41 citations · 151 across the 14 of their papers we have counts for
13 papers · 1 filter
EXAQ: Exponent Aware Quantization For LLMs Acceleration
Moran Shkolnik, Maxim Fishman, Brian Chmiel +3
Quantization has established itself as the primary approach for decreasing the computational and storage expenses associated with Large Language Models (LLMs) inference. The majori…
Robust Linear Regression for General Feature Distribution
Tom Norman, Nir Weinberger, Kfir Y. Levy
We investigate robust linear regression where data may be contaminated by an oblivious adversary, i.e., an adversary than may know the data distribution but is otherwise oblivious…
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays
Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster +1
We consider stochastic convex optimization problems, where several machines act asynchronously in parallel while sharing a common memory. We propose a robust training method for th…
Generative Minimization Networks: Training GANs Without Competition
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy +2
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative mode…
Adaptive Sampling for Stochastic Risk-Averse Learning
Sebastian Curi, Kfir. Y. Levy, Stefanie Jegelka +1
In high-stakes machine learning applications, it is crucial to not only perform well on average, but also when restricted to difficult examples. To address this, we consider the pr…
Online Variance Reduction with Mixtures
Zalán Borsos, Sebastian Curi, Kfir Y. Levy +1
Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction. In this work, we propose a new framewo…