Information Theoretic Bounds on Optimal Worst-case Error in Binary Mixture Identification. - Khashayar Gatmiry; Seyed Abolfazl Motahari (CoRR, 2018)

Testing Determinantal Point Processes. - Khashayar Gatmiry; Maryam Aliakbarpour; Stefanie Jegelka (NeurIPS, 2020)

Optimization and Adaptive Generalization of Three layer Neural Networks. - Khashayar Gatmiry; Stefanie Jegelka; Jonathan A. Kelner (ICLR, 2022)

On the generalization of learning algorithms that do not converge. - Nisha Chandramoorthy; Andreas Loukas; Khashayar Gatmiry; Stefanie Jegelka (NeurIPS, 2022)

Convergence of the Riemannian Langevin Algorithm. - Khashayar Gatmiry; Santosh S. Vempala (CoRR, 2022)

Optimal algorithms for group distributionally robust optimization and beyond. - Tasuku Soma; Khashayar Gatmiry; Stefanie Jegelka (CoRR, 2022)

The Network Visibility Problem. - Khashayar Gatmiry; Manuel Gomez-Rodriguez (ACM Trans. Inf. Syst., 2022)

Quasi-Newton Steps for Efficient Online Exp-Concave Optimization. - Zakaria Mhammedi; Khashayar Gatmiry (COLT, 2023)

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models. - Khashayar Gatmiry; Zhiyuan Li 005; Tengyu Ma 001; Sashank J. Reddi; Stefanie Jegelka; Ching-Yao Chuang (NeurIPS, 2023)

Projection-Free Online Convex Optimization via Efficient Newton Iterations. - Khashayar Gatmiry; Zakaria Mhammedi (NeurIPS, 2023)

Sampling with Barriers: Faster Mixing via Lewis Weights. - Khashayar Gatmiry; Jonathan A. Kelner; Santosh S. Vempala (CoRR, 2023)

A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry. - Yuansi Chen; Khashayar Gatmiry (CoRR, 2023)

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm? - Yuansi Chen; Khashayar Gatmiry (CoRR, 2023)

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization. - Khashayar Gatmiry; Zhiyuan Li 005; Ching-Yao Chuang; Sashank J. Reddi; Tengyu Ma 001; Stefanie Jegelka (CoRR, 2023)

A Unified Approach to Controlling Implicit Regularization via Mirror Descent. - Haoyuan Sun; Khashayar Gatmiry; Kwangjun Ahn; Navid Azizan (J. Mach. Learn. Res., 2023)

EM for Mixture of Linear Regression with Clustered Data. - Amirhossein Reisizadeh; Khashayar Gatmiry; Asuman E. Ozdaglar (AISTATS, 2024)

Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier. - Khashayar Gatmiry; Jonathan A. Kelner; Santosh S. Vempala (COLT, 2024)

Adversarial Online Learning with Temporal Feedback Graphs. - Khashayar Gatmiry; Jon Schneider (COLT, 2024)

Simplicity Bias via Global Convergence of Sharpness Minimization. - Khashayar Gatmiry; Zhiyuan Li 005; Sashank J. Reddi; Stefanie Jegelka (ICML, 2024)

Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (ICML, 2024)

What does guidance do? A fine-grained analysis in a simple setting. - Muthu Chidambaram; Khashayar Gatmiry; Sitan Chen; Holden Lee; Jianfeng Lu 001 (NeurIPS, 2024)

Bandit Algorithms for Prophet Inequality and Pandora's Box. - Khashayar Gatmiry; Thomas Kesselheim; Sahil Singla 001; Yifan Wang (SODA, 2024)

On the Role of Depth and Looping for In-Context Learning with Task Diversity. - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (CoRR, 2024)

Learning Mixtures of Gaussians Using Diffusion Models. - Khashayar Gatmiry; Jonathan A. Kelner; Holden Lee (COLT, 2025)

Computing Optimal Regularizers for Online Linear Optimization. - Khashayar Gatmiry; Jon Schneider; Stefanie Jegelka (COLT, 2025)

Rethinking Invariance in In-context Learning. - Lizhe Fang; Yifei Wang 001; Khashayar Gatmiry; Lei Fang; Yisen Wang 001 (ICLR, 2025)