A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
KT models struggle with student concept drift, but BKT remains the most stable.
PSI-KT improves KT accuracy and interpretability in learning materials.
A new method predicts student skill success rates in real-time.
Paper derives constraints for Bayesian Knowledge Tracing parameters.
Recent student knowledge modeling algorithms such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Networks (DKVMN) have been shown to produce accurate predictions of problem correctness within the same learning system. However, these algorithms do not attempt to directly infer student knowledge. In this pa…
Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. Multi-fidelity optimization promises relief using cheaper p…
RKT model improves knowledge tracing by considering exercise relations and student forget behavior.
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item res…
Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in …
New BNN architectures reduce computational cost for uncertainty quantification.
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized…
Bayesian inference for deep neural networks using trace-class priors and MLMC.
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variabl…
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…
KT models improved slightly with synthetic student data.
Hybrid framework injects TSLM insights into GRLM for robust time-series reasoning.
qDKT improves KT models by considering individual question outcomes.
Bayesian model identifies skill difficulties and student subgroups in engineering education.
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes. Exact calculation of this term is often intractable when the size of the kernel matrix exceeds a few t…
Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer a causal inference of those dynamics expressed in few parameters specific to built environments. These parameters …
Ray tracing sampler improves neural network sampling efficiency and resilience.
Framework incorporates prior knowledge into Bayesian models for data streams.
Bayesian method transfers knowledge between brain tumor datasets.
Bayesian neural networks incorporate domain knowledge through variational inference.
This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely studied for discrete R&S problems, which sample the one-step Bayes-optimal point. When used ove…
A new method for efficient computation of Knowledge Gradient in Bayesian optimization.
DBULL learns new clusters without forgetting past knowledge in streaming unlabelled data.
ACI uses Bayesian data assimilation to trace causes from effects in complex systems.
Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a…
Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation. We want to discuss here the application of Bayesian models to knowledge sha…
Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of the model's training set. The existing works empirically show the possibility of these membership inference (tracing) attacks against complex…
Study integrates attentional and spacing factors to improve category learning models.
Enhances Bayesian learning with rule-based evolutionary techniques.
Optimizes expensive experiments by incorporating expert knowledge.
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
Differentiable relaxation for inferring partial orders from noisy linear data.
Modeling infection hotspots to quantify effects of contact tracing and testing.
Develops a simulation-based method to translate expert knowledge into prior distributions for Bayesian models.
New method improves uncertainty estimation in Bayesian deep learning models.
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …
Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting. When using Bayesian models in continu…
Enhances model compression with multi-teacher knowledge distillation.
Bayesian networks combine prior knowledge with data to learn causal relationships.
Improves transparency and incorporates prior knowledge in Gaussian Process models.
In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge…
Paper extends knowledge gradient for preferential BO, overcoming computational challenges.
Paper proves stability for recovering connections from holonomy traces.