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Found 17 papers, 12 papers with code
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length.
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
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 skills from real-world student response data.
Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude.
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully-designed input structure to provide contextual information on each item.
DiPS: Differentiable Policy for Sketching in Recommender Systems
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 they are likely to interact with.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker.
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
Consequently, several recently proposed methods, such as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to learn a weighting function that downweights samples that are likely to have corrupted labels under the meta-learning framework.
Contrastive Learning Improves Model Robustness Under Label Noise
One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise, or re-weight training samples and down-weight those with higher loss values.
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Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions.
Option Tracing: Beyond Binary Knowledge Tracing
This paper details our solutions to Tasks 1&2 of the NeurIPS 2020 Education Challenge. 1 Knowledge tracing, a family of methods to estimate each student’s mastery levels on skills/knowledge components from their past responses to assessment questions, is useful for progress monitoring, personalization, and helping teachers to deliver personalized and targeted feedback to students to improve their learning outcomes.
Context-Aware Attentive Knowledge Tracing
We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.
Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at $z\sim0$ and $z\sim1$.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Optimal Bidding Strategy without Exploration in Real-time Bidding
Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.
Scalable Bid Landscape Forecasting in Real-time Bidding
The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).
Robust Loss Functions under Label Noise for Deep Neural Networks
For binary classification there exist theoretical results on loss functions that are robust to label noise.
On the Robustness of Decision Tree Learning under Label Noise
In most practical problems of classifier learning, the training data suffers from the label noise.
Making Risk Minimization Tolerant to Label Noise
Through extensive empirical studies, we show that risk minimization under the $0-1$ loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.
