About Me

I am a PhD student in Electrical & Computer Engineering at Georgia Tech, advised by Prof. Mark Davenport. My areas of research include active learning and learning from similarity comparisons.

Current Research

My research focuses on developing methods for learning data efficient models based on learning from similarity comparisons and active learning. The motivation behind using comparisons comes from the idea that it is easier to compare differentdata samples than evaluate every sample on an absolute basis. Active learning aims to reduce the label complexity of tasks by selecting the most informative samples to be labeled.

I’m interested in leveraging similarity comparisons to motivate task agnostic active learning schemes and also designing active methods to select labels while considering the bias influencing human responses. I’m interested in understanding how the problem considered and the modality of the data affect the design of active data selection methods. To study the performance of these methods, I have worked on problems such as metric learning - which involves learning a representation of objects via comparisons of distances between them, classification - where label acquisition is formulated as a problem of soliciting responses to similarity queries and preference learning - where the objective is to estimate a user based on their item preferences.

Publications

N. Nadagouda, A. Xu and M. Davenport, “Active metric learning and classification using similarity queries”, in Uncertainty in Artificial Intelligence (UAI), August 2023. Also presented at Human in the Loop Learning Workshop, Neural Information Processing Systems (NeurIPS), December 2022.

A. McRae, A. Xu, J. Jin, N. Nadagouda, N. Ahad, P. Guan, S. Karnik, and M. Davenport, “Delta Distancing: A Lifting Approach to Localizing Items From User Comparisons”, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), May 2022.

N. Nadagouda and M. Davenport, “Switched Hawkes Processes”, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), June, 2021.

G. Canal, M. Connor, J. Jin, N. Nadagouda, M. O’Shaughnessy, C. Rozell, and M. Davenport, “The Picasso Algorithm for Bayesian Localization Via Paired Comparisons in a Union of Subspaces Model”, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), May, 2020.

Abstracts

N. Nadagouda and M. Davenport, “Active query synthesis for preference learning”, at Women in Machine Learning Workshop, Neural Information Processing Systems (NeurIPS), December 2023.

N. Ahad, N. Nadagouda, E. Dyer and M. Davenport, “Active learning for time instant classification”, at Data-centric Machine Learning Research Workshop, International Conference on Machine Learning (ICML), July 2023.

Y. Teng, A. Mamuye, E. Mo, K. Zhu, R. Walker, N. Nadagouda, and M. Davenport, “Range-Only Simultaneous Localization and Mapping using Paired Comparisons”, at IEEE Annual Conf. on RFID, April 2021.

N. Nadagouda, and M. Davenport, “Switched Hawkes Processes”, at Workshop on Recent Developments on Mathematical/Statistical approaches in Data Science (MSDAS), University of Texas at Dallas, June 2019.

Article

N. Nadagouda, Journey of a researcher: Finding pleasure in the pathless woods, American Ceramic Society Bulletin, Student Perspectives, June/July 2020.

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