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Published in ACS Nano, 2015
We report an experimental measurement of the acoustic signal emitted from an individual suspended carbon nanotube (CNT) approximate 2 μm in length, 1 nm in diameter, and 10^–21 kg in mass.
Recommended citation: Mason, B. J., Chang, S. W., Chen, J., Cronin, S. B., & Bushmaker, A. W. (2015). Thermoacoustic transduction in individual suspended carbon nanotubes. ACS nano, 9(5), 5372-5376. https://pubs.acs.org/doi/full/10.1021/acsnano.5b01119
Published in International Educational Data Mining Society, 2016
In this paper, we develop a model of how undergraduate chemistry students perceive images of molecules.
Recommended citation: Rau, M. A., Mason, B., & Nowak, R. (2016). How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations. International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED592702.pdf
Published in Neural Information Processing Systems, 2017
This paper investigates the theoretical foundations of metric learning.
Recommended citation: Mason, B., Jain, L., & Nowak, R. (2017). Learning low-dimensional metrics. Advances in neural information processing systems, 30. https://proceedings.neurips.cc/paper/2017/file/f12ee9734e1edf70ed02d9829018b3d9-Paper.pdf
Published in Cognitive Science, 2018
We describe how a novel machine learning technique can generate an optimal sequence of perceptual fluency problems to improve online education.
Recommended citation: Sen, A., Patel, P., Rau, M. A., Mason, B., Nowak, R., Rogers, T. T., & Zhu, J. (2018, January). For Teaching Perceptual Fluency, Machines Beat Human Experts. In CogSci http://pages.cs.wisc.edu/~jerryzhu/pub/cogsci18.pdf
Published in PAA 2018 Annual Meeting, 2018
In this paper, we study the impact of socioeconomic variability on human fecundity.
Recommended citation: Nobles, J., Hamoudi, A., Nowak, R., Landau, E., Baron, A., Brittingham, J., & Mason, B. (2018, April). Socioeconomic Variability in Human Fecundity. In PAA 2018 Annual Meeting. PAA. https://paa.confex.com/paa/2018/mediafile/ExtendedAbstract/Paper23959/paa2018_Fecundity.pdf
Published in International Educational Data Mining Society, 2018
Here, we describe a novel educational data mining approach that uses machine learning to generate an optimal sequence of visuals for perceptual-fluency problems.
Recommended citation: Sen, A., Patel, P., Rau, M. A., Mason, B., Nowak, R., Rogers, T. T., & Zhu, X. (2018). Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice. International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED593113.pdf
Published in Cognitive Science, 2019
This paper provides the first method to assess students’ perceptual competencies implicitly, without requiring verbalization or assuming explicit visual attention. These findings have implications for the design of instructional interventions that help students acquire perceptual representational competencies.
Recommended citation: Mason, B., Rau, M. A., & Nowak, R. (2019). Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods. Cognitive science, 43(9), e12744. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cogs.12744
Published in Neural Information Processing Systems, 2019
We consider the problem of actively learning the nearest neighbor graph of a dataset of n items in as few queries as possible.
Recommended citation: Mason, B., Tripathy, A., & Nowak, R. (2019). Learning nearest neighbor graphs from noisy distance samples. Advances in Neural Information Processing Systems, 32. https://proceedings.neurips.cc/paper/2019/file/98c56bce74669e2e4e7a9fc1caa8c326-Paper.pdf
Published in Neural Information Processing Systems, 2020
In this paper, we introduce the all-epsilon-good identification problem in multi-armed bandits.
Recommended citation: Mason, B., Jain, L., Tripathy, A., & Nowak, R. (2020). Finding all $\epsilon $-good arms in stochastic bandits. Advances in Neural Information Processing Systems, 33, 20707-20718. https://proceedings.neurips.cc/paper/2020/file/edf0320adc8658b25ca26be5351b6c4a-Paper.pdf
Published in International Conference on Machine Learning, 2021
In this paper, we propose improved fixed-design confidence bounds for the linear logistic model. With this bound we propose a new regret minimization and a new pure exploration bandit algorithm.
Recommended citation: Jun, K. S., Jain, L., Mason, B., & Nassif, H. (2021, July). Improved confidence bounds for the linear logistic model and applications to bandits. In International Conference on Machine Learning (pp. 5148-5157). PMLR. http://proceedings.mlr.press/v139/jun21a/jun21a.pdf
Published in arXiv preprint arXiv:2110.04945 - (To appear at ICASSP 2022), 2021
We develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel.
Recommended citation: Alemohammad, S., Babaei, H., Barberan, C. J., Liu, N., Luzi, L., Mason, B., & Baraniuk, R. G. (2021). NFT-K: Non-Fungible Tangent Kernels. arXiv preprint arXiv:2110.04945. https://arxiv.org/pdf/2110.04945
Published in Uncertainty In Artificial Intelligence, 2021
This paper shows how ideas from cover trees and multi-armed bandits can be leveraged to develop an neariest neighbor search algorithm from noisy data that has optimal dependence on the dataset size and the (unknown) geometry of the dataset.
Recommended citation: Mason, B., Tripathy, A., & Nowak, R. (2021, December). Nearest neighbor search under uncertainty. In Uncertainty in Artificial Intelligence (pp. 1777-1786). PMLR. https://proceedings.mlr.press/v161/mason21a/mason21a.pdf
Published in Neural Information Processing Systems, 2021
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification.
Recommended citation: Katz-Samuels, J., Mason, B., Jamieson, K. G., & Nowak, R. (2021). Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. Advances in Neural Information Processing Systems, 34. https://proceedings.neurips.cc/paper/2021/file/94aada62f90dd50a84ca74304563d5db-Paper.pdf
Published in arXiv preprint arXiv:2202.01243, 2022
In this paper, we study an underexplored hidden cost of overparameterization: the fact that overparameterized models are more vulnerable to privacy attacks, in particular the membership inference attack that predicts the (potentially sensitive) examples used to train a model.
Recommended citation: Tan, J., Mason, B., Javadi, H., & Baraniuk, R. G. (2022). Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. arXiv preprint arXiv:2202.01243. https://arxiv.org/pdf/2202.01243
Published in Artifical Intelligence and Statistics (AIStats), 2022
This paper provides the first instance-dependent, non-asymptotic upper bounds on sample complexity of level-set estimation that match information theoretic lower bounds.
Recommended citation: Mason, B., Camilleri, R., Mukherjee, S., Jamieson, K., Nowak, R., & Jain, L. (2021). Nearly Optimal Algorithms for Level Set Estimation. arXiv preprint arXiv:2111.01768. (To appear at AIStats, 2022) https://arxiv.org/pdf/2111.01768
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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