1 citations · 2 across the 7 of their papers we have counts for
8 papers
Large-Scale Regularized Matching on GPU Clusters
Aida Rahmattalabi, Gregory Dexter, Sanjana Garg +5
Production decision systems such as ad allocation or content matching involve millions of users and thousands of items, reducing to large-scale linear programs with sparse block-di…
DuaLip-GPU Technical Report
Gregory Dexter, Aida Rahmattalabi, Sanjana Garg +6
Large-scale linear programs (LPs) arise in many decision systems, including ranking, allocation, and matching problems that must be solved repeatedly at massive scale. Prior work s…
Effective Quantization of Muon Optimizer States
Aman Gupta, Rafael Celente, Abhishek Shivanna +7
The Muon optimizer, based on matrix orthogonalization, has recently shown faster convergence and better computational efficiency over AdamW in LLM pre-training. However, the memory…
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song +17
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Al…
LLM Query Scheduling with Prefix Reuse and Latency Constraints
Gregory Dexter, Shao Tang, Ata Fatahi Baarzi +3
The efficient deployment of large language models (LLMs) in online settings requires optimizing inference performance under stringent latency constraints, particularly the time-to-…
The Space Complexity of Approximating Logistic Loss
Gregory Dexter, Petros Drineas, Rajiv Khanna
We provide space complexity lower bounds for data structures that approximate logistic loss up to -relative error on a logistic regression problem with data $\mathbf{X} \in \mat…
A Precise Characterization of SGD Stability Using Loss Surface Geometry
Gregory Dexter, Borja Ocejo, Sathiya Keerthi +3
Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent…
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…