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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Approximation Algorithms for Budget Constrained Network Upgradeable Problems
Published in arXiv, 2014
TL;DR: We explore budget-constrained network upgradeable problems on undirected graphs, presenting algorithms for: 1. Finding a maximum weight constrained spanning tree within a budget, improving upon prior results. 2. Solving the longest path problem in a directed acyclic graph (DAG) with limited improvements, achieving efficient algorithms and approximations.
Recommended citation: Saharoy, Debjyoti, and Sandeep Sen. "Approximation algorithms for budget constrained network upgradeable problems." arXiv preprint arXiv:1412.3721 (2014). https://arxiv.org/abs/1412.3721
Universal Sets and Cover-Free Families
Published in arXiv, 2016
TL;DR: Polynomial time $(n, d)$-universal set over ${0,1}$, size improvement over Bshouty’s.
Recommended citation: Saharoy, Debjyoti, and Shailesh Vaya. "Universal Sets and Cover-Free Families." arXiv preprint arXiv:1606.01376 (2016). https://arxiv.org/abs/1606.01376
Learning Buyer Behavior under Realistic Pricing Restrictions
Published in ISAIM, 2018
TL;DR: We propose a new algorithm for learning buyer behavior parameters efficiently, adaptable to any price constraints set by the seller. This approach offers practical insights for profit-maximizing pricing strategies, inventory management, and promotion planning.
Recommended citation: Saharoy, Debjyoti, and Theja Tulabandhula. "An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions." arXiv preprint arXiv:1803.01968 (2018). https://isaim2018.cs.ou.edu/papers/ISAIM2018_ML_Saharoy_Tulabandhula.pdf
Dont Just Pay Attention, PLANT It
Published in Preprint, 2024
TL;DR: In Extreme Multi-Label Text Classification (XMTC), we train a standalone attention model outside the pipeline, integrate it, and fine-tune, enabling faster learning with less data. Specifically, we transfer transfer L2R models to fine-tune attention in XMTC for ICD coding
Recommended citation: Your Name, You. (2024). "Paper Title Number 1." Journal 1. 1(1). https://arxiv.org/pdf/2410.23066
talks
Don’t Just Pay Attention, PLANT It
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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