A novel feature selection method using a teacher-student network.
problem Feature selection for high-dimensional data in machine learning.
method Teacher-student network approach for feature selection.
result The proposed TSFS method outperforms existing methods in classification and clustering.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…
Proposes grade-aware course recommendation methods to improve student GPA.
problem Helping students select courses that lead to timely graduation and good grades.
method Two approaches: ranking courses by expected GPA impact and combining grade predictions with course recommendations.
result Grade-aware methods recommend courses leading to better student performance.
Proposes new models to predict student grades more accurately.
problem Accurately predicting future grades considering prior and concurrent courses.
method Context-aware, non-linear, and neural attentive models.
result Models outperform existing methods in predicting student grades.
Conditional T/S learning improves student model performance by selectively learning from teacher or ground truth.
problem Teacher's occasional wrong guidance leads to suboptimal student model performance.
method Proposes a conditional T/S learning scheme where the student selectively chooses between teacher and ground truth based on teacher correctness.
result The conditional learning achieves significant performance improvements over traditional T/S learning.
Develops a generic approach for stable model distillation.
problem Stability of student models in model distillation when data variability is present.
method Generic approach based on central limit theorem for average loss, multiple testing framework.
result Demonstrates selection of a corpus size for consistent student models across different pseudo samples.
Method teaches students without teachers, estimating true labels from crowdsourcing.
problem Teaching without access to true labels.
method Apply crowdsourcing techniques to estimate true labels and student models for iterative teaching.
result Teaching performance is particularly effective for low-level students.
Improved learning to reweight using deep interactions between student and teacher models.
problem Limitation of existing learning to reweight methods in utilizing student model's internal states.
method Proposes an algorithm that uses the student model's internal states to the teacher model, which returns adaptive weights to enhance student model training.
result Significant improvement over previous methods in image classification and neural machine translation experiments.
We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a G…
This work proposes a student-teacher network for predicting hospital admission locations.
problem Accurate prediction of hospital admission locations to optimize resource allocation.
method Reinforcement learning approach where a teacher network selects data batches for a student network.
result The approach outperforms state-of-the-art methods on tabular data and image recognition.
A new Heckman selection model uses a bivariate contaminated normal distribution for more accurate data analysis.
problem Sample selection biases in econometric data analysis.
method Introduces a Heckman selection model using a bivariate contaminated normal distribution and presents an efficient ECM algorithm for parameter estimation.
result The proposed model outperforms normal and Student's t counterparts in real data analysis and simulation studies.
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through b…
ReOPD uses pre-collected teacher trajectories to distill knowledge from multi-turn interactions.
problem The cost of fully online on-policy distillation for multi-turn interactions.
method ReOPD, an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes, addressing the prefix trap and distribution shift.
result ReOPD preserves or improves OPD-level accuracy, uses zero tool calls, and is at least 4imes faster per training step. MUTLA dataset analyzes multimodal data for teaching and learning analytics.
problem Lack of a comprehensive multimodal dataset for teaching and learning analytics.
method Presented a large-scale MUTLA dataset with synchronized multimodal data from SAIL.
result Provides insights for predicting student engagement and improving adaptive learning.
NAK model predicts student grades by focusing on relevant past courses.
problem Accurately predicting future course grades for students.
method NAK model learns attention weights to focus on relevant past courses.
result NAK model outperforms CKRM and other methods in predicting grades.
Method introduces topological regularization using information filtering networks.
problem Sparse probabilistic modeling and multicollinear regression.
method Topological regularization via information filtering network.
result Direct application to L0-norm regularized problems. These are lecture notes mainly aimed at graduate students on selected aspects of generalized geometry: in particular generalized complex and Kaehler structures and generalized holomorphic bundles. They are based on lectures given in March 2010 at the Chinese University of Hong Kong.
New metric MADD assesses fairness of predictive student models.
problem Predictive student models can be biased and unfair, leading to discrimination.
method Proposes MADD metric to analyze model's discriminatory behaviors.
result Fair predictive performance does not guarantee fair behaviors or outcomes.
For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits select…
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analytically intractable inference which is why several approximative methods have been proposed. The expectation propagation (EP) has been found …
One-pass SGD dynamics in overparameterized quadratic networks show slow escape from poor solutions.
problem Slow escape from poor generalization solutions in overparameterized neural networks.
method Analysis of one-pass SGD dynamics using ordinary differential equations for overlap matrices.
result Overparameterization only modestly accelerates escape from poor solutions.
This paper distills Bayesian posterior expectations for deep neural networks.
problem Improving deep neural network performance and uncertainty quantification.
method Develops a framework for distilling expectations from Bayesian posterior distributions using Monte Carlo samples.
result The framework successfully distills posterior predictive distribution and expected entropy.
A VB method for high-dimensional regression with student-t priors achieves nearly optimal performance and computational efficiency.
problem High-dimensional linear model inferences with heavy-tailed shrinkage priors.
method Variational Bayesian (VB) procedure for high-dimensional linear models with student-t priors.
result The VB method achieves nearly optimal contraction rate and computational efficiency, outperforming MCMC methods.
Lecture note for data science students on machine learning basics and advanced topics.
problem No specific problem stated; preparing students for advanced machine learning.
method Introduction of basic machine learning concepts and advanced topics.
result Students are prepared to study advanced machine learning topics.
As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one often needs approximation inference if distribution assumption is beyond Gaussian distribution. In this paper, we propose a Bayesian linear reg…
Unified framework for image classification and regression using cGAN-generated samples.
problem Lack of unified KD methods for both classification and regression tasks.
method cGAN-KD framework based on cGANs.
result Unified framework for both classification and regression tasks, compatible with other KD methods.
The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.
problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.
A new framework for deep learning from multiple experts tackles long-tailed data issues.
problem Training deep networks on imbalanced data distributions.
method Learning From Multiple Experts (LFME) framework, involving self-paced expert selection and curriculum instance selection.
result LFME achieves superior performance compared to state-of-the-art methods.
Predicts student performance in interactive online question pools using GNNs.
problem Predicting student performance in interactive online question pools with evolving knowledge.
method Proposes R^2GCN, a GNN model for heterogeneous networks to predict student performance.
result Achieves higher accuracy in student performance prediction than traditional methods.
Proposes RaT to mitigate bias in student-teacher estimation.
problem Systematic bias in teacher's predictions propagates to student model.
method Uses teacher to estimate residuals in student's predictions.
result RaT method reduces teacher bias effect and achieves optimal rate.
The high level of attrition and low rate of certification in Massive Open Online Courses (MOOCs) has prompted a great deal of research. Prior researchers have focused on predicting dropout based upon behavioral features such as student confusion, click-stream patterns, and social interactions. However, few studies have…
Analyzed writing style changes in Danish high school students.
problem Detecting global development trends and identifying at-risk students in high school writing.
method Used a Siamese neural network to compute essay similarity and clustered student profiles.
result High school students' writing styles become less similar as they progress, with some students showing significant improvement and others limited development or setbacks.
Enhances student diversity in collaborative learning.
problem Student homogenization in large groups.
method Random routing, diverse feature sets, and random subgroup imitation.
result Significantly outperforms state-of-the-art approaches.
Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature sele…
Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual i…
Hidden Markov Model predicts student performance in educational games.
problem Estimating student knowledge progression in educational games.
method Time series analysis using hidden Markov model with game features.
result State trajectories accurately predict student performance.
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model stud…
Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable…
Student-teacher learning improves generalization with noisy inputs.
problem Transfer knowledge from clean inputs to noisy inputs.
method Analyzes student-teacher learning using deep linear networks and experiments with nonlinear networks.
result Three factors are vital for success: zero training loss, teacher knowledge, and feature decomposition.
Predicts student outcomes in real-time using domain adaptation.
problem Real-time student performance prediction in online courses.
method GritNet architecture with unsupervised domain adaptation.
result GritNet enhances real-time predictions, especially in early weeks.
A new teacher-class network method compresses DNNs by distributing knowledge to multiple student networks.
problem Overwhelming size of Deep Neural Networks (DNNs).
method Single teacher with multiple student networks, transferring knowledge to each student.
result The combined knowledge of the class of students achieves better performance and reduces parameters.
A new KD model for collaborative filtering improves top-N recommendation performance.
problem Challenges in applying KD to recommender models due to feedback sparsity and ambiguity.
method Proposes a new KD model (CD) for collaborative filtering, reformulating a loss function, using probabilistic rank-aware sampling, and developing training strategies.
result Outperforms state-of-the-art methods by 2.7-33.2% in hit rate (HR) and 2.7-29.1% in NDCG.
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality…
Bayesian model identifies skill difficulties and student subgroups in engineering education.
problem Identifying and supporting diverse student needs in entry-level university engineering modules.
method Hierarchical Bayesian modeling of student response data.
result Clear patterns of skill mastery and distinct student subgroups identified.
Improves convergence speed in compressive sensing with a new probabilistic approach.
problem Efficiently solving the best subset selection problem in compressive sensing.
method Smooth probabilistic reformulation of ℓ0 regularized regression. result Empirically outperforms existing compressive sensing algorithms across various settings.
Improved ImageNet classification with semi-supervised learning.
problem Image classification with limited labeled data.
method Noisy Student Training: semi-supervised learning with noisy student models.
result 88.4% top-1 accuracy on ImageNet, 2.0% better than state-of-the-art.
Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.
Study shows tuition fees in Germany reduce enrollment by up to 4.5%
problem Impact of German university tuition fees on enrollment rates
method Robust model selection and design-based inference in high-dimensions
result Significant negative impact of up to 4.5 percentage points on enrollment