Razaviyayn, Meisam

44 publications

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

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

JMLR 2025 Four Axiomatic Characterizations of the Integrated Gradients Attribution Method Daniel Lundstrom, Meisam Razaviyayn

NeurIPS 2025 Nested Learning: The Illusion of Deep Learning Architectures Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni

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

NeurIPS 2025 PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni

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

ICML 2025 Stochastic Control for Fine-Tuning Diffusion Models: Optimality, Regularity, and Convergence Yinbin Han, Meisam Razaviyayn, Renyuan Xu

ICML 2025 Synthetic Text Generation for Training Large Language Models via Gradient Matching Dang Nguyen, Zeman Li, Mohammadhossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman

ICLRW 2024 Addax: Memory-Efficient Fine-Tuning of Language Models with a Combination of Forward-Backward and Forward-Only Passes Zeman Li, Xinwei Zhang, Meisam Razaviyayn

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

NeurIPS 2024 DOPPLER: Differentially Private Optimizers with Low-Pass Filter for Privacy Noise Reduction Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn

NeurIPSW 2024 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

ICLR 2024 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

NeurIPSW 2024 On the Inherent Privacy of Two Point Zeroth Order Projected Gradient Descent Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan

ICML 2024 Optimal Differentially Private Model Training with Public Data Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

NeurIPSW 2023 $f$-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization Sina Baharlouei, Shivam Patel, Meisam Razaviyayn

ICML 2023 A Unifying Framework to the Analysis of Interaction Methods Using Synergy Functions Daniel Lundstrom, Meisam Razaviyayn

ICMLW 2023 A Unifying Framework to the Analysis of Interaction Methods Using Synergy Functions Daniel Lundstrom, Ali Ghafelebashi, Meisam Razaviyayn

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

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

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

ALT 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

TMLR 2023 RIFLE: Imputation and Robust Inference from Low Order Marginals Sina Baharlouei, Sze-Chuan Suen, Meisam Razaviyayn

TMLR 2023 Robustness Through Data Augmentation Loss Consistency Tianjian Huang, Shaunak Ashish Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

ICMLW 2023 Robustness Through Data Augmentation Loss Consistency Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

ICLR 2023 Stochastic Differentially Private and Fair Learning Andrew Lowy, Devansh Gupta, Meisam Razaviyayn

ICML 2022 A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions Daniel D Lundstrom, Tianjian Huang, Meisam Razaviyayn

TMLR 2022 A Stochastic Optimization Framework for Fair Risk Minimization Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

NeurIPSW 2022 A Stochastic Optimization Framework for Fair Risk Minimization Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

NeurIPSW 2022 Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, J Zico Kolter

NeurIPSW 2022 Policy Gradient Finds Global Optimum of Nearly Linear-Quadratic Control Systems Yinbin Han, Meisam Razaviyayn, Renyuan Xu

NeurIPSW 2022 Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses & Extension to Non-Convex Andrew Lowy, Meisam Razaviyayn

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

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

ICLR 2020 Rényi Fair Inference Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn

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

ICML 2018 Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization over Networks Mingyi Hong, Meisam Razaviyayn, Jason Lee

NeurIPS 2018 On the Convergence and Robustness of Training GANs with Regularized Optimal Transport Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason Lee

NeurIPS 2017 On Optimal Generalizability in Parametric Learning Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh

NeurIPS 2015 Discrete Rényi Classifiers Meisam Razaviyayn, Farzan Farnia, David Tse

NeurIPS 2014 Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang