Paper connects Sharpe ratio and Student t-statistic, providing exact distribution and asymptotic behavior.
problem Error-prone Sharpe ratio due to statistical estimation of expected returns and volatilities.
method Derive exact distribution of Sharpe ratio for independent normally distributed returns, extend to AR(1) assumptions.
result Empirical Sharpe ratio is asymptotically optimal and achieves Cramer Rao bound.
A new family of nonparametric statistics, the r-statistics, is introduced. It consists of counting the number of records of the cumulative sum of the sample. The single-sample r-statistic is almost as powerful as Student's t-statistic for Gaussian and uniformly distributed variables, and more powerful than the sign and…
New framework tests mean-variance spanning in high dimensions.
problem Testing mean-variance spanning in high-dimensional asset spaces.
method Robust Student-t statistic based on batch-mean method, combined using Cauchy combination test.
result Advantages of diversification vary by economic conditions and cross-country.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
The paper improves DR estimators for off-policy evaluation in contextual bandits.
problem Challenges in evaluating policies with adaptively collected data.
method Adaptive weighting to control variance in DR estimators.
result Improved t-statistic based on the estimator is asymptotically normal.
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.
This paper improves student networks to be more robust against perturbations.
problem Improving robustness of lightweight student networks.
method The paper introduces a method to make student networks more confident and robust by leveraging teacher networks.
result The proposed method enhances the robustness of student networks without sacrificing accuracy.
Developed predictive models for improving programming course performance.
problem Improving student performance in programming courses.
method Used M5P Decision Tree and Linear Regression Classifier on structured questionnaire data.
result Variable-based LRC model produced the best model with least evaluation metrics.
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.
We propose a 4-factor model for overnight returns and give explicit definitions of our 4 factors. Long horizon fundamental factors such as value and growth lack predictive power for overnight (or similar short horizon) returns and are not included. All 4 factors are constructed based on intraday price and volume data a…
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.
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…
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.
The Open University studies student online behavior in virtual learning environments.
problem Improving retention rates in online modules.
method GUHA and Markov chain-based analysis of student activity.
result Both methods are valid for modeling student activities.
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.
Real-time student performance prediction without historical data.
problem Predict student performance in real-time with limited historical data.
method Domain adaptation framework using GritNet architecture with unsupervised transfer learning.
result GritNet generalizes well across different courses and enhances predictions in the early stages.
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.
Detects student engagement states using unobtrusive appearance, context, and mouse data.
problem Detecting student engagement states in a classroom setting.
method Multimodal approach combining appearance, context, and mouse data; classifiers are fused at the decision level.
result Effective detection of student engagement states in a classroom setting.
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.
Proposes KTAN for better training of student networks with both intermediate representations and probability distributions.
problem Reduces large computation and storage cost of deep networks by transferring generalization ability.
method Holistically considers intermediate representations and probability distributions; uses a Teacher-to-Student layer and adversarial learning.
result Significantly improves performance of student networks on image classification and object detection tasks.
Estimates model performance from compute budget for distillation.
problem Risk mitigation in large-scale distillation.
method Distillation scaling law based on compute budget allocation.
result Maximizes student performance with compute-optimal allocation.
Distilled models often fail to match teacher models, despite improving generalization.
problem The discrepancy between teacher and student predictive distributions remains large.
method Investigated the optimization difficulties and dataset details affecting student performance.
result Optimizing for matching the teacher does not always lead to better generalization.
Publication bias skews asset pricing research findings.
problem Bias in sharing and publishing research findings.
method Meta-studies and empirical Bayes corrections.
result Publication bias effects are minimal and not dominant.
A new method for student-initiated action advice using novelty detection.
problem Exploration and sample inefficiency in RL, especially with teacher absence.
method Random Network Distillation (RND) to measure advice novelty, updates only for advised states.
result Significant performance improvement over state-of-the-art methods, especially in challenging scenarios.
Improved image reconstruction using VAEs with Student's t-prior.
problem Improving the robustness of VAEs in image reconstruction.
method Proposed a VAE with Student's t-distribution as prior, trained all distribution parameters.
result Better image reconstruction achieved with Student's t-prior compared to Gaussian priors.
Explains Cartan geometries for graduate students.
problem None explicitly stated, focuses on definition.
method Definition and explanation.
result Defines Cartan geometries for a specific audience.
Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the assoc…
Raising statistical hurdles may not be justified due to data bias.
problem Data bias leads to unobserved results that weaken identification of revised hurdles.
method Theoretical and empirical analysis of statistical hurdles and data bias.
result Statistics targeting only published findings can be strongly identified.
Study predicts academic achievement using students' support networks.
problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, …
This paper proposes a method to embed teacher knowledge into a student network without increasing parameters.
problem The need for portable neural networks on mobile devices with limited resources.
method Feature embedding approach to distill knowledge from a teacher network to a student network without introducing new parameters.
result The proposed method maintains the performance of the teacher network while significantly reducing computational and storage complexity.
A new distillation method transfers channel information from teacher to student.
problem Transfer knowledge from teacher to student with fewer parameters and calculations.
method Channel Distillation (CD) and Guided Knowledge Distillation (GKD) with loss decay.
result Achieved 27.68% top-1 error on ImageNet with ResNet18, outperforming state-of-the-art methods.
Pea-KD improves BERT student models by 4.4% on average in GLUE tasks.
problem Efficiently compressing BERT while maintaining performance.
method Parameter-efficient and accurate Knowledge Distillation (Pea-KD) with Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP).
result Pea-KD improves student model's performance by 4.4% on average in four GLUE tasks.
Two-layer ReLU networks outperform kernel methods in teacher-student settings.
problem Understanding the excess risk of two-layer ReLU neural networks in teacher-student models.
method Investigated a two-phase training process for a student network, comparing it to kernel methods.
result The student network reaches near-global optimality and outperforms kernel methods in minimax optimal rate.
Study analyzes online student behavior patterns using log data.
problem Understanding and optimizing student learning in online educational systems.
method Non-negative matrix factorization techniques for soft clustering.
result Behavioral changes of individual students and the system over time.
The paper develops a student performance prediction model using ensemble methods.
problem Improving student performance prediction in high school courses.
method Developed a multilabel classification model using SVM, RF, KNN, and MLP. Improved performance with LP transformation and partitioning schemes.
result The model achieved better performance than binary relevance and classifier chains.
Paper improves neural network models for MOOC student course prediction.
problem Improving neural network-based predictive methods for MOOC student course trajectory modeling.
method Enhanced LSTM networks and Transformer architecture applied to MOOC student data.
result Improved accuracy in predicting MOOC student course trajectories.
Personalized education at scale improves student outcomes.
problem Traditional personalized education is expensive and inequitable.
method Adapting educational presentations using RL, semi-supervised learning, NLP, and CV.
result Personalized education at scale can improve student outcomes.
A new method transfers knowledge without data, matching teacher's predictions closely.
problem Lack of access to training data for knowledge transfer.
method Adversarial training to match teacher's predictions without data.
result Zero-shot student performs well on CIFAR10, improving state-of-the-art.
Although compelling assessments have been examined in recent years, more studies are required to yield a better understanding of the several methods where assessment techniques significantly affect student learning process. Most of the educational research in this area does not consider demographics data, differing met…
Bayesian neural networks approximate Student-t processes in the infinite-width limit.
problem Modeling uncertainty in neural networks with greater flexibility.
method Extending asymptotic properties of Gaussian processes to Student-t processes in the infinite-width limit of BNNs.
result Posterior BNNs converge to Student-t processes in the infinite-width limit.