Meisam Razaviyayn
Andrew and Erna Viterbi Early Career Chair and Associate Professor Industrial and Systems Engineering, Computer Science, and Electrical and Computer Engineering
Researches optimization for machine learning, including nonconvex min-max problems and fair learning.
Meisam Razaviyayn's lab at USC works on learning theory & optimization and trustworthy & safe ML. 32 papers since 2023, 21 at top venues, mostly ICLR and ICML. 6 PhD students. 10 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 66%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 10
Papers by yearfrom DBLP, 2023–2026
- 4 papers2026 · T1 2 · collecting
- 13 papers2025 · T1 9
- 5 papers2024 · T1 5
- 10 papers2023 · T1 5
T1 venuesOtherVenues: ICLR, ICML, AISTATS, NeurIPS, ACC
Research focuspaper titles that use each topic's words
- Learning theory & optimization12 papers
- fair3 papers
- Trustworthy & safe ML1 paper
- nonconvex1 paper
- systems engineering0 papers
Paper list
From DBLP.
20264 papers
- Trans. Mach. Learn. Res.A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data (opens in a new tab)
- ICLR (T1)It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization (opens in a new tab)
- Trans. Mach. Learn. Res.Memory-Efficient Differentially Private Training with Gradient Random Projection (opens in a new tab)
- ICLR (T1)TNT: Improving Chunkwise Training for Test-Time Memorization (opens in a new tab)
202513 papers
- ICLR (T1)Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models (opens in a new tab)
- ICLR (T1)DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction (opens in a new tab)
- MLSPEfficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models (opens in a new tab)
- JMLR (T1)Four Axiomatic Characterizations of the Integrated Gradients Attribution Method (opens in a new tab)
- IEEE Trans. Intell. Transp. Syst.Incentive Systems for Fleets of New Mobility Services (opens in a new tab)
- NeurIPS (T1)Nested Learning: The Illusion of Deep Learning Architectures (opens in a new tab)
- AISTATS (T1)On the Inherent Privacy of Zeroth-Order Projected Gradient Descent (opens in a new tab)
- NeurIPS (T1)PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts (opens in a new tab)
- SIAM J. Control. Optim.Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators (opens in a new tab)
- J. Priv. ConfidentialityPrivate Stochastic Optimization with Large Worst-Case Lipschitz Parameter (opens in a new tab)
- ICML (T1)Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence (opens in a new tab)
- ICML (T1)Synthetic Text Generation for Training Large Language Models via Gradient Matching (opens in a new tab)
20245 papers
- NAACL (T1)Differentially Private Next-Token Prediction of Large Language Models (opens in a new tab)
- NeurIPS (T1)DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction (opens in a new tab)
- ICLR (T1)f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization (opens in a new tab)
- ICLR (T1)Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization (opens in a new tab)
- ICML (T1)Optimal Differentially Private Model Training with Public Data (opens in a new tab)
202310 papers
- ICML (T1)A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions (opens in a new tab)
- AISTATS (T1)Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes (opens in a new tab)
- ACCNoise amplifiation of momentum-based optimization algorithms (opens in a new tab)
- ICLR (T1)Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses (opens in a new tab)
- AISTATS (T1)Private Non-Convex Federated Learning Without a Trusted Server (opens in a new tab)
- ALTPrivate Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses (opens in a new tab)
- Trans. Mach. Learn. Res.RIFLE: Imputation and Robust Inference from Low Order Marginals (opens in a new tab)
- Trans. Mach. Learn. Res.Robustness through Data Augmentation Loss Consistency (opens in a new tab)
- ICLR (T1)Stochastic Differentially Private and Fair Learning (opens in a new tab)
- J. Glob. Optim.Zeroth-order algorithms for nonconvex-strongly-concave minimax problems with improved complexities (opens in a new tab)
Where alumni went10 listed, 60% to industry
Industry 6Academia 4
Also to
- NYU
Apply to USCFall 2027
Computer Science PhD (Viterbi)
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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