{\rtf1\ansi\deff0\deftab360 {\fonttbl {\f0\fswiss\fcharset0 Arial} {\f1\froman\fcharset0 Times New Roman} {\f2\fswiss\fcharset0 Verdana} {\f3\froman\fcharset2 Symbol} } {\colortbl; \red0\green0\blue0; } {\info {\author Biblio 7.x}{\operator }{\title Biblio RTF Export}} \f1\fs24 \paperw11907\paperh16839 \pgncont\pgndec\pgnstarts1\pgnrestart B. J. Weber, Kalantre, S. S., McJunkin, T., Taylor, J. M., and Zwolak, J. P., ?Theoretical bounds on data requirements for the ray-based classification?, SN Comput. Sci., vol. 3, no. 57, 2022.\par \par J. Ziegler, McJunkin, T., Joseph, E. S., Kalantre, S. S., Harpt, B., Savage, D. E., Lagally, M. G., Eriksson, M. A., Taylor, J. M., and Zwolak, J. P., ?Toward Robust Autotuning of Noisy Quantum dot Devices?, Physical Review Applied, vol. 17, 2022.\par \par J. P. Zwolak, McJunkin, T., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., and Taylor, J. M., ?Ray-based framework for state identification in quantum dot devices?, PRX Quantum, vol. 2, no. 020335, 2021.\par \par J. P. Zwolak, McJunkin, T., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., Lagally, M. G., Coppersmith, S. N., Eriksson, M. A., and Taylor, J. M., ?Auto-tuning of double dot devices in situ with machine learning?, Phys. Rev. Applied , vol. 13, no. 034075 , 2020.\par \par J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., ?Ray-based classification framework for high-dimensional data?, Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada, 2020.\par \par J. P. Zwolak, Kalantre, S. S., Wu, X., Ragole, S., and Taylor, J. M., ?QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments?, PLOS ONE, vol. 13, no. 10, p. e0205844, 2018.\par \par S. S. Kalantre, Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., and Taylor, J. M., ?Machine Learning techniques for state recognition and auto-tuning in quantum dots?, 2017.\par \par }