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.
Method selects the best deep learner for time-series prediction using Bayesian networks.
Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.
Bayesian X-Learner calibrates uncertainty and robustness for CATE estimation under heavy-tailed data.
PACOH improves meta-learning with theoretical guarantees and practical efficiency.
New framework identifies and reduces errors in machine learning under distribution shift.
The paper addresses learner privacy in convex optimization with feedback.
F-PACOH improves meta-learners' reliability in uncertain regions.
New theory shows how learning algorithms can create a bias towards negative outcomes.
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…
Bayesian approach helps update deep models without forgetting past data.
Bayesian algorithms improve online learning with adversaries over infinite action spaces.
TrueLearn Python library for personalized educational recommendations.
We consider the problem of prediction by a machine learning algorithm, called learner, within an adversarial learning setting. The learner's task is to correctly predict the class of data passed to it as a query. However, along with queries containing clean data, the learner could also receive malicious or adversarial …
Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regression tree), and their variants. In this paper, we propose a new perspective named variable grouping to enhance the predictive performance. Th…
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them…
In this paper the problem of learning appropriate bias for an environment of related tasks is examined from a Bayesian perspective. The environment of related tasks is shown to be naturally modelled by the concept of an {\em objective} prior distribution. Sampling from the objective prior corresponds to sampling differ…
We study the query complexity of Bayesian Private Learning: a learner wishes to locate a random target within an interval by submitting queries, in the presence of an adversary who observes all of her queries but not the responses. How many queries are necessary and sufficient in order for the learner to accurately est…
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice. While baggi…
Paper derives constraints for Bayesian Knowledge Tracing parameters.
Develops methods for cooperative Bayesian inference.
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
Bayesian meta-learning predicts Alzheimer's disease progression.
PSI-KT improves KT accuracy and interpretability in learning materials.
Infinite BART model selects number of trees and allows different functions for clusters.
Ensemble learning is a statistical paradigm built on the premise that many weak learners can perform exceptionally well when deployed collectively. The BART method of Chipman et al. (2010) is a prominent example of Bayesian ensemble learning, where each learner is a tree. Due to its impressive performance, BART has rec…
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
Applied Data Scientists throughout various industries are commonly faced with the challenging task of encoding high-cardinality categorical features into digestible inputs for machine learning algorithms. This paper describes a Bayesian encoding technique developed for WeWork's lead scoring engine which outputs the pro…
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with non…
Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kerne…
Bayesian neural networks learn efficiently at infinite width, matching polynomial-width performance.
Bayesian method mitigates negative transfer in unknown source data.
IE-GP framework learns sequentially arriving data with adaptive kernels.
The paper solves IRL for Bayesian stopping time problems.
We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression appr…
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Vi…
Bayesian scores improve structure learning in probabilistic circuits.
The aim of this work is to address the question of whether we can in principle design rational decision-making agents or artificial intelligences embedded in computable physics such that their decisions are optimal in reasonable mathematical senses. Recent developments in rare event probability estimation, recursive ba…
Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity proble…
Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, …
Bayesian optimization tackles uncertainty in context variables.
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
Bayesian model identifies skill difficulties and student subgroups in engineering education.
New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.
New bounds for Bayesian bandits show prior improves performance.
Owing to the recent advances in "Big Data" modeling and prediction tasks, variational Bayesian estimation has gained popularity due to their ability to provide exact solutions to approximate posteriors. One key technique for approximate inference is stochastic variational inference (SVI). SVI poses variational inferenc…
Optimal algorithm converts weak to strong learner with less data.
Bayesian neural networks integrate uncertainty into neural networks for improved performance.