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.
Extends CRR model with q-binomial random walks for asset pricing.
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…
In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical ag…
Novel oracle-type inequality for logistic loss in DNNs achieves sharp convergence rates.
Paper derives convergence rates for NPMLE in Hellinger distance using deep neural networks.
BAMS uses Bayesian sampling to discover AV failures more efficiently and accurately.
Enhances systemic risk analysis by incorporating debt valuation factors.
Study extreme-case Value-at-Risk under IFR distributions, providing guidance for risk management.
The paper proves neural networks' consistency and optimal convergence rates for various function classes.
The paper establishes convergence rates for MoE models in classification problems.
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learni…
A method for safe online classification reduces test costs while maintaining low error rates.
Study shows deep linear networks can converge to flatter minima at large learning rates.
Gradient descent with logistic loss can interpolate deep networks with smoothed ReLU activations under certain conditions.
The study examines how class imbalance impacts logistic regression models in low-default credit portfolios.
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal P…
SGD converges globally to logistic loss minima for two-layer nets.
This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent e…
FOLKLORE algorithm speeds up online multiclass logistic regression.
Study predicts P2P lending platform failures using machine learning.
We consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices. The generative model is formulated as a matrix logistic regression. The performance of the model is analysed in a high-dimensional regime under a structural assumption. The mi…
This paper examines SVB's failure and its impact on bank stocks.
Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data. The direction of this ray is the maximum margin predictor of a maximal linearly separable subset of the data; the gradient descent iterates converge to this ray in direction a…
Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.
Gradient descent converges to a small neighborhood of the true parameter in logistic regression with Gaussian design.
Modeling bank leverage dynamics to understand systemic risk in financial markets.
Locally learned synaptic failure enables complete Bayesian inference.
Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to classification and boosting. Existing "fast rates" for this setting exhibit exponential dependence on the predictor norm, and Hazan et al. (20…
We improve MoE models for classification with rigorous guarantees and practical methods.
In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithm…
Self-attention optimizers converge to optimal weights, revealing bias patterns.
Prior-weighted logistic regression has become a standard tool for calibration in speaker recognition. Logistic regression is the optimization of the expected value of the logarithmic scoring rule. We generalize this via a parametric family of proper scoring rules. Our theoretical analysis shows how different members of…
New research shows Byzantine failures hurt generalization more than data poisoning in robust distributed learning.
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th…
For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR). First, we propose two algorithms based on the Hybrid Generalized Approximate Message Passing (HyGAMP) framework: one finds the maximum a posterio…
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for …
We consider high-dimensional binary classification by sparse logistic regression. We propose a model/feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the non-asymptotic bounds for the resulting misclassification excess risk. The bounds can be reduc…
We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, a…
New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.
A new hybrid Newton algorithm improves convergence in logistic regression.
Study improves covariance estimation for SGD under Markovian data, matching best rates.
Large stepsize GD for logistic regression converges faster than expected.
Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally prohibitive for large datasets. Here we adapt ideas from symbolic data analysis to summarise the collection of predictor variables into his…
Stochastic Gradient Descent (SGD) is a central tool in machine learning. We prove that SGD converges to zero loss, even with a fixed (non-vanishing) learning rate - in the special case of homogeneous linear classifiers with smooth monotone loss functions, optimized on linearly separable data. Previous works assumed eit…
New bounds on trajectory safety in training models with Langevin Dynamics.
The paper explores how benign overfitting occurs in heavy-tailed input distributions.
The problem of stock hedging is reconsidered in this paper, where a put option is chosen from a set of available put options to hedge the market risk of a stock. A formula is proposed to determine the probability that the potential loss exceeds a predetermined level of Value-at-Risk, which is used to find the optimal s…
Efficiently preserves privacy in logistic regression for IoT data.