arXiv research

Introduces model class selection to find sets of near-optimal models.

Adaptive model selection for RL with unknown function classes.

Paper proposes a method to select base classes for few-shot learning.

Regularizing for or against class selectivity in DNNs improves test accuracy.

New algorithm offers costless model selection in contextual bandits.

We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspects of performing model selection, we argue that any practical model selection procedure must not only trade off estimat…

A new algorithm selects models for contextual bandits, reducing regret.

The study explores whether model selection guarantees apply to contextual bandits.

Class selectivity affects robustness to corruptions but not to adversarial attacks.

Study model selection in batch policy optimization with three error sources.

Lower class selectivity makes networks more robust to natural perturbations but more vulnerable to adversarial attacks.

New algorithms for model selection in linear bandits adapt to instance complexity.

Tree tensor networks balance model complexity and empirical risk for high-dimensional function approximation.

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a ke…

Paper introduces a new performance metric for class imbalance datasets.

The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this…

XNB classifier improves model interpretability by selecting class-specific features.

The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable class of models, MDL predictions are close to the true distribution in a strong sense. The result is completely general. No independence, ergodicity, stationarity, identifiabilit…

New method proves neural networks can select features consistently.

A new data augmentation method selects mixed classes based on class distances for better performance.

New algorithms improve model selection in linear bandits with optimal regret.

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take plac…

In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handle…

We introduce the problem of model selection for contextual bandits, where a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation. Our main result is a new model selection guarantee for linear contextual bandits. We work in the stochastic realizable setting with a seque…

This paper proposes a method for multi-class classification problems, where the number of classes K is large. The method, referred to as Candidates vs. Noises Estimation (CANE), selects a small subset of candidate classes and samples the remaining classes. We show that CANE is always consistent and computationally effi…

New study reveals surprising adaptive rates in model selection for transfer learning.

Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximizing mutual information between subsets of features and class labels. Practical methods are forced to rely on approximations due to the diffi…

We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves o…

Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correc…

Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate. The selection can be optimized post-hoc for trained models or incorporated directly into the method itself (self-explaining). However, an overall selection does not properly c…

Random Forest variable importance is improved by class balancing techniques.

New findings show strategic interactions can undermine model expressiveness in machine learning.

A novel method selects genes for high-dimensional gene expression data with class imbalance.

ARL-GEN adapts to the smallest model class in nested families for RL with improved regret.

Framework tackles class imbalance and noisy labels in active learning.

New method stabilizes model selection with theoretical guarantees.

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowle…

The paper proposes methods to control errors in language generation models using textual entailment.

ADML combines debiased learning with data-driven model selection for efficient inference.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment ca…

Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.

Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-sc…

Selective inference for group lasso estimators across various distributions and covariates.

New algorithms for model selection in linear contextual bandits without feature diversity conditions.

Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal processing techniques rely on such a simplified model. As this model is often unknown for many classes of the signals, we need to select su…

A new method selects representative subdata without assuming underlying models.

New gene selection method improves tumor classification accuracy.

New algorithm recovers graph structure from noisy data.