Study examines how balancing methods affect model behavior in imbalanced classification problems.
problem Impact of balancing methods on model behavior in imbalanced classification problems.
method Used Explainable Artificial Intelligence tools (variable importance method, partial dependence profile, accumulated local effects) to compare model behavior before and after balancing.
result Significant changes in model behavior due to balancing methods, leading to biased models.
Proposes φ-balancing for more balanced expert utilization in MoE models.
problem Balanced expert utilization in MoE models to avoid bias.
method Directly targets population-level balance by minimizing a convex potential function.
result Consistently outperforms prior methods in stability and effectiveness.
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.
problem Ensuring the balance property in insurance pricing models
method Using constrained GLM fitting
result Constrained GLM fitting is superior to the two previously discussed balance correction methods
Paper explores balancing market dynamics and interpretable forecasting models for energy prices.
problem Tackles the challenge of accurately predicting mFRR price and understanding market dynamics.
method Compares XGBoost and EBM for forecasting mFRR activation price in the balancing market.
result EBM provides comparable forecasting accuracy to XGBoost but with higher interpretability.
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…
Data balancing reduces variance in machine learning models.
problem Reduction of variance in machine learning models.
method Non-asymptotic statistical bound and eigenvalue decay of Markov operators.
result Data balancing across modalities and sources reduces variance.
New model reveals balance crucial for robust neural coding.
problem Efficient neural coding in noisy, chaotic networks.
method Analytical model of balanced predictive coding with dissociated balance and weight disorder.
result Superclassical scaling in coding accuracy, independent of balance and weight disorder.
Study confirms asymptotic behavior of logarithmic balanced metric near infinity.
problem Asymptotic behavior of logarithmic balanced metric near infinity.
method Non-trivial refinement of tools from previous work.
result Partial confirmation of conjecture on asymptotic behavior.
CUBE explains models by balanced experiments and contrasts.
problem Post-hoc explanation of trained predictive models.
method Design-based framework using balanced low-high probes.
result Reveals dominant learned effect structure and clarifies query efficiency.
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…
Researchers derive a new equation for valuing American options.
problem Valuation and hedging of American options on dividend-paying assets.
method Derive a stochastic balance equation for the value function and its gradient.
result The derived equation uniquely solves the valuation problem.
MCNet improves uncertainty calibration in online advertising by modeling complex relations and balancing performance.
problem Lack of effective calibration for complex relations and context features in online advertising.
method Introduces MCNet with MCF, order-preserving, and field-balance regularizers.
result Superior performance in generating well-calibrated probability predictions on public and industrial datasets.
Balances the regret of different algorithms in bandit and RL problems.
problem Model selection in bandit and reinforcement learning.
method Estimates and balances the empirical regrets of algorithms.
result Achieves near-optimal regret compared to the optimal base algorithm.
This study improves electricity price forecasting in the Irish balancing market.
problem Limited and inconsistent research on short-term price forecasting in volatile balancing markets.
method Compared statistical, machine learning, and deep learning models using a public dataset and framework.
result LEAR, a statistical approach, outperforms complex models in the balancing market.
This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.
problem Predictive multiplicity due to Rashomon effect in high-stakes environments.
method Investigates the impact of balancing and filtering techniques on predictive multiplicity using 21 real-world datasets.
result Data-centric AI strategies can mitigate predictive multiplicity, but preprocessing methods may introduce it.
ADAPT method controls balance between sparse generator and discriminator in GANs.
problem High computational complexity in GANs training.
method Balanced Dynamic Sparse Training (ADAPT) method.
result ADAPT method achieves a good trade-off between performance and computational cost.
Novel characterization of augmented balancing weights combining outcome and weighting models.
problem Improving estimation accuracy in machine learning models with balancing weights.
method Characterization of augmented balancing weights as linear models, extending to ridge and lasso regression.
result Equivalence and closed-form expressions for specific model choices, providing insights into performance.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Study compares empirical systemic risk with balance sheet risk in interbank networks.
problem Disentangling balance sheet risk from network effects in systemic risk.
method Generalised DebtRank dynamics and maximum-entropy approach to compare observed and expected systemic risk.
result Systemic risk levels are compatible but differ significantly during turbulent times.
The paper proposes a new method for covariate balancing using IPM to improve causal inference.
problem Covariate imbalance in causal inference weighting methods, especially when models are not correctly specified.
method The integral probability metric (IPM) is used to determine optimal weights for treated and control groups.
result The proposed method can be consistent without specifying either the propensity score or outcome regression model.
Kernel balancing weights are generalized as KRRR, providing better confidence intervals for treatment effects.
problem Lack of generalization error, correct feature specification, and limited to average effects.
method Interpreting kernel balancing weights as KRRR, relaxing feature specification, and extending Gaussian approximation.
result KRRR provides strong generalization properties and justifies confidence sets for causal functions.
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 …
Study on balanced Hermitian structures on Lie algebras twisted by representations.
problem Conditions for balanced and locally conformally balanced Hermitian structures on Lie algebras.
method Analysis of Hermitian structures on twisted cartesian products of Lie algebras.
result Classification of six-dimensional balanced Hermitian twisted cartesian products Lie algebras.
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…
LSB is a new MCMC method for discrete spaces that reduces target evaluations.
problem Sampling in discrete domains with high efficiency and adaptability.
method Local self-balancing proposals, mutual information objective, self-balancing learning.
result LSB converges with fewer target evaluations compared to existing methods.
The study identifies assets with local balance deviating from global balance to mitigate financial risk.
problem Selecting outperforming assets during financial crises.
method Investigates deviations of local balance from global balance as a criterion for asset selection.
result Assets with local balance deviating from global balance can mitigate financial risk.
This paper shows how we can build a model for transactions when goods are given away in the expectation of a later settlement. In settings where people keep track of their social accounts we are able to redefine concepts like account balance, yield curve and the law of diminishing returns. The model provides us with a …
Regularization leads to balancedness in deep linear networks.
problem Balancedness in deep linear networks.
method Geometric invariant theory and Riemannian geometry of fibers.
result Balancing flows converge to the balanced manifold at a uniform exponential rate.
BalLOT uses optimal transport for balanced k-means clustering.
problem Balanced k-means clustering of data. method BalLOT is an optimal transport approach to alternating minimization.
result BalLOT provides theoretical guarantees for exact and partial recoveries of planted clusters.
Estimates causal effects using neural networks for balancing covariates.
problem Estimating causal effects from observational data.
method Neural Balancing Weights (NBW) using α-divergence for density ratio estimation. result Generalized approach for balancing multidimensional data.
The basic financial purpose of corporation is creation of its value. Liquidity management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. These boo…
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
Blowing up flat metrics yields balanced ones with constant curvature.
problem Constructing balanced metrics with constant curvature on orbifolds.
method Blowing up a compact orbifold with balanced Chern-Ricci flat metrics.
result Blown-up orbifolds admit balanced metrics with constant Chern scalar curvature.
We will consider locally conformally balanced manifolds. We prove that a locally conformally balanced condition is not stable under a small deformation. We prove that locally conformally balanced condition is stable under any proper modification. We prove that symmetric products of the Kodaira surface can be resolve to…
MTAdam optimizes multiple loss terms in neural models, balancing gradients dynamically.
problem Balancing multiple loss terms in neural model training is challenging and computationally demanding.
method Generalized Adam algorithm that computes separate derivatives and balances gradients across layers dynamically.
result Training with MTAdam leads to faster recovery from suboptimal initial loss weighting and matches conventional training outcomes.
In this paper, the concept of balanced manifolds is generalized to reduced complex spaces: the class B and balanced spaces. Compared with the case of Kahlerian, the class B is similar to the Fujiki class C and the balanced space is similar to the Kahler space. Some properties about these complex spaces are obtained, an…
A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.
problem Imbalanced thermal comfort datasets make it hard to train accurate models.
method Proposed a modified conditional GAN (comfortGAN) to balance the dataset.
result A balanced dataset trained with comfortGAN yields higher classification accuracy.
BI-MAML learns multiple tasks without forgetting old ones.
problem Catastrophic forgetting in meta learning.
method Incremental model adaptation with balanced learning strategy.
result BI-MAML outperforms state-of-the-art models in accuracy and efficiency.
Study properties of balanced hyperbolic compact complex manifolds.
problem Understanding cohomology and harmonic spaces of balanced hyperbolic manifolds.
method Proved vanishing theorems and Hard Lefschetz-type theorems for balanced hyperbolic compact complex manifolds.
result Non-existence of certain L1 currents on the universal covering space of a balanced hyperbolic manifold. Unified model for signed networks separates balance and anomaly effects.
problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.
Study balanced Hermitian structures on almost abelian Lie algebras, classifying six-dimensional cases.
problem Classify balanced Hermitian structures on almost abelian Lie algebras.
method Classify six-dimensional almost abelian Lie algebras with balanced structures, investigate flow of balanced metrics and anomaly flow.
result Prove conjecture for compact almost abelian solvmanifolds with left-invariant complex structures.
The paper proposes a neural network method to estimate treatment effects by balancing treated and control distributions.
problem Estimating individual and average treatment effects from observational data.
method Balance regularization of multi-head neural network architectures to reduce confounding effects.
result The approach reduces bias-variance trade-off and improves treatment effect estimation.
Determined the balanced cone of a specific geometric space.
problem Determining the balanced cone of the small resolution of the quintic conifold.
method Intersection number method to explicitly determine the cone.
result Explicitly determined the balanced cone of the small resolution of the quintic conifold.
The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.
problem Debiased machine learning requires a proper approach to balance covariates.
method The paper advocates for using Riesz regression with basis functions of X for balancing.
result Covariate balancing is only valid when the score-relevant regression error is a function of covariates alone.
Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.
problem Run risk and hidden-to-maturity accounting in banking systems.
method Balance sheet model and optimization problem to assess run risk and resilience.
result Held-to-maturity accounting can mask revaluation losses and increase run risk.
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
problem Underwater default incentives in mortgages.
method Analyzes automatic balance adjustment and prepayment penalties in mortgage contracts.
result Automatic balance adjustments are preferable to traditional contracts at certain spreads, reducing underwater default.
Paper proves Chow stability implies balanced embedding.
problem Chow stability and balanced embeddings in projective varieties.
method Continuity method conditional on technical hypothesis.
result Chow stability implies balanced embedding.
Existence of balanced embedding proved for complex manifold into infinite-dimensional space.
problem Balanced embedding of non-compact complex manifolds into infinite-dimensional projective space.
method Gradient flow in a Hilbert space, long-time existence established by perturbation, convergence depends on a priori bounds.
result Existence of balanced embedding proved in a model case.