This paper improves SVM classification using a differentiable loss function and a gradient method.
problem Improving SVM classification with a differentiable loss function.
method Uses the Huberized Support Vector Machine (HSVM) and Proximal Gradient (PG) method.
result The proposed method converges linearly and supports the solution in finite time.
RHPSVM improves SVM performance with robust loss function.
problem Outliers and resampling instability in SVM models.
method RHPSVM uses a rescaled Huberized pinball loss function.
result RHPSVM outperforms existing SVM models in noisy and small-sample scenarios.
Paper proposes an algorithm for robust estimation using Huber's criterion.
problem Non-convexity and non-robustness of joint maximum likelihood estimation.
method Block-wise minimization majorization framework with data-adaptive step sizes.
result Improved convergence and robustness in sparse learning.
Generalized Huber's theorem for specific manifold curvature types.
problem Finite point conformal compactification on manifolds with certain curvature integrability.
method Generalization of Huber's theorem to higher dimensions with $L^ rac{n}{2}$ integrable Ricci curvatures.
result Validated finite point conformal compactification theorem for new class of manifolds.
Huber regression assessed for robustness in statistical learning.
problem Understanding Huber regression in nonparametric statistical learning.
method Assessment from statistical learning perspective, focusing on risk consistency, adaptive tuning, and convergence rates.
result Huber regression can be asymptotically mean regression calibrated under (1+ε)-moment conditions, justifying its robustness. Proposes an alternative probabilistic interpretation of Huber loss.
problem Lack of intuitive understanding of Huber loss transition point.
method Relates Huber loss to Kullback-Leibler divergence between Laplace distributions.
result Identifies optimal transition point intuitively based on data noise.
Deep Huber QRNs predict Huber quantiles for house prices.
problem Predicting more functionals of predictive probability distributions.
method Training a DL algorithm with the Huber quantile scoring function.
result DHQRNs provide satisfactory absolute performance in house price prediction.
The paper proves new inequalities on the unit ball in higher dimensions.
problem Establishing new weighted inequalities on the unit ball.
method Limiting approach to prove Carleman and Huber inequalities.
result Sharp weighted Carleman and Huber inequalities on the unit ball.
Proposes a new Huber loss combining absolute and quadratic properties.
problem Improving robustness in learning models.
method Introduces a generalized Huber loss with a log-exp transform and provides an efficient minimization algorithm.
result Shows that the new loss function can be minimized efficiently.
Paper introduces a new robust loss function for RL.
problem Heuristic selection of threshold parameters in quantile Huber loss.
method Derived from Wasserstein distance, captures noise in quantile values.
result Enhances robustness against outliers and enables parameter adjustment.
A new Markov subsampling strategy based on Huber criterion improves data processing from noisy full data.
problem High noise level in data leads to poor performance of subsampling procedures.
method Design a Markov subsampling strategy based on Huber criterion to construct an informative subset from noisy full data.
result The estimator based on HMS is statistically consistent with a sub-Gaussian deviation bound.
Super learner with Huber loss improves cost prediction and causal effect estimation in healthcare expenditure data.
problem Challenges in modeling healthcare expenditure distributions with standard super learning methods.
method Proposes a super learner using Huber loss, a robust loss function that down-weights outliers.
result Demonstrates appreciable finite-sample gains in cost prediction and causal effect estimation.
The paper extends Huber's theorem to higher dimensions with specific geometric constraints.
problem Applying Huber's theorem to higher-dimensional conformal metrics with bounded scalar curvature.
method Analyzing conformal metrics on a punctured ball with L2n bounded scalar curvature. result The volume density at infinity is precisely one, and the blow-down metric is Rn. The paper introduces a new FOR framework using Huber and ε-insensitive losses.
problem Handling outliers and sparsity in functional output regression.
method Proposes a flexible FOR framework with infimal convolution losses and computable algorithms.
result Demonstrates efficiency and effectiveness on synthetic and real-world data.
HOMER improves robustness and efficiency in estimating means of heavy-tailed data.
problem Lack of robustness and efficiency in estimating means of heavy-tailed data.
method HOMER aggregates block means through a radial Huber center, interpolating between robustness and mean efficiency.
result HOMER maintains robustness while approaching mean efficiency, especially under finite third moments.
This paper examines how noise affects deep neural networks and improves their performance.
problem The impact of noise on the stability of deep ReLU neural networks for nonparametric regression.
method Investigates the optimal rate of convergence for deep ReLU neural networks under Huber loss, considering the p-th moment of noise and the smoothness of the function.
result The optimal rate of convergence cannot be achieved by ordinary least squares but can be by Huber loss with a properly chosen parameter.
Improved robust regression with clean covariates achieves better rates than Huber's model.
problem Robust regression under adaptive contamination of responses with clean covariates.
method Exploiting clean covariates to construct an estimator achieving better rates than Huber's model.
result Improved estimation rate even with constant contamination, achieving consistency.
Unified approach connects robust statistics models for efficient mean estimation.
problem Efficient mean estimation in the presence of heavy-tailed noise and Huber contamination.
method Developed connections between Huber's epsilon-contamination model and heavy-tailed noise model, providing efficient and robust estimators.
result Simple efficient estimators are robust to both Huber contamination and heavy-tailed noise.
We show that for compact orientable hyperbolic orbisurfaces, the Laplace spectrum determines the length spectrum as well as the number of singular points of a given order. The converse also holds, giving a full generalization of Huber's theorem to the setting of compact orientable hyperbolic orbisurfaces.
A new method for quantized matrix completion using Huber loss.
problem Quantized Matrix Completion with robustness to quantization errors.
method Rank minimization with Huber loss regularization, Smooth Rank Approximation.
result Our method achieves better accuracy and efficiency than state-of-the-art methods.
Proposes a new loss function for robust learning.
problem Creating a robust loss function for machine learning.
method Extended pseudo Huber loss with log-exp transform and logistic function.
result Linear convergence algorithm for minimizer finding.
This paper solves hedging in incomplete markets using neural networks.
problem Hedging in incomplete markets with risk factor, illiquidity, and discrete transaction dates.
method Proposes a jump-diffusion model and uses RNN, LSTM, and Mogrifier-LSTM neural networks for hedging strategies.
result Mogrifier-LSTM is the fastest and most effective model for hedging.
New proof shows faster convergence rate for robust estimation with Lasso in adversarially contaminated outputs.
problem Robust estimation of parameters in the presence of adversarial output contamination.
method Extended Lasso with Huber loss function and L1 penalty, focusing on specific properties of the Huber function. result Same convergence rate as Dalalyan and Thompson (2019), but with a different proof.
Paper proposes a robust framework for detecting multiple periodic components in time series.
problem Detecting multiple periodic components in time series with interlaced patterns and external noise.
method Applying maximal overlap discrete wavelet transform to isolate periodic components, ranking them by wavelet variance, and detecting single periodicity robustly.
result The proposed algorithm outperforms other methods for both single and multiple periodicity detection.
A robust Gaussian process model using Huber likelihood for outlier resistance.
problem Outliers in observational data sets affect Gaussian process regression's robustness.
method Proposes a Gaussian process model with Huber likelihood and weights based on projection statistics.
result Demonstrates improved statistical efficiency and robustness to outliers.
The paper extends Huber's theorem to higher dimensions using n-Laplace equations.
problem Proving finite point conformal compactification for general dimensions.
method Using n-Laplace equations and strengthened Arsove-Huber's theorem.
result Established finite point conformal compactification theorem for manifolds.
New method for high-dimensional regression with unknown scale parameter.
problem High-dimensional linear regression with unknown scale parameter.
method Penalized Huber M-estimator with adaptive Lepski's method. result The method effectively calibrates scale in high-dimensional robust regression.
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…
New theorems in 2D and 4D for metrics with curvature or singularity.
problem Proving new versions of Huber theorem in dimensions 2 and 4.
method Using Coulomb frames and Bach tensor conditions to construct conformal metrics.
result Constructs conformal metrics with regularity across singularities in 4D.
Study minimax rates for density estimation under Huber contamination and Besov IPM losses.
problem Minimax convergence rates of nonparametric density estimation under Huber contamination model with outliers.
method Re-scaled thresholding wavelet series estimator and GAN architectures.
result Achieves minimax optimal convergence rates under Besov IPM losses.
Near-optimal algorithms for mean estimation and linear regression with Gaussian covariates and Huber contamination.
problem Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination.
method Near-optimal algorithms with optimal error guarantees, achieving sample complexity n=ildeO(d/ε2) and almost linear runtime. result First sample near-optimal and almost linear-time algorithms with optimal error guarantees for both problems.
Robust estimators for Gaussian sparse tasks with optimal error under contamination.
problem Robust mean estimation, PCA, and linear regression in the presence of Huber contamination.
method Novel multidimensional filtering method for sparse regime.
result Optimal error guarantees within constant factors for Gaussian robust k-sparse mean estimation. This paper proposes a method to automatically infer the quantile parameter in machine learning.
problem Estimating the quantile parameter in asymmetric loss functions.
method Jointly infers the quantile parameter and function parameters using convexity properties and a gradient boosting algorithm.
result The proposed method can automatically recover the quantile parameter and improve function parameter recovery.
Paper proposes robust regression methods using depth functions.
problem Robust regression in Huber's ε-contamination models. method Maximizers of multivariate regression depth functions.
result Achieves minimax rates in various regression problems.
New algorithms parallelize all-in-one SVM training, improving text classification accuracy.
problem Training all-in-one SVMs is computationally infeasible for many classes.
method Developed distributed algorithms for two all-in-one SVM formulations.
result Superior accuracy on text classification data compared to one-vs.-rest SVMs.
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
problem One-class SVM with privileged information
method Sequential Minimal Optimization (SMO) algorithm
result Finite-time convergence established
Paper presents robust confidence sequences for means with known moment bounds and arbitrary corruption.
problem Tackles robustness to outliers and adversarial corruptions in mean estimation.
method Designs new robust exponential supermartingales to create confidence sequences.
result Achieves optimal width and shows smaller margin of error compared to fixed-time robust methods.
Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metr…
Study improves error bounds for sparse regression with heavy-tailed covariates.
problem Estimating sparse coefficients in linear regression with heavy-tailed covariates.
method Employed an ℓ1-penalized Huber regression method. result Error bound identical to Gaussian case for L-subexponential covariates. Unified Pin-SVM improves accuracy over existing Pin-SVM model.
problem Difficulty in Pin-SVM model for −1≤τ<0. method Unified Pin-SVM model that solves a QPP for −1≤τ≤1. result Significant improvement in accuracy over existing Pin-SVM model.
Localized SVMs maintain SVM's consistency properties for large datasets.
problem Inefficient computational requirements of global SVMs for large data sets.
method Localized SVMs apply different hyperparameters to different regions of the input space.
result Localized SVMs inherit Lp- and risk consistency from global SVMs. New RESK distributions improve robust clustering of skewed data.
problem Robustly clustering non-symmetric, heavy-tailed data clusters.
method Proposes RESK distributions and an EM algorithm with robust skew-Huber M-estimator.
result Numerical experiments confirm the effectiveness of the proposed methods.
A new method quantizes conditional probability measures using deep learning.
problem Quantizing conditional probability measures efficiently.
method DCMQ method using Huber-energy kernel and deep neural network.
result Promising results on various examples.
New proof shows special surfaces have finite type.
problem Characterizing surfaces with finite topological type.
method Shorter proof of Huber's theorem.
result Special surfaces have finite topological type.
MU-SVM improves multiclass classification accuracy.
problem Multiclass classification problems.
method Proposes MU-SVM for multiclass learning and an analytic span bound for model selection.
result Achieves > 20% improvement in test accuracies compared to multi-class SVM.
Paper uses Gromov-Hausdorff convergence to re-examine surface classification.
problem Classifying complete Riemannian surfaces with finite total curvature.
method Gromov-Hausdorff convergence theory applied to surfaces.
result New understanding of Huber's classification theorem for surfaces.
BAEN-SVM improves SVM robustness to noisy data.
problem Noise and geometric irrationalities in SVM.
method Bounded asymmetric elastic net loss combined with SVM.
result BAEN-SVM is robust to noise and geometrically well-defined.
Improved robust regression for heavy-tailed and contaminated data.
problem Linear regression with heavy-tailed and adversarially contaminated covariates and responses.
method Applying a filtering algorithm to covariates and then using Huber regression, least trimmed squares, or least absolute deviation estimators on the remaining data.
result Near-optimal error rates achieved for the Huber regression estimator.