arXiv research

Paper explores vector embeddings, distributional hypothesis, and PIP loss for natural language processing.

The paper explores MAE as a loss function for DNN vector-to-vector regression, proving its advantages over MSE.

The paper tackles multi-armed bandits with vector losses, focusing on minimizing the $\ell^\infty$-norm of relative losses.

Paper analyzes proper losses and their performance in machine learning tasks.

Classification and regression tasks in overparameterized models show different generalization properties.

A new SVM classifier using $L_{0/1}$ soft-margin loss for improved performance.

Improved bounds for unbounded losses using transductive priors.

A new pinball loss function improves SVQR model's prediction and sparsity.

RHPSVM improves SVM performance with robust loss function.

Paper introduces a new SVR model using a combined reward and penalty loss function.

PoWER-BERT speeds up BERT inference by eliminating redundant word-vectors.

Study improves $H$-consistency bounds for regression analysis.

SGD converges to zero loss for separable data with fixed learning rate.

New approach estimates personalized treatment effects using surrogate losses.

Study vector-valued robust control under uncertainty.

Paper corrects Max-Margin loss for multi-label tasks.

New empirical Bayes estimator outperforms soft-thresholding for high-dimensional sparse vectors.

Study compares metric learning loss functions for speaker verification.

The paper interprets VQ-VAE loss as a form of information bottleneck.

Study on using random subspaces for ERM with various loss functions.

New SVM model balances sparsity and robustness in noisy data.

A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article. The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification directio…

This paper extends neural collapse to imbalanced data under cross-entropy loss.

The paper analyzes top-k classification and proposes consistent loss functions.

Sampling a fraction of pairs can match full evaluation in machine learning losses.

This paper consider penalized empirical loss minimization of convex loss functions with unknown non-linear target functions. Using the elastic net penalty we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear this inequali…

RFSVM with random features achieves faster learning rates.

The paper studies consistency of surrogate loss procedures under constrained classifiers.

New vector quantization method reduces relevance of parallel components in database points.

The paper improves Kaczmarz algorithm with momentum for linear least squares.

The stochastic linear bandit problem proceeds in rounds where at each round the algorithm selects a vector from a decision set after which it receives a noisy linear loss parameterized by an unknown vector. The goal in such a problem is to minimize the (pseudo) regret which is the difference between the total expected …

We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…

End-to-end speaker verification framework reduces text dependency.

Study uses SVM to predict weather-induced home insurance claims and losses.

BAEN-SVM improves SVM robustness to noisy data.

A new method for stochastic optimal control improves accuracy over existing techniques.

EDSVM uses elite observations to guide SVM classification.

This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some loss functions such as the truncated quadratic loss and quadratic loss, SVM with Gaussian kernel can reach the almost optimal learning rate, p…

Paper analyzes SVM behavior in high dimensions with exact formulas.

New loss function calibrates WW-hinge loss for multiclass SVM.

We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under…

The paper introduces a new FOR framework using Huber and ε-insensitive losses.

Proposes SPE for robust speaker verification.

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vec…

Unified SVM algorithm for various losses with fast training.

Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these "deep learning" models employ the so…

Proposes TRNN for tensorial time series data analysis.

It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.