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 an asynchronous consensus dynamics on $N$ agents: at each step a uniformly chosen agent replaces its state by $f(Y_1,\dots,Y_r)$, where $f$ is a fixed monotone aggregation rule and $Y_1,\dots,Y_r$ are the states of $r$ agents sampled uniformly with replacement. Let $T$ be the first time at which all agents agr...
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
The query complexity of recovering a planted assignment from a random constraint-satisfaction instance with one-sided noise is studied, and bounds for nonadaptive algorithms are proved and adaptivity gives a factor $\exp(\Theta(k))$ improvement.
Cassandra Marcussen, Elchanan Mossel, Colin Sandon· 0 citations
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