arXiv research

New methods for handling time-varying label noise in time series classification.

HeMPPCAT improves PCA for data with varying noise.

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced per…

The chapter compares Gaussian process models for stochastic simulators with varying noise.

SpecGrad improves neural vocoder sound quality by adapting diffusion noise to log-mel spectrogram.

Neural networks estimate time-varying parameters in AR(p) models with different noise types.

New auto-encoder handles varying noise levels without retraining.

In this note, we present a version of the Thompson sampling algorithm for the problem of online linear generalization with full information (i.e., the experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a Gaussian prior and time-varying Gaussian likelihoods, and we show that it essentially reduces …

New method improves multi-fidelity Bayesian optimization by accounting for local correlations and varying noise.

ALPCAHUS clusters data from multiple subspaces with varying noise.

This paper explores BDL hyperparameters for robust polynomial mapping with noise.

The $\alpha$-Alternator adapts to varying noise levels in sequences, improving robustness and performance.

LAAT detects multiple low-density manifolds in noisy data.

Improved GP bandit algorithms for noiseless, varying noise, and RKHS norms.

Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …

Improved robust latent variable estimation for neural dynamics.

Develops a method to model multivariate count processes with Cox processes and shot noise intensities.

CNNs learn about input data uncertainties, improving classification performance.

Generative model handles varying data dimensions using jump diffusion processes.

The study tackles rough noise in high-frequency financial data using fractional Brownian motion.

Unified Bayesian framework for LTV system identification using neural networks and Gaussian Processes.

We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of $O(1/k)$ with the constant term depending on the numb…

Minimal assumptions analysis of Q-learning with time-varying policies.

Robust Kalman filter for corrupted measurements.

Slow feature analysis (SFA) is a method for extracting slowly varying features from a quickly varying multidimensional signal. An open source Matlab-implementation sfa-tk makes SFA easily useable. We show here that under certain circumstances, namely when the covariance matrix of the nonlinearly expanded data does not …

Study shows that ridgeless Gaussian kernel regression overfits even with varying bandwidth or dimensionality.

A novel topological method analyzes fMRI data over time.

In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the $\ell^1$ norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predict…

Online L2D algorithm for multiclass classification with varying experts.

Proposes a new loss function for learning with noisy labels.

We revisit the Bayesian online inference problems for the linear dynamic systems (LDS) under non- Gaussian environment. The noises can naturally be non-Gaussian (skewed and/or heavy tailed) or to accommodate spurious observations, noises can be modeled as heavy tailed. However, at the cost of such noise robustness, the…

GNIs induce asymmetric heavy-tailed noise in SGD, affecting network performance.

A method to approximate instance-dependent label noise using instance-confidence embedding.

The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights. Fuzzy damping coefficients are obtained for each nucleus or center p…

SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.

In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Ito semimartingales, and discuss how to measure liquidity risk using high frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility…

This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an $N$-agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents…

Study improves model robustness in noisy datasets.

The paper proposes a new SDF scaled by time-varying volatility from S&P 500 options.

Study improves parameter estimation for SDEs driven by Levy noise.

Noise can stabilize systemic risk models with uncertain robustness.

We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of multiple objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and ca…

We address noisy Euclidean distances in high dimensions, estimating noise levels and correcting distances.

Improved privacy and utility in machine learning with adaptive differential privacy.

New method infers causal relationships from nonstationary time series data.

New research shows that binary classification can be done with noisy data, but only if there are clean samples available.

Local EGOP learns functions varying along a few directions.

New method uses generative models to improve phase retrieval stability.