arXiv research

Binary feedback outperforms ordinal comparisons in ranking recovery.

Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.

The article explains how to estimate confusion matrices for classifiers using unlabeled data.

BO algorithms improve binary and preferential optimization by distinguishing between types of uncertainty.

Background: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable…

Suppose that we wish to estimate a vector $\mathbf{x}$ from a set of binary paired comparisons of the form "$\mathbf{x}$ is closer to $\mathbf{p}$ than to $\mathbf{q}$" for various choices of vectors $\mathbf{p}$ and $\mathbf{q}$. The problem of estimating $\mathbf{x}$ from this type of observation arises in a variety …

Comparison of AutoML and DL shows AutoML outperforms simple DL for binary classification.

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…

Confidence intervals improve evaluation of binary prediction rules in data mining.

Lossy compression of statistical data using quantum annealing.

New method for estimating gradients in stochastic binary networks.

Proposes MELODIC family for simultaneous binary logistic regression.

This paper studies binary classification problem associated with a family of loss functions called large-margin unified machines (LUM), which offers a natural bridge between distribution-based likelihood approaches and margin-based approaches. It also can overcome the so-called data piling issue of support vector machi…

Paper presents a universal baseline for binary prediction models.

The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.

Paper explores connections between loss functions and consistency in binary classification and regression.

Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of metric elicitation is to discover the performance metric of a practitioner, which reflects her innate rewards (costs) for correct (incorrect)…

A new method reduces preference distortion in LLM alignment.

Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.

A new method for 1-bit matrix completion that is faster and more accurate.

Improved GP models for fast training and good performance.

We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu…

Networked sensing, where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors, has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet-of-things. To reduce the communication, sensing and storage complexity, t…

Improves classifier evaluation by aligning with Total Classification Cost.

Malware is constantly adapting in order to avoid detection. Model based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to a…

This paper applies deep learning to ordinal regression, modeling it as a binary search.

Geometric analysis of ROC and PR curves for binary classification.

Federated learning for Bayesian clustering of large datasets.

New oracle uses uncertainty for active classification with noisy feedback.

Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.

Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they …

LxCIM metric improves binary classification performance evaluation.

Estimates density ratio for two-sample comparison using tree models.

This paper compares preprocessing techniques for XGBoost models on various data sets.

This paper addresses dueling bandits with contextual information, improving regret bounds by accounting for variance.

This paper extends the idea of Universum learning [1, 2] to single-class learning problems. We propose Single Class Universum-SVM setting that incorporates a priori knowledge (in the form of additional data samples) into the single class estimation problem. These additional data samples or Universum belong to the same …

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data poi…

We examine the supervised learning problem in its continuous setting and give a general optimality condition through techniques of functional analysis and the calculus of variations. This enables us to solve the optimality condition for the desired function u numerically and make several comparisons with other widely u…

We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin …

Model learns metrics and preferences from user comparisons.

Optimal model diagnoses funduscopic images for ocular diseases.

New Gaussian min-max theorem extends classical results to non-i.i.d. Gaussian matrices.

Quantum GNNs outperform classical GNNs in jet tagging.

This study compares machine learning algorithms for predictive performance and interpretability.

We introduce an unsupervised approach to efficiently discover the underlying features in a data set via crowdsourcing. Our queries ask crowd members to articulate a feature common to two out of three displayed examples. In addition we also ask the crowd to provide binary labels to the remaining examples based on the di…

The paper extends multiple instance learning to multiclass and regression problems.

Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards sma…

AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence o…