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Aritra Ghosh

A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing

May 11, 2023
Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew Lan

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* 16th International Conference on Educational Data Mining (EDM 2023)

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Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Dec 20, 2022
Chuan Tian, C. Megan Urry, Aritra Ghosh, Ryan Ofman, Tonima Tasnim Ananna, Connor Auge, Nico Cappelluti, Meredith C. Powell, David B. Sanders, Kevin Schawinski, Dominic Stark, Grant R. Tremblay

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* Accepted for publication in The Astrophysical Journal. 35 Pages. 25 Figures

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Automated Scoring for Reading Comprehension via In-context BERT Tuning

May 19, 2022
Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard Baraniuk, Andrew Lan

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* Published as a conference paper at AIED 2022. A grand prize-winner for the NAEP AS Challenge. Code available at: https://github.com/ni9elf/automated-scoring

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DiPS: Differentiable Policy for Sketching in Recommender Systems

Dec 08, 2021
Aritra Ghosh, Saayan Mitra, Andrew Lan

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* AAAI 2022 with supplementary material

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BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

Aug 17, 2021
Aritra Ghosh, Andrew Lan

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* IJCAI 2021 with supplementary material

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Do We Really Need Gold Samples for Sample Weighting Under Label Noise?

Apr 19, 2021
Aritra Ghosh, Andrew Lan

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* WACV 2021

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Option Tracing: Beyond Correctness Analysis in Knowledge Tracing

Apr 19, 2021
Aritra Ghosh, Jay Raspat, Andrew Lan

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* AIED 2021

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Contrastive Learning Improves Model Robustness Under Label Noise

Apr 19, 2021
Aritra Ghosh, Andrew Lan

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* Learning from Limited or Imperfect Data (L^2ID) Workshop @ CVPR 2021

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Context-Aware Attentive Knowledge Tracing

Jul 24, 2020
Aritra Ghosh, Neil Heffernan, Andrew S. Lan

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* Published in KDD 2020

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Optimal Bidding Strategy without Exploration in Real-time Bidding

Mar 31, 2020
Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Viswanathan Swaminathan

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* SIAM SDM 2020. Added supplementary material

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