arXiv research

Estimates causal effects using neural networks for balancing covariates.

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

Paper simplifies balancing weights by relaxing outcome assumptions.

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

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

New algorithm improves causal effect estimation for continuous treatments.

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

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

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…

Differentially private method for estimating individualized treatment rules.

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…

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…

New method for causal inference with complex treatment compositions.

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…

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

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

Active learning method balances bias and variance under class imbalance.

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 …

New Ricci flow method for directed graphs with balancing factor.

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

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…

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.

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…

The paper studies matrix normalization and graph balancing using a new functional and gradient descent.

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…

A {\em balanced} spatial graph has an integer weight on each edge, so that the directed sum of the weights at each vertex is zero. We describe the Alexander module and polynomial for balanced spatial graphs (originally due to Kinoshita \cite{ki}), and examine their behavior under some common operations on the graph. We…

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

Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal re…

The classical $k$-means algorithm for partitioning $n$ points in $\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been observed in many scientific and business applications. In this paper, we present an…

Lo-Hp decouples weight generation into local and global policies to improve flexibility and efficiency.

Balance corrects biased survey data for more accurate insights.

Improved MTM algorithm reduces high-dimensional convergence issues.

WiGS improves active learning for regression by dynamically selecting informative samples.

A new method balances model quality and Byzantine robustness in Federated Learning.

A new algorithm improves credit scoring accuracy for imbalanced data.

Under the assumption of asymptotic relative Chow-stability for polarized algebraic manifolds $(M, L)$, a series of weighted balanced metrics $ω_m$, $m \gg 1$, called polybalanced metrics, are obtained from complete linear systems $|L^m|$ on $M$. Then the asymptotic behavior of the weights as $m \to \infty$ will be stud…

A new GCN model detects cryptocurrency fraud by considering network evolution and balance theory.

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

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…

This paper introduces new loss functions for balanced multi-class classification.

We study channel number reduction in combination with weight binarization (1-bit weight precision) to trim a convolutional neural network for a keyword spotting (classification) task. We adopt a group-wise splitting method based on the group Lasso penalty to achieve over 50% channel sparsity while maintaining the netwo…

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. Howeve…

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

SONA improves conditional generation by balancing authenticity and alignment.