Deep neural nets can estimate regression with dependent data without the curse of dimensionality.
problem Regression with dependent data and structural assumptions on the regression function.
method Deep recurrent neural network estimate under suitable structural assumptions.
result Deep neural nets can circumvent the curse of dimensionality for regression with dependent data.
Deep Evidence Regression improves credit risk prediction uncertainty.
problem Quantifying uncertainty in credit risk predictions.
method Applying Deep Evidence Regression to credit risk settings.
result Demonstrated improved prediction of Loss Given Default.
This paper extends neural collapse to regression problems, revealing key features and structures.
problem Understanding the structure learned by deep neural networks in regression tasks.
method Established Neural Regression Collapse (NRC) across different models, analyzing feature and weight alignments.
result Deep neural regression models exhibit a collapsed feature space, aligning with target dimensions and covariances.
Proposes a method to estimate drug sensitivity uncertainty using deep regression forests.
problem Lack of confidence intervals in deep learning models for critical tasks.
method Uses Deep Regression Forests to estimate variance and uncertainty for drug sensitivity prediction.
result Improves efficiency and coverage of uncertainty estimates for drug sensitivity predictions.
Deep single-index Fréchet regression for metric space-valued outputs
problem Predicting outputs in non-Euclidean spaces
method DeSI (Deep Single-Index Fréchet Regression)
result Interpretable index direction for inputs
DRE combines DNN with random feature regression for efficient neural network design.
problem Designing and training deep neural networks (DNN) efficiently and effectively.
method DRE architecture with two-layer neural networks, randomly drawn input and output weights trained with linear ridge regression.
result DRE outperforms state-of-the-art DNN in many data sets with lower computational cost.
Deep-HGP uses Bayesian nonparametric approach for complex data regression.
problem Complex data regression with compositional structures.
method Deep Gaussian processes with a squared-exponential kernel, data-driven lengthscale parameters.
result Posterior distribution optimally recovers unknown true regression curve in terms of quadratic loss.
Flexible framework for deep distributional regression models.
problem Learning conditional distributions from semi-structured data.
method Combines additive regression models with deep networks using TensorFlow.
result State-of-the-art predictive performance with interpretability.
Deep model tackles claim size modeling with quantile-based regression.
problem Actuarial claim size modeling difficulty with no simple distribution.
method Deep composite regression model with quantile splicing point.
result Deep neural network regression models show superiority over classical approaches.
Deep neural networks estimate regression functions on manifolds.
problem Estimating regression functions on manifolds from data.
method Fully connected deep neural networks with ReLU activation, analyzing convergence rates.
result Estimates achieve a rate of convergence dependent on manifold dimension, not predictor dimension.
Paper tackles uncertainty prediction for deep sequential regression.
problem Challenges in generating accurate uncertainty estimates for deep recurrent networks.
method Flexible method that generates symmetric and asymmetric uncertainty estimates without stationarity assumptions.
result Outperforms competitive baselines on both drift and non-drift scenarios.
Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in…
DFIV uses deep neural nets to learn nonlinear features in IV regression.
problem Learning causal relationships from observational data with nonlinear interactions.
method DFIV trains deep neural nets to define nonlinear features on instruments and treatments, alternating training to compose stages 1 and 2.
result DFIV outperforms state-of-the-art methods on IV benchmarks and off-policy policy evaluation.
PAGER detects failures in deep regression models using a new framework.
problem Detecting failures in deep regression models.
method PAGER uses a combination of epistemic uncertainty and manifold non-conformity scores.
result PAGER accurately characterizes and detects failures in deep regressors.
Density-Regression improves deep uncertainty estimation with faster inference.
problem Efficient uncertainty estimation under distribution shifts with modern deep models.
method Leverages density function for fast inference and distance-aware feature space.
result Density-Regression achieves competitive uncertainty estimation performance.
Paper proposes deep neural networks for nonparametric regression from dependent data.
problem Nonparametric regression from strongly mixing observations.
method Minimum error entropy principle applied to deep neural networks.
result Deep neural networks achieve minimax optimal convergence rates for Gaussian errors.
Bayes-optimal learning of deep random networks with Gaussian weights is studied.
problem Learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights.
method Closed-form expressions for Bayes-optimal test error, ridge regression, kernel and random features regression are computed.
result Optimally regularized ridge regression and kernel regression achieve Bayes-optimal performances, while logistic loss yields a near-optimal test error for classification.
Proposes a deep ordinal regression framework using optimal transport loss and unimodal output probabilities.
problem Lack of unimodal output probabilities in recent ordinal regression models.
method Introduces a deep learning framework based on optimal transport loss and unimodal output distribution, inspired by the Proportional Odds model.
result Demonstrates improved performance and unimodal output probabilities on real-world datasets compared to existing methods.
Deep learning improves quantile regression for censored survival data.
problem Predicting nonlinear patterns in censored survival data.
method Neural network with adjusted check function for inverse censoring distribution.
result Deep learning outperforms traditional quantile regression methods in prediction accuracy.
Deep P-Spline automates DNN structure selection for complex regression problems.
problem Challenges in selecting optimal network structures for DNNs.
method Linking neuron selection to knot placement in basis expansion techniques, introducing a difference penalty for automated knot selection.
result Deep P-Spline extends model class and forms a latent variable modeling framework with theoretical guarantees.
Deep learning model estimates uncertainty in complex regression tasks.
problem Uncertainty quantification in probabilistic regression predictions.
method Combines statistical and deep learning transformation models using gradient descent.
result State-of-the-art performance on small datasets and complex image data.
Simplifies transfer learning with deep neural networks using ridge regression.
problem High computational cost of finetuning deep models for transfer learning.
method Leverage the low-rank property of deep neural networks' feature vectors in kernel ridge regression.
result Successful on supervised and semi-supervised transfer learning tasks.
Improved neural network regression uncertainty estimation.
problem Neural networks lack classical uncertainty due to finite data.
method Bootstrapped Deep Ensembles, incorporating parametric bootstrap.
result Significantly improved uncertainty estimation compared to standard Deep Ensembles.
DFIV uses deep features for IV regression, achieving optimal rates.
problem Optimal IV regression with deep features for complex target functions.
method Two-stage approach: deep feature learning followed by IV regression.
result DFIV achieves minimax optimal learning rate under certain conditions.
Bias correction needed after deep learning regression training.
problem Systematic error accumulation in deep learning regression models.
method Adjust bias of the machine learning model post-training.
result Bias correction efficiently solves error accumulation.
NMDR estimates complex mixtures of distributions efficiently.
problem Estimating complex finite mixtures of distributions in high-dimensional settings.
method Flexible additive predictors, neural networks, and deep learning optimizers.
result Competitive performance in complex scenarios compared to existing approaches.
Deep neural networks with adversarial training achieve sup-norm convergence for nonparametric regression.
problem Achieving sup-norm convergence for deep neural network estimators in nonparametric regression.
method Developed an adversarial training scheme to address the sup-norm convergence issue.
result Deep neural network estimators achieve optimal sup-norm convergence with the proposed adversarial training.
Improves regression accuracy by using multiple discrete representations.
problem Improving regression accuracy using deep learning.
method Proposes using multiple discrete representations simultaneously for regression problems.
result Reduces prediction error compared to a baseline RvC approach.
This paper applies deep learning to ordinal regression, modeling it as a binary search.
problem Ordinal regression with deep learning models.
method Formulated ordinal regression as a binary search problem, using recurrent neural networks.
result Deep learning model shows comparable or better predictive power compared to traditional methods.
Sparse-penalized deep neural networks improve performance in weakly dependent processes.
problem Nonparametric regression and classification under weak dependence.
method Sparse-penalized deep neural networks with oracle inequalities and convergence rates established.
result The proposed estimators outperform non-penalized ones in simulations.
Metrics assess uncertainty structure and distribution for regression models.
problem Quantifying uncertainty in high-dimensional and nonlinear regression tasks.
method Two bounded comparison metrics for uncertainty structure and distribution.
result DNNs and DNOs provide encouraging uncertainty metric values in high dimensions.
Deep neural networks enforce non-crossing quantile regression curves.
problem Estimating quantile regression curves without crossing.
method Penalized deep ReQU neural networks with a non-crossing penalty.
result Established non-asymptotic risk and error bounds for the estimated QRP.
New method estimates covariance in deep heteroscedastic regression without labels.
problem Estimating covariance in deep heteroscedastic models is challenging due to sample-dependent covariance and lack of ground truth.
method Proposes a self-supervised approach using KL Divergence and 2-Wasserstein distance for covariance estimation and a neighborhood-based heuristic for pseudo labels.
result Demonstrates effective pseudo labels and a computationally cheaper yet accurate deep heteroscedastic regression.
Deep learning method improves regression accuracy.
problem Nonparametric regression challenges.
method Over-parametrized deep neural networks with logistic activation, gradient descent, special topology, random initialization, and data-dependent learning rate.
result Theoretical bound on L2 error and improved finite sample performance. Paper introduces DQPOPE for estimating return distributions in reinforcement learning.
problem Estimating the entire return distribution from off-policy data.
method Deep quantile process regression for distributional off-policy evaluation.
result DQPOPE achieves statistical advantages by estimating full return distribution with same sample size.
Neural networks improve nonparametric regression with measurement errors.
problem Nonparametric regression with measurement errors.
method Proposes a neural network design using FNN, normalizing flow, and inference network.
result Neural network approach is more flexible and superior or comparable to classical methods.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
DER uses neural nets to better handle uncertainty in machine learning.
problem Need for principled uncertainty reasoning in safety-critical domains.
method Uncertainty-aware regression-based neural networks (NNs) with evidential distributions.
result DER shows promise over traditional methods but is a heuristic.
Bayesian deep learning accounts for input uncertainty using Errors-in-Variables models.
problem Uncertainty in deep regression models, especially from input data.
method Bayesian treatment with Errors-in-Variables model to decompose predictive uncertainty.
result The approach yields more complete and consistent uncertainty estimates.
The paper proposes a method for valid multi-target regression predictions.
problem Valid multi-variate predictions for multi-target regression.
method Copula functions applied to deep neural networks for inductive conformal prediction.
result The proposed method ensures efficiency and validity for multi-target regression problems.
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
Deep learning method for semiparametric regression of spatial data.
problem Estimating relationships between response and covariates in spatially dependent data.
method A sparsely connected deep neural network with ReLU activation function.
result The method is consistent and can handle large datasets.
Deep learning models forecast multiple yield curves with improved accuracy.
problem Globalization of financial markets affects yield curves.
method Combines self-attention mechanism and nonparametric quantile regression.
result Effective point and interval forecasts of future yields.
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
problem Uncertainty in multi-step time series prediction.
method Developed a novel quantile regression deep learning framework for multi-step time series prediction.
result Integrating quantile loss function with deep learning provides additional predictions for selected quantiles without loss in accuracy.
We consider the classical sparse regression problem of recovering a sparse signal x0 given a measurement vector y=Φx0+w. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sp…
New method improves deep neural network performance in regression tasks.
problem Improving generalization, robustness, and explainability of deep neural networks in regression.
method Developed a new Information Bottleneck approach using Cauchy-Schwarz divergence.
result Demonstrated superior performance on six real-world regression tasks.
The paper tightens bounds on covering numbers for deep ReLU networks.
problem Characterizing the capacity and performance of deep ReLU networks.
method Derives tight lower and upper bounds on metric entropy of ReLU networks.
result Establishes optimality in nonparametric regression via deep networks.
The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works on ordinal regression do not take label noise into account. We propose a theoretically grounded approach for class conditional label noise i…