. Lee (CoRR, 2018)

Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization. - Mingyi Hong 001; Jason D. Lee; Meisam Razaviyayn (CoRR, 2018)

Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition. - Maziar Sanjabi; Meisam Razaviyayn; Jason D. Lee (CoRR, 2018)

Computational RAM to Accelerate String Matching at Scale. - Zamshed I. Chowdhury; S. Karen Khatamifard; Zhengyang Zhao 001; Masoud Zabihi; Salonik Resch; Meisam Razaviyayn; Jianping Wang 006; Sachin S. Sapatnekar; Ulya R. Karpuzcu (CoRR, 2018)

A New Sequential Optimization Procedure and Its Applications to Resource Allocation for Wireless Systems. - Augusto Aubry; Antonio De Maio; Alessio Zappone; Meisam Razaviyayn; Zhi-Quan Luo (IEEE Trans. Signal Process., 2018)

Performance of noisy Nesterov's accelerated method for strongly convex optimization problems. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (ACC, 2019)

Training Generative Networks Using Random Discriminators. - Babak Barazandeh; Meisam Razaviyayn; Maziar Sanjabi (DSW, 2019)

Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. - Maher Nouiehed; Maziar Sanjabi; Tianjian Huang; Jason D. Lee; Meisam Razaviyayn (NeurIPS, 2019)

Rényi Fair Inference. - Sina Baharlouei; Maher Nouiehed; Meisam Razaviyayn (CoRR, 2019)

SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems. - Songtao Lu; Meisam Razaviyayn; Bo Yang 053; Kejun Huang; Mingyi Hong 001 (CoRR, 2019)

When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima? - Maziar Sanjabi; Sina Baharlouei; Meisam Razaviyayn; Jason D. Lee (CoRR, 2019)

On the pervasiveness of difference-convexity in optimization and statistics. - Maher Nouiehed; Jong-Shi Pang; Meisam Razaviyayn (Math. Program., 2019)

Correction to: On the pervasiveness of difference-convexity in optimization and statistics. - Maher Nouiehed; Jong-Shi Pang; Meisam Razaviyayn (Math. Program., 2019)

A linearly convergent doubly stochastic Gauss-Seidel algorithm for solving linear equations and a certain class of over-parameterized optimization problems. - Meisam Razaviyayn; Mingyi Hong 001; Navid Reyhanian; Zhi-Quan Luo (Math. Program., 2019)

Solving Non-Convex Non-Differentiable Min-Max Games Using Proximal Gradient Method. - Babak Barazandeh; Meisam Razaviyayn (ICASSP, 2020)

Rényi Fair Inference. - Sina Baharlouei; Maher Nouiehed; Ahmad Beirami; Meisam Razaviyayn (ICLR, 2020)

Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. - Songtao Lu; Meisam Razaviyayn; Bo Yang 053; Kejun Huang; Mingyi Hong 001 (NeurIPS, 2020)

Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities. - Zhongruo Wang; Krishnakumar Balasubramanian 002; Shiqian Ma; Meisam Razaviyayn (CoRR, 2020)

Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. - Meisam Razaviyayn; Tianjian Huang; Songtao Lu; Maher Nouiehed; Maziar Sanjabi; Mingyi Hong 001 (CoRR, 2020)

A Block Successive Upper-Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization. - Mingyi Hong 001; Tsung-Hui Chang; Xiangfeng Wang; Meisam Razaviyayn; Shiqian Ma; Zhi-Quan Luo (Math. Oper. Res., 2020)

A Trust Region Method for Finding Second-Order Stationarity in Linearly Constrained Nonconvex Optimization. - Maher Nouiehed; Meisam Razaviyayn (SIAM J. Optim., 2020)

Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. - Meisam Razaviyayn; Tianjian Huang; Songtao Lu; Maher Nouiehed; Maziar Sanjabi; Mingyi Hong 001 (IEEE Signal Process. Mag., 2020)

Alternating Direction Method of Multipliers for Quantization. - Tianjian Huang; Prajwal Singhania; Maziar Sanjabi; Pabitra Mitra; Meisam Razaviyayn (AISTATS, 2021)

Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training. - Andrew Lowy; Meisam Razaviyayn (CoRR, 2021)

FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. - Andrew Lowy; Rakesh Pavan; Sina Baharlouei; Meisam Razaviyayn; Ahmad Beirami (CoRR, 2021)

Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models. - Babak Barazandeh; Ali Ghafelebashi; Meisam Razaviyayn; Ram Sriharsha (CoRR, 2021)

Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. - Andrew Lowy; Meisam Razaviyayn (CoRR, 2021)

RIFLE: Robust Inference from Low Order Marginals. - Sina Baharlouei; Kelechi Ogudu; Sze-Chuan Suen; Meisam Razaviyayn (CoRR, 2021)

Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain. - Dmitrii M. Ostrovskii; Babak Barazandeh; Meisam Razaviyayn (CoRR, 2021)

DAIR: Data Augmented Invariant Regularization. - Tianjian Huang; Shaunak Ashish Halbe; Chinnadhurai Sankar; Pooyan Amini; Satwik Kottur; Alborz Geramifard; Meisam Razaviyayn; Ahmad Beirami (CoRR, 2021)

Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems. - Dmitrii M. Ostrovskii; Andrew Lowy; Meisam Razaviyayn (SIAM J. Optim., 2021)

Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (IEEE Trans. Autom. Control., 2021)

Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. - Songtao Lu; Jason D. Lee; Meisam Razaviyayn; Mingyi Hong 001 (IEEE Trans. Signal Process., 2021)

Stochastic Differentially Private and Fair Learning. - Andrew Lowy; Devansh Gupta; Meisam Razaviyayn (AFCP, 2022)

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions. - Daniel Lundström; Tianjian Huang; Meisam Razaviyayn (ICML, 2022)

I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads. - Sina Baharlouei; Meisam Razaviyayn; Elizabeth Tseng; David Tse (PKDD/ECML Workshops (2), 2022)

Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. - Andrew Lowy; Meisam Razaviyayn (CoRR, 2022)

GeNVoM: Read Mapping Near Non-Volatile Memory. - S. Karen Khatamifard; Zamshed I. Chowdhury; Nakul Pande; Meisam Razaviyayn; Chris H. Kim; Ulya R. Karpuzcu (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

CRAM-Seq: Accelerating RNA-Seq Abundance Quantification Using Computational RAM. - Zamshed I. Chowdhury; S. Karen Khatamifard; Salonik Resch; M. Hüsrev Cilasun; Zhengyang Zhao 001; Masoud Zabihi; Meisam Razaviyayn; Jian-Ping Wang 006; Sachin S. Sapatnekar; Ulya R. Karpuzcu (IEEE Trans. Emerg. Top. Comput., 2022)

A Stochastic Optimization Framework for Fair Risk Minimization. - Andrew Lowy; Sina Baharlouei; Rakesh Pavan; Meisam Razaviyayn; Ahmad Beirami (Trans. Mach. Learn. Res., 2022)

Learning Deep Models: Critical Points and Local Openness. - Maher Nouiehed; Meisam Razaviyayn (INFORMS J. Optim., 2022)

Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes. - Sina Baharlouei; Fatemeh Sheikholeslami; Meisam Razaviyayn; Zico Kolter (AISTATS, 2023)

Private Non-Convex Federated Learning Without a Trusted Server. - Andrew Lowy; Ali Ghafelebashi; Meisam Razaviyayn (AISTATS, 2023)

Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. - Andrew Lowy; Meisam Razaviyayn (ALT, 2023)

Noise amplifiation of momentum-based optimization algorithms. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (ACC, 2023)

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. - Andrew Lowy; Meisam Razaviyayn (ICLR, 2023)

A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions. - Daniel Lundström; Meisam Razaviyayn (ICML, 2023)

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators. - Yinbin Han; Meisam Razaviyayn; Renyuan Xu (CoRR, 2023)

Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability. - Daniel Lundström; Meisam Razaviyayn (CoRR, 2023)

Four Axiomatic Characterizations of the Integrated Gradients Attribution Method. - Daniel Lundström; Meisam Razaviyayn (CoRR, 2023)

Optimal Differentially Private Learning with Public Data. - Andrew Lowy; Zeman Li; Tianjian Huang; Meisam Razaviyayn (CoRR, 2023)

Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework. - Sina Baharlouei; Meisam Razaviyayn (CoRR, 2023)

RIFLE: Imputation and Robust Inference from Low Order Marginals. - Sina Baharlouei; Sze-Chuan Suen; Meisam Razaviyayn (Trans. Mach. Learn. Res., 2023)

Robustness through Data Augmentation Loss Consistency. - Tianjian Huang; Shaunak Ashish Halbe; Chinnadhurai Sankar; Pooyan Amini; Satwik Kottur; Alborz Geramifard; Meisam Razaviyayn; Ahmad Beirami (Trans. Mach. Learn. Res., 2023)

Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities. - Zhongruo Wang; Krishnakumar Balasubramanian 002; Shiqian Ma; Meisam Razaviyayn (J. Glob. Optim., 2023)

f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization. - Sina Baharlouei; Shivam Patel; Meisam Razaviyayn (ICLR, 2024)

Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization. - Yinbin Han; Meisam Razaviyayn; Renyuan Xu (ICLR, 2024)

Optimal Differentially Private Model Training with Public Data. - Andrew Lowy; Zeman Li; Tianjian Huang; Meisam Razaviyayn (ICML, 2024)

Differentially Private Next-Token Prediction of Large Language Models. - James Flemings; Meisam Razaviyayn; Murali Annavaram (NAACL-HLT, 2024)

DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. - Xinwei Zhang 001; Zhiqi Bu; Mingyi Hong 001; Meisam Razaviyayn (NeurIPS, 2024)

Adaptively Private Next-Token Prediction of Large Language Models. - James Flemings; Meisam Razaviyayn; Murali Annavaram (CoRR, 2024)

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data. - Devansh Gupta; A. S. Poornash; Andrew Lowy; Meisam Razaviyayn (CoRR, 2024)

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence. - Yinbin Han; Meisam Razaviyayn; Renyuan Xu (CoRR, 2024)

On the Inherent Privacy of Zeroth-Order Projected Gradient Descent. - Devansh Gupta; Meisam Razaviyayn; Vatsal Sharan (AISTATS, 2025)

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. - Xinwei Zhang 001; Zhiqi Bu; Borja Balle; Mingyi Hong 001; Meisam Razaviyayn; Vahab Mirrokni (ICLR, 2025)

Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. - Zeman Li; Xinwei Zhang 001; Peilin Zhong; Yuan Deng; Meisam Razaviyayn; Vahab Mirrokni (ICLR, 2025)

Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter. - Andrew Lowy; Meisam Razaviyayn (J. Priv. Confidentiality, 2025)

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs. - James Flemings; Haosheng Gan; Hongyi Li; Meisam Razaviyayn; Murali Annavaram (CoRR, 2025)

PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts. - Zeman Li; Yuan Deng; Peilin Zhong; Meisam Razaviyayn; Vahab Mirrokni (CoRR, 2025)

Synthetic Text Generation for Training Large Language Models via Gradient Matching. - Dang Nguyen; Zeman Li; MohammadHossein Bateni; Vahab Mirrokni; Meisam Razaviyayn; Baharan Mirzasoleiman (CoRR, 2025)

Tradeoffs Between Convergence Rate and Noise Amplification for Momentum-Based Accelerated Optimization Algorithms. - Hesameddin Mohammadi; Meisam Razaviyayn; Mihailo R. Jovanovic (IEEE Trans. Autom. Control., 2025)

Incentive Systems for Fleets of New Mobility Services. - Ali Ghafelebashi; Meisam Razaviyayn; Maged M. Dessouky (IEEE Trans. Intell. Transp. Syst., 2025)

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization. - Ali Behrouz; Meisam Razaviyayn; Peilin Zhong; Vahab Mirrokni (CoRR, 2025)

ATLAS: Learning to Optimally Memorize the Context at Test Time. - Ali Behrouz; Zeman Li; Praneeth Kacham; Majid Daliri; Yuan Deng; Peilin Zhong; Meisam Razaviyayn; Vahab Mirrokni (CoRR, 2025)

Memory-Efficient Differentially Private Training with Gradient Random Projection. - Alex Mulrooney; Devansh Gupta; James Flemings; Huanyu Zhang; Murali Annavaram; Meisam Razaviyayn; Xinwei Zhang (CoRR, 2025)