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 dimension can now be measured directly: how textually distinct a paper is from the work that came before it. Using semantic language models,...
We study the following class of matroid optimization problems with a linear constraint (P-MOL). Given a matroid M=(E,I), two weight functions $v,w:E\to R_{\ge 0}$, and a threshold $L\in R_{\ge 0}$, find $opt v(S)$ where S is either an independent set or a base of M satisfying a budget-type constraint: $w(S)\le L$ or $w...
Ilan Doron-Arad, H. Shachnai, Gilad Shmerler· 0 citations
Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recombination operators involved are qualitatively different from those studied classically. Mutations are no longer random; an ML algorithm mutates a solution with the goal o...
Anna M. Brandenberger, Ilan Doron-Arad, Elchanan Mossel· 0 citations
It is shown that stochastic autoregressive learning fundamentally differs from the deterministic theory, and that CoT learning at scale $\varepsilon$ is upper-bounded by base learning at scale $\varepsilon/M^2$, whereas e2e learning at scale $\varepsilon$ is upper-bounded, up to logarithmic factors, by $(M/\varepsilon)...
Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel· 1 citation
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