Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs. Unlike forms of regression that fit parameters assuming a fixed model structure, symbolic regression is a search problem over the space of expressions, represented, for examp...
Oussama Boussif, Mohammed Mahfoud, Younesse Kaddar et al.· 2 citations
KV-streams is proposed, a plug-and-play strategy compatible with any compaction strategy that substantially increases throughput while showing no evidence of hindering performance, and is shown to be an efficient and lightweight plug-and-play addition to any post-training pipeline.
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda et al.· 0 citations
This work investigates strategic diversity, or substantive variation among approaches to a problem, as an alternative principle for constructing self-training data with two sampling methods: GROOT, a new method which constructs a hierarchical tree of approaches and samples distinct paths, and Verbalized Sampling, adapt...
Alexander Gurung, Esmeralda S. Whitammer, Mirella Lapata· 0 citations
It is demonstrated that the Markovian assumption can both hamper signal propagation during training and catastrophically reduce the learned sampler's expressivity due to state aliasing.
Tiago da Silva, Esmeralda S. Whitammer, S. Lahlou· 0 citations
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