150 citations · 150 across the 3 of their papers we have counts for
5 papers
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…