1 citations · 1 across the 16 of their papers we have counts for
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More of the same? Are scientific papers losing originality?
Ilan Doron-Arad, Elchanan Mossel
Scientific output is growing rapidly, but it is unclear whether the expanding literature remains original or is increasingly repeating itself. Originality has many dimensions. One…
Tight Approximation Results for Matroid Optimization with a Linear Constraint
Ilan Doron-Arad, Hadas Shachnai, Gilad Shmerler
We study the following class of matroid optimization problems with a linear constraint (P-MOL). Given a matroid M=(E,I), two weight functions , and a threshold…
A Tight Information-Theoretic Lower Bound for Randomized Online Set Cover
Ilan Doron-Arad, Joseph, Naor
Online set cover is a fundamental problem in online algorithms, admitting a deterministic -competitive algorithm, where is the number of sets and is the nu…
Stochastic Autoregressive Learning
Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel
Motivated by LLMs, which generate outputs by iteratively sampling from next-token distributions, we introduce a PAC-learning model for binary stochastic autoregressive learning. Th…
Incremental Dominating Set
Ilan Doron Arad, Jonathan Gal, Seffi Naor
Dominating Set is a fundamental problem in graph theory: given a graph, find a minimum-weight subset of vertices such that every vertex is either selected or adjacent to a selected…
Genetic Algorithms with Optimization Guided Operators
Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recombination operators involved ar…
A Theory of Online Learning with Autoregressive Chain-of-Thought Reasoning
Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel
Autoregressive generation lies at the heart of the mechanism of large language models. It can be viewed as the repeated application of a next-token generator: starting from an inpu…
Why ReLU? A Bit-Model Dichotomy for Deep Network Training
Ilan Doron-Arad, Elchanan Mossel
Theoretical analyses of Empirical Risk Minimization (ERM) are standardly framed within the Real-RAM model of computation. In this setting, training even simple neural networks is k…
Online Realizable Regression and Applications for ReLU Networks
Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel
Realizable online regression can behave very differently from online classification. Even without any margin or stochastic assumptions, realizability may enforce horizon-free (fini…