arXiv research

Study examines how balancing methods affect model behavior in imbalanced classification problems.

Paper proves Chow stability implies balanced embedding.

The balance property is crucial for insurance pricing, ensuring total actuarial price equals loss. Maximum likelihood GLMs fulfill it, but Lindholm-Wüthrich suggests three methods, with constrained GLM being superior.

ADAPT method controls balance between sparse generator and discriminator in GANs.

New method balances covariates for stable causal survival effect estimation.

Proposes $φ$-balancing for more balanced expert utilization in MoE models.

Unified theory for causal inference using various methods.

A novel clustering method uses torque balance to group objects.

Estimates causal effects using neural networks for balancing covariates.

Paper introduces balanced payment systems to improve liquidity and risk management.

New meta-learning method improves domain generalization by balancing parameters closer to domain centroids.

LSB is a new MCMC method for discrete spaces that reduces target evaluations.

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

We solve tensor balancing, rescaling an Nth order nonnegative tensor by multiplying N tensors of order N - 1 so that every fiber sums to one. This generalizes a fundamental process of matrix balancing used to compare matrices in a wide range of applications from biology to economics. We present an efficient balancing a…

Proposes a novel neural network method to estimate average treatment effect.

SVM used for estimating treatment effects without confounding.

This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.

Balanced Activation improves object detection performance on long-tailed datasets.

This paper optimizes revenue and resource balance in network revenue management.

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual …

Improved GFlowNets learn more efficiently with trajectory balance.

Water balance models (WBMs) are often employed to understand regional hydrologic cycles over various time scales. Most WBMs, however, are physically-based, and few employ state-of-the-art statistical methods to reconcile independent input measurement uncertainty and bias. Further, few WBMs exist for large lakes, and mo…

Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced $k$-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to th…

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

Dynamic treatment effects estimated over time using covariate balancing.

The purpose of this paper is to extend the Green-Naghdi-Rivlin balance of energy method to continua with microstructure. The key idea is to replace the group of Galilean transformations with the group of diffeomorphisms of the ambient space. A key advantage is that one obtains in a natural way all the needed balance la…

A new method detects and removes false trailing balances in credit data.

We prove a general criterion to establish existence and uniqueness of a short-time solution to an evolution equation involving "closed" sections of a vector bundle, generalizing a method used recently by Bryant and Xu for studying the Laplacian flow in G_2-geometry. We apply this theorem in balanced geometry introducin…

Active learning method balances bias and variance under class imbalance.

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

MTAdam optimizes multiple loss terms in neural models, balancing gradients dynamically.

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

A new sampling method balances imbalanced data using gamma distribution.

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

We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inapp…

SurvCaus improves survival CATE estimation using neural nets.

TKIL improves class-balanced performance in incremental learning.

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…

A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.

Proposes a method to balance tasks in multitask learning with a single gradient step update.

We develop an efficient sampling method by simulating Langevin dynamics with an artificial force rather than a natural force by using the gradient of the potential energy. The standard technique for sampling following the predetermined distribution such as the Gibbs-Boltzmann one is performed under the detailed balance…

Flashback Learning balances model stability and plasticity in continual learning.

Study on balanced Hermitian structures on Lie algebras twisted by representations.

We study the intrinsic geometrical structure of hypersurfaces in 6-manifolds carrying a balanced Hermitian SU(3)-structure, which we call {\em balanced} SU(2)-{\em structures}. We provide conditions which imply that such a 5-manifold can be isometrically embedded as a hypersurface in a manifold with a balanced SU(3)-st…

The study identifies assets with local balance deviating from global balance to mitigate financial risk.

We establish a link between Archimedes' method of integration for calculating areas, volumes and centers of mass of segments of parabolas and quadrics of revolution by factorization via the moments of a balance and an integration technique for a particular integrable system, namely Bianchi's Bäcklund transformation for…

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…

In this paper, we propose a novel technique to implement stochastic gradient methods, which are beneficial for learning from large datasets, through accelerated stochastic dynamics. A stochastic gradient method is based on mini-batch learning for reducing the computational cost when the amount of data is large. The sto…