224 citations · 410 across the 13 of their papers we have counts for
14 papers
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Wanyong Feng, Aritra Ghosh, Stephen Sireci +1
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length. Bilevel optimization-based CAT…
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee +3
In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different ski…
Subconvexity for twists of Rankin-Selberg -functions
Aritra Ghosh
Let and be two holomorphic or Hecke-Maass primitive cusp forms for and be a primitive Dirichlet character of modulus , an odd prime. A subconvex b…
Using Machine Learning to Determine Morphologies of AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
Chuan Tian, C. Megan Urry, Aritra Ghosh +9
We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within . We first use PSFGAN to decouple host ga…
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Aritra Ghosh, C. Megan Urry, Aayush Mishra +10
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for million galaxies in the Hyper Supr…
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Nigel Fernandez, Aritra Ghosh, Naiming Liu +4
Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual represe…
DiPS: Differentiable Policy for Sketching in Recommender Systems
Aritra Ghosh, Saayan Mitra, Andrew Lan
In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that the…
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
Aritra Ghosh, Andrew Lan
Learning with labels noise has gained significant traction recently due to the sensitivity of deep neural networks under label noise under common loss functions. Losses that are th…
Context-Aware Attentive Knowledge Tracing
Aritra Ghosh, Neil Heffernan, Andrew S. Lan
Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flex…
Optimal Bidding Strategy without Exploration in Real-time Bidding
Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel +1
Maximizing utility with a budget constraint is the primary goal for advertisers in real-time bidding (RTB) systems. The policy maximizing the utility is referred to as the optimal…
Scalable Bid Landscape Forecasting in Real-time Bidding
Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel +3
In programmatic advertising, ad slots are usually sold using second-price (SP) auctions in real-time. The highest bidding advertiser wins but pays only the second-highest bid (know…
Robust Loss Functions under Label Noise for Deep Neural Networks
Aritra Ghosh, Himanshu Kumar, P. S. Sastry
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particu…
Making Risk Minimization Tolerant to Label Noise
Aritra Ghosh, Naresh Manwani, P. S. Sastry
In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in presence…