arXiv research

The paper investigates instance-based interpretation methods for VAEs.

Effective multilingual search with instance-based transfer learning.

A new hybrid method improves transfer learning performance.

A new classification method using hypothesis testing.

New efficient algorithm for approximate PML distribution.

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a forest of trees whose nodes store previously seen examples. It can be shown data points one at a time and updates itself incrementally, hence i…

Paper introduces Native Guide for generating time series counterfactual explanations.

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machi…

Transfer learning framework for fragility modeling under domain shift and class imbalance

Paper presents a Transformer model for automatic domain adaptation.

Study how communication and feedback graphs affect learning outcomes.

Predicts failure of autonomous vehicle steering control models.

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach wh…

Improved regret bounds for bandits with expert advice.

New method estimates GGLM parameters, overcoming non-convexity.

A method for disentangling latent variables using weak supervision based on pairwise similarities.

Optimal prototypes found for challenging pathological geometries.

Adversarial weighting improves regression task adaptation.

In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is …

CLAREL improves zero-shot learning by using per-image semantic supervision and metric rescaling.

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (\eg attributes) for unseen classes. …

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…

Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientif…

Proposes a Siamese NN for algorithm selection focusing on alike performing instances.

Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domai…

The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets whi…

A new algorithm enhances minority class representation in imbalanced datasets.

PatternLocal improves XAI for non-linear models by suppressing suppressor variables.

Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.

Paper introduces a method to explain deep learning models and identify good generalization.

NoiseRank reduces label noise without supervision, improving classification accuracy.

ROVAE uses noisy pairwise comparisons to disentangle factors in VAEs.

The paper proposes a method to improve fairness in machine learning models without refitting.

Paper proposes PP-GCN for fine-grained social event categorization.

Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we pr…

DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.

Paper uses financial news for stock trend forecasting using deep multiple instance learning.

Paper tackles instance-dependent label noise by approximating it with part-dependent noise.

New models handle survival analysis with concept-based learning.

L2P predicts heavy-tailed outcomes by placing new instances among known ones.

TSInsight improves interpretability of deep time-series models.

The paper predicts diseases using both clinical and genomics data.

Cognitive KR model learns new facts from few examples.

RL agents fail to generalize to unseen environments, even when dynamics are similar.

Improved self-supervised learning using credal sets.

We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from p…

A new method for early stopping in neural networks without validation sets.

Survey of counterfactual explanations for time series classification.