A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes DISCO, the first CVI for density-based clustering with noise.
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which plays a key role in many RL algorithms, are either prone to noise or inefficient. To solve this issue, we introduce a novel policy evaluation a…
Proposes incorporating noise sources in machine learning evaluation for more reliable conclusions.
Measures three types of noise in LLM evaluations.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
Hill-climbing is a powerful baseline for NAS, even with reduced noise.
Study on noise models for noisy labels in NLP.
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
Paper tackles instance-dependent label noise by approximating it with part-dependent noise.
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
Noise titration benchmarks time series forecasting models rigorously.
Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective evaluations are usually inaccurate, because noise is almost inevitable in real world, and it is a crucial issue to weaken the negative effect …
In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations. Typically, the observation noise is assumed to be independent of the evaluation point and to satisfy a tail bound uniformly on the domain; a restrictive assumption for many applications. In this work, we c…
Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of…
RID-Noise improves robust design under noisy conditions using neural networks.
As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the po…
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoof…
Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In an effort to understand the functional purpose of correlated variability, we implement and evaluate correlated noise models in deep convolu…
Empirical mode modeling improves state-space analysis of noisy data.
Image classifiers often suffer from adversarial examples, which are generated by strategically adding a small amount of noise to input images to trick classifiers into misclassification. Over the years, many defense mechanisms have been proposed, and different researchers have made seemingly contradictory claims on the…
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 …
TrustNet robustly learns noise patterns from trusted data to improve weakly-supervised classification.
Survey on deep learning robust training methods for noisy labels.
Study uses Bayesian Optimization to analyze noise effects in materials research.
This paper connects noise injection to Bayesian inference for neural networks, improving model uncertainty.
We present a data-efficient reinforcement learning algorithm resistant to observation noise. Our method extends the highly data-efficient PILCO algorithm (Deisenroth & Rasmussen, 2011) into partially observed Markov decision processes (POMDPs) by considering the filtering process during policy evaluation. PILCO conduct…
Study benchmarks label noise detection methods, identifying best practices.
A novel feature selection method using noise-based hypothesis testing improves feature selection accuracy.
S-SGD adds symmetrical noise to weights to avoid sharp minima in deep learning.
Proposes a method to improve classification robustness against label noise.
A new gradient estimator for online optimization with two function evaluations.
Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that - Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; un…
Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory ta…
This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised fea…
New model accounts for scale variation and noise in pairwise comparisons.
New auto-encoder handles varying noise levels without retraining.
Antithetic noise improves diffusion models' uncertainty quantification.
Deep-neural-network (DNN) based noise suppression systems yield significant improvements over conventional approaches such as spectral subtraction and non-negative matrix factorization, but do not generalize well to noise conditions they were not trained for. In comparison to DNNs, humans show remarkable noise suppress…
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. …
We consider the problem of training a model under the presence of label noise. Current approaches identify samples with potentially incorrect labels and reduce their influence on the learning process by either assigning lower weights to them or completely removing them from the training set. In the first case the model…
The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source localization method. Utilizing the common assumption of disjoint speaker activities, we propose a novel method to train the CNN using synth…
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifi…
Extends neural network training framework to handle noise and uncertainty.
New approach uses under-trained deep ensembles to learn from noisy labels.
Stochastic differential equation approximation for linear TD(0) under Markovian noise
We propose a robust method to estimate heteroscedastic noise models using Student's t-distribution.
Noise-aware DP inference improves accuracy for complex models.