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.
A new batch ensemble method reduces regret in stochastic bandits.
New hyperparameter ensembles boost neural network performance and uncertainty.
Efficient neural network ensembles improve image classification reliability and uncertainty quantification.
New algorithm reduces overfitting in neural networks.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on …
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers. There are only a limited number of studies on the ense…
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the stat…
Ensembling DNNs improves minority group performance, leading to fairness.
Two main obstacles preventing the widespread adoption of variational Bayesian neural networks are the high parameter overhead that makes them infeasible on large networks, and the difficulty of implementation, which can be thought of as "programming overhead." MC dropout [Gal and Ghahramani, 2016] is popular because it…
Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge of 2014. Methods: The main contribution of this work is a multi-label ensemble…
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger numb…
Probability estimates generated by boosting ensembles are poorly calibrated because of the margin maximization nature of the algorithm. The outputs of the ensemble need to be properly calibrated before they can be used as probability estimates. In this work, we demonstrate that online boosting is also prone to producin…
Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the numbe…
The paper proposes an iterative approach to batch reinforcement learning for safer and more informative data collection.
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to configure the anomaly detector is by providing true labels for a few instances. We study the problem of…
P3BO optimizes biological sequence design by combining multiple methods.
Bayesian neural networks show complex posterior distributions that HMC can capture effectively.
HATT improves online decision tree ensembles by using a more eager splitting strategy.
ELMV uses ensemble learning to handle missing values in EHR data.
We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator,…
New distributed EnKF method for non-sequential assimilation of large datasets.
Anti-Distillation improves reproducibility of deep networks by making ensemble predictions more diverse.
Proposes a meta-learning method for robust portfolio optimization.
Residual networks' depth is mathematically equivalent to expanding an implicit ensemble size.
Attention augments forest for tabular data accuracy.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
EnsLoss combines multiple loss functions to prevent overfitting in classification.
In recent years, Deep Neural Networks (DNNs) have gained progressive momentum in many areas of machine learning. The layer-by-layer process of DNNs has inspired the development of many deep models, including deep ensembles. The most notable deep ensemble-based model is Deep Forest, which can achieve highly competitive …
Proposes a method to imitate active learning heuristics for better performance.
Prediction-time batch normalization improves model robustness under covariate shift.
Improved accuracy in machine learning with Cross-Cluster Weighted Forests.
HOLMES improves real-time model serving for ICU patients, balancing accuracy and speed.
New method predicts spatio-temporal data with short and long-range dependence.
We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network. We implement the l…
Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently. In this paper, we investigate this problem in the context of Gaussian Processes (GPs) and propose a new model which learns a mixture of latent processes by decomposing the c…
New method for efficient uncertainty quantification in DeepONets.
Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the bette…
ALTBI enhances outlier detection by maximizing the inlier-memorization effect.
AI-assisted framework detects and predicts rare extreme events.
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incrementa…
Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamilton…
The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design. However, insufficient information regarding underlying model provenance and the lack of …
The paper tackles MSDA by learning dictionary atoms in Wasserstein space.
Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called BLRNet. By embracing stochasticity during tr…
A new method estimates time-varying parameters in earth system models using offline and online data assimilation.
New research shows many batch selection methods for training work just as well as full batch training.