35 citations · 73 across the 6 of their papers we have counts for
6 papers
RedEx: Beyond Fixed Representation Methods via Convex Optimization
Amit Daniely, Mariano Schain, Gilad Yehudai
Optimizing Neural networks is a difficult task which is still not well understood. On the other hand, fixed representation methods such as kernels and random features have provable…
Locally Optimal Descent for Dynamic Stepsize Scheduling
Gilad Yehudai, Alon Cohen, Amit Daniely +3
We introduce a novel dynamic learning-rate scheduling scheme grounded in theory with the goal of simplifying the manual and time-consuming tuning of schedules in practice. Our appr…
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…
Adversarial Robustness of Streaming Algorithms through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias +3
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more g…
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Alon Cohen, Amit Daniely, Yoel Drori +2
We consider stochastic optimization with delayed gradients where, at each time step , the algorithm makes an update using a stale stochastic gradient from step for som…
Scalable Learning of Non-Decomposable Objectives
Elad ET. Eban, Mariano Schain, Alan Mackey +3
Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given…