arXiv research

Improves causal inference with observational data by balancing features and weights.

Cross-balancing improves causal inference by balancing features with outcome data.

Classifies Bitcoin addresses based on their balance functions.

MCNet improves uncertainty calibration in online advertising by modeling complex relations and balancing performance.

Kernel balancing weights are generalized as KRRR, providing better confidence intervals for treatment effects.

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the de…

Proposes a balanced multi-component and multi-layer neural network for efficient function approximation.

This paper uses PCA and FA for feature selection in credit rating.

Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments out…

Paper proposes D3M to improve anti-spoofing detection by balancing loss function and using complementary features.

LP-FT improves personalized model training in FL by balancing generalization and personalization.

GENIE balances domain-invariant feature learning and gradient alignment for improved DG performance.

Jigsaw-VAE tackles feature imbalance in VAE latent variables, improving generalization across environments.

Study reveals class disparities in balanced datasets through spectral imbalance.

CUBE explains models by balanced experiments and contrasts.

ContextBench benchmarks methods for generating linguistically fluent inputs that activate specific latent features in language models.

A RL framework selects features to balance bias and accuracy dynamically.

Diffusion models learn balanced data representations, unlike classification models.

FSRM method improves treatment effect estimation from observational data.

This paper optimizes clustering interpretability by balancing value and user-defined features.

New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.

Paper explores balancing market dynamics and interpretable forecasting models for energy prices.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

cGAN learns a distribution for causal inference without specifying P.

SVM used for estimating treatment effects without confounding.

Study shows topological features improve time series classification.

IndiSeek learns disentangled representations by balancing independence and completeness.

Enhanced framework selects features for unbiased causal inference.

In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the…

We study the problem of partitioning a small sample of $n$ individuals from a mixture of $k$ product distributions over a Boolean cube $\{0, 1\}^K$ according to their distributions. Each distribution is described by a vector of allele frequencies in $\R^K$. Given two distributions, we use $γ$ to denote the average $\el…

Improved preterm prediction using synthetic EHG signals.

Deep linear networks exhibit collapsing features and classifiers across datasets.

Paper develops a dynamic Bayesian approach for active learning that optimizes exploration-exploitation balance.

Study constructs balanced datasets for seismic failure prediction.

SGD in DLNs reveals feature learning dynamics.

DINs use deep learning to optimize portfolio Sharpe ratio without manual feature engineering.

Data transformation, e.g. feature transformation and selection, is an integral part of any machine learning procedure. In this paper we introduce an information-theoretic model and tools to assess the quality of data transformations in machine learning tasks. In an unsupervised fashion, we analyze the transfer of infor…

TKIL improves class-balanced performance in incremental learning.

Transformers learn unseen tasks via prompts without fine-tuning.

New scalable method balances hospital profit status and heart attack outcomes.

It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifie…

We introduce a novel data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves linearly when lifted into a high (or infinite) dimensional feature space wh…

Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enou…

Recent advances in both machine learning and Internet-of-Things have attracted attention to automatic Activity Recognition, where users wear a device with sensors and their outputs are mapped to a predefined set of activities. However, few studies have considered the balance between wearable power consumption and activ…

Random Forest variable importance is improved by class balancing techniques.

Value selection reduces model size while maintaining accuracy.

This paper investigates arbitrage chains involving four currencies and four foreign exchange trader-arbitrageurs. In contrast with the three-currency case, we find that arbitrage operations when four currencies are present may appear periodic in nature, and not involve smooth convergence to a "balanced" ensemble of exc…

Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Surv…