Publications associées à When Is Amplification Necessary for Composition in Randomized Query Complexity?

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Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

When Is Amplification Necessary for Composition in Randomized Query Complexity?

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Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

The Cost of Scaling a Reliable Interconnection Topology

Cognitive workload monitoring in virtual reality based rescue missions with drones

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Multi-armed Bandits in Action

A Spectral Algorithm for 3-valued Function Equivalence Classification

New insights on Drosophila antimicrobial peptide function in host defense and beyond

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Incorporating Deep Bagging Ensemble Method as a Surrogate Model for Simulating Hyper-Concentrated Sediment-Laden Flows