arXiv research

Toy model study shows resampling/reweighting can improve feature learning in imbalanced classification.

ARL improves fairness without protected features, showing AUC improvements for worst-case groups.

Unified analysis of reweighted least-squares algorithms for linear models.

DoubleEnsemble improves financial predictions by selecting key features and reweighting samples.

Paper identifies problematic baselines in Shapley value explanations and proposes a reweighting mechanism.

AFR simplifies reducing reliance on spurious features, improving model performance.

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simul…

Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation…

We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used. Our work extends the previous fast rate analysis of random features method from…

RFM reduces feature space for linear models, improving sparse recovery.

This paper develops a novel deep recurrent neural network for sequential signal reconstruction.

Improved matching for multiple objects using a novel reweighting method.

This paper improves model robustness to underrepresented groups using ranking metrics and reweighting.

Reweighted l1-algorithms have attracted a lot of attention in the field of applied mathematics. A unified framework of such algorithms has been recently proposed by Zhao and Li. In this paper we construct a few new examples of reweighted l1-methods. These functions are certain concave approximations of the l0-norm func…

Proposes a new method to improve regression models with reweighted samples.

Improved learning to reweight using deep interactions between student and teacher models.

Defense against user shilling attacks in collaborative filtering using edge reweighting.

RR-GNN improves GNN prediction intervals by accounting for graph heteroscedasticity and structural biases.

We analyze a reweighted version of the Kikuchi approximation for estimating the log partition function of a product distribution defined over a region graph. We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion, and show that a…

CurveRL optimizes large model reasoning by reweighting prompts based on their rank and density.

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, …

New reweighted losses improve diffusion model training and image quality.

Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful when users need to develop models with data from varying sources, of varying quality, or from different time ranges. We build CrossTrainer, a…

Reweighting improves GAN accuracy without sacrificing statistical power.

New algorithm improves online binary classification with constant time complexity.

Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from available training data, the learned networks have achieved state-of-the-art performance in respect of both speed and accuracy. However, the …

Resampling outperforms reweighting for correcting biased data in machine learning models.

Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software triggers. In most cases, these are classification models used to select the "signal"…

Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope, precision, and accuracy of Standard Model measurements and searches for new phenome…

A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. …

Algorithm samples polygons of fixed edge lengths in any dimension.

The paper improves sample reweighting methods for adapting to covariate shifts.

Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data. L1 PCA uses the L1 norm to measure error, whereas the conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the fitting error of…

A bilevel optimization method for reweighting data leads to suboptimal results.

Optimizes sample reweighting to match laws under covariate shift using Wasserstein distance.

Q-MMR evaluates policies using reweighted rewards and moment matching.

Method reweights instances and classes to improve robustness in noisy data.

Kernel methods can learn hierarchical polynomials efficiently.

Method reweights auxiliary tasks to reduce data need for main task.

Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing

Iteratively reweighted $\ell_1$ algorithm is a popular algorithm for solving a large class of optimization problems whose objective is the sum of a Lipschitz differentiable loss function and a possibly nonconvex sparsity inducing regularizer. In this paper, motivated by the success of extrapolation techniques in accele…

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs. Though there exist various developments on sampling and aggregation to accelerate the training process and improve the performances, limited works focus on dealing with the dimensional information imbalance…

The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like…

Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.

Proposes SVI for covariate-shift generalization with sparse variable independence.

A new method of moments estimator goes beyond data reweighting.

Adaptive PI by reweighting nonconformity scores improves model uncertainty reflection.