Tootfinder

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng, Hubert P. H. Shum
https://arxiv.org/abs/2507.07134

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performa…

USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
Jiaqi Zheng, Cheng Guo, Yi Cao, Chaoqun Hou, Tong Liu, Bo Zheng
https://arxiv.org/abs/2507.06503

USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
Large-scale homepage recommendations face critical challenges from pseudo-negative samples caused by exposure bias, where non-clicks may indicate inattention rather than disinterest. Existing work lacks thorough analysis of invalid exposures and typically addresses isolated aspects (e.g., sampling strategies), overlooking the critical impact of pseudo-positive samples - such as homepage clicks merely to visit marketing portals. We propose a unified framework for large-scale homepage recommendat…

On the Reliability of Sampling Strategies in Offline Recommender Evaluation
Bruno L. Pereira, Alan Said, Rodrygo L. T. Santos
https://arxiv.org/abs/2508.05398 https://

On the Reliability of Sampling Strategies in Offline Recommender Evaluation
Offline evaluation plays a central role in benchmarking recommender systems when online testing is impractical or risky. However, it is susceptible to two key sources of bias: exposure bias, where users only interact with items they are shown, and sampling bias, introduced when evaluation is performed on a subset of logged items rather than the full catalog. While prior work has proposed methods to mitigate sampling bias, these are typically assessed on fixed logged datasets rather than for the…

Meter-scale Observations of Equatorial Plasma Turbulence
Magnus F Ivarsen, Lasse B N Clausen, Yaqi Jin, Jaeheung Park
https://arxiv.org/abs/2506.08665 http…

Meter-scale Observations of Equatorial Plasma Turbulence
The multi-Needle Langmuir Probe collects an electron current through four fixed-bias cylindrical copper needles. This allows for an extremely high sampling frequency, with plasma properties being inferred through polynomial fitting in the current-voltage plane. We present initial results from such a multi-needle probe mounted on the International Space Station, orbiting Earth at an altitude of around 400 km. That altitude, and its orbital inclination (~50 degrees), place the ISS as a suitable p…

Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling
Yunsoo Ha, Juliane Mueller
https://arxiv.org/abs/2508.03901 https://arxiv.org/pdf/2508…

Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling
Simulation optimization is often hindered by the high cost of running simulations. Multi-fidelity methods offer a promising solution by incorporating cheaper, lower-fidelity simulations to reduce computational time. However, the bias in low-fidelity models can mislead the search, potentially steering solutions away from the high-fidelity optimum. To overcome this, we propose ASTRO-MFDF, an adaptive sampling trust-region method for multi-fidelity simulation optimization. ASTRO-MFDF features two …

This https://arxiv.org/abs/2412.04338 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Numerical Aspects of Large Deviations
An introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the text. Next, Markov chain Monte Carlo (MCMC) simulations are introduced. In particular, the Metropolis-Hastings algorithm is explained. As first implementation of MCMC, sampling of the plain Bernoulli model is shown. Next, an exponential bias is used for the same model, which allows one to obtain the ta…

This https://arxiv.org/abs/2502.13583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
A substantial body of work in machine learning (ML) and randomized numerical linear algebra (RandNLA) has exploited various sorts of random sketching methodologies, including random sampling and random projection, with much of the analysis using Johnson--Lindenstrauss and subspace embedding techniques. Recent studies have identified the issue of inversion bias -- the phenomenon that inverses of random sketches are not unbiased, despite the unbiasedness of the sketches themselves. This bias pres…

This https://arxiv.org/abs/2412.12405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalized entropy calibration for analyzing voluntary survey data
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first establish the relationship between the generalized calibration weighting and its dual expression for regression estimation. The dual relations…

Entropy-Based Methods to Address Sampling Bias in Archaeological Predictive Modeling
Mehmet S{\i}dd{\i}k \c{C}ad{\i}rc{\i}, Golnaz Shahtahmassebi
https://arxiv.org/abs/2508.02272

Entropy-Based Methods to Address Sampling Bias in Archaeological Predictive Modeling
Predictive modeling in archaeology is essential for the understanding of people's behavior in the past and for guiding heritage conservation. However, spatial sampling bias caused by uneven research effort can severely limit model reliability. This research describes a novel new framework that integrates entropy-based corrections to measure and minimize such biases in archaeological modeling of foresight. Leveraging the open access data of the Grand Staircase-Escalante National Monument, we emp…

The seeds of the future are in the present: A blind exploration of metastable states
Timoth\'ee Devergne, Vladimir Kostic, Massimiliano Pontil, Michele Parrinello
https://arxiv.org/abs/2508.01477

The seeds of the future are in the present: A blind exploration of metastable states
In this work, we present a novel type of molecular dynamics simulation that aims at discovering, in a blind way, new metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural…

MambaRate: Speech Quality Assessment Across Different Sampling Rates
Panos Kakoulidis, Iakovi Alexiou, Junkwang Oh, Gunu Jho, Inchul Hwang, Pirros Tsiakoulis, Aimilios Chalamandaris
https://arxiv.org/abs/2507.12090

MambaRate: Speech Quality Assessment Across Different Sampling Rates
We propose MambaRate, which predicts Mean Opinion Scores (MOS) with limited bias regarding the sampling rate of the waveform under evaluation. It is designed for Track 3 of the AudioMOS Challenge 2025, which focuses on predicting MOS for speech in high sampling frequencies. Our model leverages self-supervised embeddings and selective state space modeling. The target ratings are encoded in a continuous representation via Gaussian radial basis functions (RBF). The results of the challenge were ba…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
Niklas Baumgarten, David Schneiderhan
https://arxiv.org/abs/2506.02647 https:/…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
We present a multilevel stochastic gradient descent method for the optimal control of systems governed by partial differential equations under uncertain input data. The gradient descent method used to find the optimal control leverages a parallel multilevel Monte Carlo method as stochastic gradient estimator. As a result, we achieve precise control over the stochastic gradient's bias, introduced by numerical approximation, and its sampling error, arising from the use of incomplete gradients, wh…