arXiv research

Novel characterization of augmented balancing weights combining outcome and weighting models.

New method for causal inference with complex treatment compositions.

IB-GAN improves multivariate time series classification under imbalance.

Synthetic data augmentation can improve imbalanced classification metrics.

Supervised topic models with a logistic likelihood have two issues that potentially limit their practical use: 1) response variables are usually over-weighted by document word counts; and 2) existing variational inference methods make strict mean-field assumptions. We address these issues by: 1) introducing a regulariz…

Estimates causal effects using neural networks for balancing covariates.

Synthetic augmentation helps but not always in imbalanced learning.

A study on optimizing data augmentation weights for improved test-time predictions.

Bayesian method estimates QTEs from observational data.

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

Paper simplifies balancing weights by relaxing outcome assumptions.

This paper explores how representation learning can improve design-based causal inference.

This paper improves auto-augment efficiency by sharing augmentation weights.

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

Python package automates causal parameter estimation using Riesz regression.

New algorithm improves causal effect estimation for continuous treatments.

Adaptive weighting schemes enhance time-series data augmentation for financial and UCR datasets.

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

M2m method improves deep learning performance on class-imbalanced datasets.

The paper proposes a new method for covariate balancing using IPM to improve causal inference.

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…

For a polarized Kähler manifold $(X, L)$, we show the equivalence between relative balanced embeddings introduced by Mabuchi and $σ$-balanced embeddings introduced by Sano, answering a question of Hashimoto. We give a GIT characterization of the existence of a $σ$-balanced embedding, and relate the optimal weight $σ$ t…

SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.

Unified theory for causal inference using various methods.

The paper addresses classification imbalance by framing it as a transfer learning problem.

We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. The proposed technique keeps the contribution of positive and negative weights to the layer output balanced. We validate our method on a set o…

The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-nets have been shown to obtain high segmentation accuracy and appear to generalize well when trained on one scanner type and tested on another scanner, provided that a very similar T1-…

Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.

Differentially private method for estimating individualized treatment rules.

Augmentation improves machine learning model performance on small datasets.

In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner. Since the weighted Laplacian strategy inherits the virtu…

Improves group fairness in tensor completion by augmenting tensors with balanced entities.

Improves fairness in machine learning by adding underrepresented group data.

Enhances classification accuracy on low data sets using synthetic data.

We introduce a notion of cardinality for the augmentation category associated to a Legendrian knot or link in standard contact R^3. This `homotopy cardinality' is an invariant of the category and allows for a weighted count of augmentations, which we prove to be determined by the ruling polynomial of the link. We prese…

Thurston introduced shear deformations (cataclysms) on geodesic laminations - deformations including left and right displacements along geodesics. For hyperbolic surfaces with cusps, we consider shear deformations on disjoint unions of ideal geodesics. The length of a balanced weighted sum of ideal geodesics is defined…

A new method for handling imbalanced data in regression models.

Online data augmentation improves forecasting performance in deep learning.

A new method corrects bias in causal inference by balancing covariate distributions.

We present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts, decisions, and outcomes. Only the outcome of the enacted decision is available and the historical policy is unknown. These problems arise in personalized medicine using electroni…

MASC balances dataset representation using affinity clustering and distribution discrepancies.

No radial balanced metrics found on Kepler manifold unit ball with mild boundary conditions.

New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning d…

Adaptive weights improve physics-informed neural networks and deep operator networks.

Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classificat…

Data augmentation improves robustness in adversarial training.

MCRAGE generates synthetic data to balance healthcare datasets.