Tootfinder

Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…

Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification -- a property known as adversarial consistency. However, thes…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
Aina Zhang, Xiaoming Kong, Bowen Liu, Nan Li, Yude Bu, Zhenping Yi, Meng Liu
https://arxiv.org/abs/2506.16090

Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. Thi…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Mingjie Chen (Zhejiang University), Tiancheng Zhu (Huazhong University of Science,Technology), Mingxue Zhang (The State Key Laboratory of Blockchain,Data Security, Zhejiang University,Hangzhou High-Tech Zone), Yiling He (University College London), Minghao Lin (University of Southern California), Penghui Li (Columbia University), Kui Ren (The State Key Laboratory of Blockchain,Data Security, Z…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, the…

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

DCE-LLM: Dead Code Elimination with Large Language Models
Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu
https://arxiv.org/abs/2506.11076 https://

DCE-LLM: Dead Code Elimination with Large Language Models
Dead code introduces several challenges in software development, such as increased binary size and maintenance difficulties. It can also obscure logical errors and be exploited for obfuscation in malware. For LLM-based code-related tasks, dead code introduces vulnerabilities that can mislead these models, raising security concerns. Although modern compilers and IDEs offer dead code elimination, sophisticated patterns can bypass these tools. A universal approach that includes classification, loc…

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Binghao Ye, Wenjuan Li, Dong Wang, Man Yao, Bing Li, Weiming Hu, Dong Liang, Kun Shang
https://arxiv.org/abs/2506.05679

Integer Binary-Range Alignment Neuron for Spiking Neural Networks
Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is a…

Repulsive particle interactions enable selective information processing at cellular interfaces
Jenna Elliott, Hiral Shah, Roman Belousov, Gautam Dey, Anna Erzberger
https://arxiv.org/abs/2506.14739

Repulsive particle interactions enable selective information processing at cellular interfaces
Living systems relay information across membrane interfaces to coordinate compartment functions. We identify a physical mechanism for selective information transmission that arises from the sigmoidal response of surface-bound particle densities to spatial features of adjacent external structures. This mechanism implements a form of spatial thresholding, enabling the binary classification of external cues. Expansion microscopy measurements of nuclear pore complex distributions in S. arctica show…

Optimal Convergence Rates of Deep Neural Network Classifiers
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
https://arxiv.org/abs/2506.14899 https://

Optimal Convergence Rates of Deep Neural Network Classifiers
In this paper, we study the binary classification problem on $[0,1]^d$ under the Tsybakov noise condition (with exponent $s \in [0,\infty]$) and the compositional assumption. This assumption requires the conditional class probability function of the data distribution to be the composition of $q+1$ vector-valued multivariate functions, where each component function is either a maximum value function or a Hölder-$β$ smooth function that depends only on $d_*$ of its input variables. Notably, $d_…

This https://arxiv.org/abs/2401.03276 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on using BNL and MNL for classification problems, that is, to ``better predict the behavior of new samples'' when…

I think strong and weak typing in programming languages is actually a spectrum rather than a binary classification.
See terraform for example:
> All values have a type, which dictates where that value can be used and what transformations can be applied to it.
https://developer.hashicorp…

Types and Values - Configuration Language | Terraform | HashiCorp Developer
Learn about value types and syntax, including string, number, bool, list, and map. Also learn about complex types and type conversion.

Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Junzhe Jiang, Chang Yang, Xinrun Wang, Bo Li
https://arxiv.org/abs/2506.03153

Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-en…

Universal Rates of ERM for Agnostic Learning
Steve Hanneke, Mingyue Xu
https://arxiv.org/abs/2506.14110 https://arxiv.org/pdf/2506.14…

Universal Rates of ERM for Agnostic Learning
The universal learning framework has been developed to obtain guarantees on the learning rates that hold for any fixed distribution, which can be much faster than the ones uniformly hold over all the distributions. Given that the Empirical Risk Minimization (ERM) principle being fundamental in the PAC theory and ubiquitous in practical machine learning, the recent work of arXiv:2412.02810 studied the universal rates of ERM for binary classification under the realizable setting. However, the ass…

This https://arxiv.org/abs/2112.13738 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluation of binary classifiers for asymptotically dependent and independent extremes
Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic…

This https://arxiv.org/abs/2505.17282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Attention with Trained Embeddings Provably Selects Important Tokens
Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent. Specifically, we consider a one-layer softmax attention model with a linear head for binary classification, i.e., $\texttt{Softmax}( p^\top E_X^\top ) E_X v = \frac{ \sum_{i=1}^T \exp(p^\top E_{x_i}) E_{x_i}^\top v}{\sum_{j=1}^T \exp(p^\top E_{x_{j…

This https://arxiv.org/abs/2409.16322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

On the Within-class Variation Issue in Alzheimer's Disease Detection
Alzheimer's Disease (AD) detection employs machine learning classification models to distinguish between individuals with AD and those without. Different from conventional classification tasks, we identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. Therefore, simplistic binary AD classification may overlook two crucial aspects: within-class heterogeneity and instance-level imbalance. In this work, we found usi…