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.
Study calculates Bayes risk for semi-supervised learning with uncertain labels.
Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…
Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains. We design a new training algorithm, which is robust to missing or ambiguous labels. The main idea is to intentionally corrupt the labels of generated examples t…
We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the cl…
The paper tackles multi-label ranking with uncertain probabilities.
Improved drug-protein interaction prediction using FTL method.
SoQal reduces oracle label requests in active learning by up to 35%.
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels. In this p…
Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point…
SelectMix improves deep learning robustness against noisy labels.
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal an…
Study improves resilience against adversarial clean-label attacks in real and noisy settings.
New method improves deep learning models in noisy label classification.
OPAL optimizes labeling strategy for precise inference from uncertain models.
The paper explores how to handle uncertain evidence in probabilistic models.
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class classification problems, many real-world applications fall into the multi-label setting in which on…
New method calculates Shapley values for uncertain functions.
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time. We propose a novel space decomposition method to solve G-ZSL. Some previous models with space decomposition operations only calibrate the confident pr…
New method uses conformalization to create classification regions from ambiguous labels.
In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…
In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared:…
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical lab…
A robust multiclass SVM tackles imbalanced data uncertainty.
Quantum methods model uncertain volatility in financial markets.
New model predicts dynamic volatility in uncertain financial markets.
The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …
This paper extends transfer learning for linear regression to uncertain domain information.
The study of model bias and variance with respect to decision boundaries is critically important in supervised classification. There is generally a tradeoff between the two, as fine-tuning of the decision boundary of a classification model to accommodate more boundary training samples (i.e., higher model complexity) ma…
New algorithm for uncertain time series classification.
Affective computing has become a very important research area in human-machine interaction. However, affects are subjective, subtle, and uncertain. So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract. Thus, dimensionality reductio…
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
UNREAL selectively ensembles distinct models to improve active learning performance.
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe…
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…
This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.
A method for accurate pricing of multidimensional derivatives under uncertain volatility.
The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.
Bayesian Gaussian process models handle uncertain data locations in PDE approximations.
FDR-SVM improves classification robustness in federated learning with uncertain data.
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
SADA safely combines predictions from various models for semi-supervised learning.
In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of…
Model quantifies uncertainty's impact on European option prices.
IDT learns human preferences from uncertain decisions, even when humans are suboptimal.
The paper tackles robust control with uncertain dependence using data-driven methods.
Study of multi-task semi-supervised learning in high dimensions.
Active learning selects most informative unlabeled samples for labeling.