arXiv research

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

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

New method for causal inference with complex treatment compositions.

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

Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most commonly used methods to evaluate continuous effects. However, these techniques may be sensitive to model misspecification, extreme weights o…

TKIL improves class-balanced performance in incremental learning.

SVM used for estimating treatment effects without confounding.

In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT). Unfortunately, these methods do not necessarily achieve their goal of making the multivariate distribution of covariates for the control gro…

This paper balances bias and variance in adaptive importance sampling using mirror descent.

Develops a weighting framework to generalize ITRs from source to target populations.

Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can lead to high-variance estimates due to extreme weights and be sensitive to …

A new kernel improves tensor classification accuracy and reduces computation time.

The Fock-Bargmann-Hartogs domain $D_{n,m}(μ)$ ($μ>0$) in $\mathbb{C}^{n+m}$ is defined by the inequality $\|w\|^2<e^{-μ\|z\|^2},$ where $(z,w)\in \mathbb{C}^n\times \mathbb{C}^m$, which is an unbounded non-hyperbolic domain in $\mathbb{C}^{n+m}$. This paper introduces a Kähler metric $αg(μ;ν)$ $(α>0)$ on $D_{n,m}(μ)$, …

New geometric analysis of PWSPDs balances density and geometry in high-dimensional data.

Estimates causal effects using neural networks for balancing covariates.

Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Neve…

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

Paper simplifies balancing weights by relaxing outcome assumptions.

The paper reveals low-rank structure in neural network gradients, influenced by data and model parameters.

New algorithm improves causal effect estimation for continuous treatments.

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

Given a smooth polarized Riemann surface (X, L) endowed with a hyperbolic metric $ω$ with cusp singularities along a divisor D, we show the L^2 projective embedding of (X, D) defined by L^k is asymptotically almost balanced in a weighted sense. The proof depends on sufficiently precise understanding of the behavior of …

SIGTRON improves classification accuracy for imbalanced datasets.

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…

Unified theory for causal inference using various methods.

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…

New findings show a balance between data fit and complexity in kernel hyperparameters.

Differentially private method for estimating individualized treatment rules.

Kernel-UCBVI algorithm balances exploration and exploitation in metric state-action spaces.

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…

Improved kernel quadrature with convex weights using subsampling.

We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to rela…

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…

Toeplitz operators linked to submultiplicative filtrations and weighted Bergman kernels.

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…

Develops method to construct Lie algebra weight system kernel using Vogel algebra.

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

The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.

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

Active learning method balances bias and variance under class imbalance.

New Ricci flow method for directed graphs with balancing factor.

Proposes a simple framework to balance task difficulty in multi-task learning.

The paper calculates heat kernel and closed geodesic asymptotics for nilpotent coverings.

The paper studies heat kernels on weighted Riemannian manifolds with curvature bounds.

Estimation of importance sampling weights for off-policy evaluation of contextual bandits often results in imbalance - a mismatch between the desired and the actual distribution of state-action pairs after weighting. In this work we present balanced off-policy evaluation (B-OPE), a generic method for estimating weights…

New model reveals balance crucial for robust neural coding.

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the fail…