Boosting classifiers improve accuracy with noisy inputs.
problem Noisy communication or computation degrades boosting classifier accuracy.
method Optimize resource allocation for base classifiers based on importance metrics.
result Optimized noisy boosting classifiers are more robust than bagging.
Paper tackles noisy labels in deep learning networks.
problem Learning with noisy labels in deep neural networks.
method Sparse regularization strategy to approximate one-hot constraint.
result Improves performance of commonly-used loss functions in noisy labels and class imbalance.
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based…
Gradient descent learns ReLU networks with Gaussian inputs and noisy outputs.
problem Learning one-hidden-layer ReLU networks with Gaussian inputs and noisy outputs.
method Gradient descent with tensor initialization for empirical risk minimization.
result Gradient descent converges to ground-truth parameters at a linear rate up to statistical error.
Gen-CUDE is a neural network for denoising noisy channels.
problem Denoising in finite-input, general-output noisy channels.
method Unsupervised neural network trained on noisy data.
result Gen-CUDE achieves better denoising results than other methods.
Generates positive examples from noisy data streams.
problem Learning from noisy example streams in hypothesis classes.
method Extending results from previous studies to account for noise.
result Conditions for noisily generatable binary hypothesis classes.
Detects anomalies in noisy data from linear systems.
problem Identifying samples of noise in a linear dynamical system.
method Robust spectral filtering and anomaly detection method.
result Guaranteed statistical performance in identifying noise samples.
Exact bounds derived for neural network outputs with noisy inputs.
problem Bounding the output distribution of neural networks with random inputs.
method Applying ReLU NNs to derive bounds for general NNs, then using these to find exact error guarantees.
result Exact upper and lower bounds for the output distribution of neural networks with random inputs.
Paper proposes a modified uncertainty sampling method to speed up preference learning from noisy humans.
problem Learning preferences from humans with limited queries and noisy responses.
method Modified uncertainty sampling using expected output value to speed up preference learning.
result The modified method outperforms the baseline uncertainty sampling in preference learning.
Framework prevents deep learning models from memorizing noisy labels.
problem Deep learning models memorize noisy labels during early learning phase.
method Develops a technique that exploits early learning phase via regularization.
result Framework achieves robustness to noisy annotations on benchmarks and real-world datasets.
Study shows MSE with sigmoid can match SCE in classification tasks, especially with noisy data.
problem Inconsistent errors in neural network classification tasks.
method Introduced Output Reset algorithm to use MSE with sigmoid activation.
result MSE with sigmoid activation achieves comparable accuracy and convergence rates to Softmax Cross-Entropy, especially in noisy data scenarios.
Improved active output selection reduces calibration time by 10% or more.
problem Efficiently calibrate models with noisy data.
method Improved active output selection strategy considering noise estimate.
result At least 10% fewer measurements needed compared to existing strategies.
The paper tackles learning true rankings from noisy, incomplete data.
problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.
Expands weak supervision by allowing partial labels from multiple noisy sources.
problem Creating models without labeled data using heuristic labelers.
method Probabilistic generative model estimating partial label accuracies.
result Improved model accuracy on various tasks (8.6% on text, comparable to zero-shot methods on images).
Study reveals latent state computation in stochastic volatility models.
problem Understanding latent stochastic dynamics in noisy, partially observed observations.
method Multivariate stochastic volatility setting, controlled experiments on various architectures.
result Evidence of a two-stage computation: latent state encoding and output head mapping.
Consensus NN learns from noisy data only for medical image denoising.
problem Lack of clean training data for medical image denoising.
method Trains neural network using only noisy data by splitting and combining subsets.
result Improved performance on denoising medical images compared to existing methods.
PSDR improves robustness against noisy labels by penalizing KL divergence between similar inputs.
problem Robust training of DNNs in datasets with noisy labels.
method Introduces PSDR, a manifold regularizer that penalizes KL divergence between similar inputs.
result Significantly improves robustness against noisy labels on benchmark datasets.
Learning theory for linear systems with compositional inputs.
problem Training linear system operators with unknown variables constrained to non-negativity and unity.
method Bayesian inversion method for inferring unknown variable from noisy linear system output.
result Quantified uncertainty in trained operator and convergence rates for various cases.
Study shows exponential gap in sample complexity between noisy and non-noisy recurrent neural networks.
problem Understanding the impact of noise on the sample complexity of recurrent neural networks.
method Analyzing noisy multi-layered sigmoid recurrent neural networks with independent noise and proving lower bounds.
result Exponential gap in sample complexity between noisy and non-noisy networks, even for small noise values.
Study efficient interactive learning for structured outputs with reliable computation.
problem Interactive learning with noisy labels and structured output spaces.
method Identify and utilize CRISPs (probabilistic models) that guarantee reliable and efficient computation of probabilistic quantities.
result CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.
Algorithm identifies bilinear dynamical systems from noisy data.
problem Learning a realization of a partially observed bilinear dynamical system.
method Regression of outputs to highly correlated covariates for Markov-like parameters.
result High probability error bounds on identification algorithm under uniform stability assumption.
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be …
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
problem Medical image denoising often results in loss of fine structures.
method Conditional diffusion model with stabilized reverse sampling and supervised training.
result DiffDenoise outperforms state-of-the-art methods in medical image denoising.
Framework for fair classification with noisy protected attributes and provable guarantees.
problem Fair classification with noisy protected attributes.
method Optimization framework for linear and linear-fractional fairness constraints, handling multiple non-binary attributes.
result Provably fair classifier with minimal accuracy loss, even with large noise.
In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems.We assume that the training outputs are collected from a mixture of a target and correlated noise distributions.Our proposed method simultaneously estimates the target distribution and the qual…
This paper examines error bounds for deep learning classifiers with noisy labels.
problem Understanding the performance of classifiers trained on noisy data.
method Derives error bounds for excess risk, decomposing it into statistical and approximation errors. Uses independent block construction for statistical dependencies and vector-valued setting for approximation error.
result Established theoretical results for error bounds in deep learning with noisy labels, mitigating the impact of high-dimensional input spaces.
Paper studies zero-sum games with noisy observations and identifies equilibrium conditions.
problem Zero-sum games with noisy observations of the leader's actions.
method Analyzes the equilibrium of games with noisy action observability, identifies necessary conditions for uniqueness, and investigates the cardinality of best responses.
result The noisy observations significantly impact the cardinality of the follower's set of best responses, and under certain conditions, this set becomes a singleton almost surely.
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. Recent work [Xu and Raginsky (2017)] has established a bound on the generalization error of empirical risk minimization based on the mutual information $I(S;…
New framework learns physics from output measurements only.
problem Learning governing physics from only output measurements.
method Stochastic calculus, sparse learning, Bayesian statistics, Euler Maruyama scheme.
result Potential to identify governing physics from sparse, noisy, incomplete data.
Analyzes DNNs trained with noisy gradients, finding FWCs negligible for large n.
problem Analyzing DNNs trained with noisy gradients.
method Introduced analytical framework to analyze non-Gaussian stochastic process.
result FWCs negligible for large n, improving CNN performance.
SELF filters noisy labels to improve deep learning performance.
problem Overfitting to noisy labels in deep learning.
method Self-ensemble label filtering (SELF) using running averages of predictions.
result SELF improves task performance by filtering noisy labels dynamically.
We study learning in a noisy bisection model: specifically, Bayesian algorithms to learn a target value V given access only to noisy realizations of whether V is less than or greater than a threshold theta. At step t = 0, 1, 2, ..., the learner sets threshold theta t and observes a noisy realization of sign(V - theta t…
Crowdsourced PAC learning algorithm reduces labeling tasks for noisy data.
problem Learning from noisy labels in crowdsourced data.
method Three-step algorithm combining voting, bandits, and noisy-PAC learning.
result Reduces the number of tasks workers need to label for PAC learning.
We study the Gaussian Process regression model in the context of training data with noise in both input and output. The presence of two sources of noise makes the task of learning accurate predictive models extremely challenging. However, in some instances additional constraints may be available that can reduce the unc…
New algorithms minimize noisy, irregular functions without gradients.
problem Minimizing noisy, irregular, and algebraically intractable functions.
method Generalized gradient descent recursion with smooth approximations.
result Convergence results under weak assumptions on function regularity.
Quantum reservoir computing tackles noisy quantum computers for temporal tasks.
problem Efficiently process input sequences on noisy quantum computers.
method Quantum reservoir computing using dissipative quantum dynamics.
result Small and noisy quantum reservoirs can handle high-order nonlinear temporal tasks.
Generative models have long been the dominant approach for speech recognition. The success of these models however relies on the use of sophisticated recipes and complicated machinery that is not easily accessible to non-practitioners. Recent innovations in Deep Learning have given rise to an alternative - discriminati…
The paper sets sample complexity bounds for learning high-dimensional simplices in noisy data.
problem Learning high-dimensional simplices from noisy data.
method Sample compression techniques and Fourier-based method for noisy observations.
result Established sample complexity bounds for simplex learning in noisy regimes.
Deep learning models can overfit noisy data without losing generalization.
problem Understanding the generalization of deep learning models in noisy data.
method Empirical investigation of epoch-wise double descent in fully connected neural networks trained on CIFAR-10 with 30% label noise.
result The model achieves strong re-generalization on test data after overfitting noisy training data, corresponding to a 'benign overfitting' state.
Paper improves preterm birth prediction using neural networks with noisy labels.
problem Predicting preterm birth from noisy EHR diagnosis codes.
method Developed ALC method to correct label noise in deep learning models.
result Improved prediction performance compared to baseline methods.
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervis…
New algorithms for decision trees with noisy outcomes improve learning efficiency.
problem Learning with noisy outcomes in active learning.
method Approximation algorithms for optimal decision trees with persistent noise.
result Approximation algorithms provide nearly optimal performance guarantees.
FCN improves speech clarity in noisy environments.
problem Improving speech clarity in noisy environments.
method Fully convolutional neural network (FCN) for speech enhancement.
result FCN can generalize to new speakers and robust to varying noise.
NROWAN-DQN improves stability and exploration in noisy networks.
problem Noisy networks struggle with stable exploration in complex tasks.
method Noise reduction and online weight adjustment for stable actions.
result NROWAN-DQN outperforms prior algorithms in stability and exploration.
Paper tackles noisy neural networks and proposes a method to enhance their robustness.
problem Noisy neural networks struggle with random continuous noise in weights.
method Knowledge distillation combined with noise injection during training.
result Models achieve up to twice greater noise tolerance.
Over-parameterized models can memorize noisy labels and still generalize well, revealing a hidden structure.
problem Understanding how over-parameterized models can simultaneously memorize noisy labels and generalize well.
method Investigated through modular arithmetic tasks with label noise using two-layer neural networks.
result Over-parameterized models can achieve near-perfect test accuracy with 80% label noise by extracting an internal generalization structure.
KLIC combines multiple datasets for clustering, down-weighting noisy data.
problem Robustness of COCA in noisy or conflicting datasets.
method Multiple Kernel Learning for Integrative Clustering.
result KLIC down-weights noisy datasets, improving clustering accuracy.
PML-GAN tackles noisy multi-label annotations using adversarial learning.
problem Learning multi-label models from noisy, overcomplete annotations.
method PML-GAN uses a disambiguation network and a generative adversarial network to map noisy labels to clean labels and data samples.
result PML-GAN achieves state-of-the-art performance on partial multi-label learning datasets.