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.
The paper classifies symmetric triads with multiplicities and their applications.
Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.
MKLpy simplifies Multiple Kernel Learning in Python.
The paper tackles fair classification with multiple sensitive features.
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial…
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from …
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with confl…
Unified method for learning from selectively labeled data.
Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last d…
Physiological signals that provide the objective repression of human affective states are attracted increasing attention in the emotion recognition field. However, the single signal is difficult to obtain completely and accurately description for emotion. Multiple physiological signals fusing models, building the unifo…
Classifies compact multiplicity free quasi-Hamiltonian manifolds.
The paper extends multiple instance learning to multiclass and regression problems.
Paper presents an efficient algorithm for learning minimax risk classifiers with large-scale data.
New PAC-Bayes bound controls multiple error types simultaneously.
Logistic regression can handle noisy labels effectively when labels are imperfectly assigned by multiple experts.
The classification work [5], [9] left unsettled only those anomalous isoparametric hypersurfaces with four principal curvatures and multiplicity pair or in the sphere. By systematically exploring the ideal theory in commutative algebra in conjunction with the geometry of isoparametric hypers…
The paper proposes a novel MKL approach for OCC using -norm constraints.
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currentl…
Enhances graph classification with multiple graphs.
This research improves multimodal systems by adding a second objective and regularisation methods.
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
Paper proposes a time-frequency analysis method for blind modulation classification in MIMO systems.
Developed a multiway classification method for sparse data.
We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.
Framework uses dropout to efficiently explore Rashomon set for multiplicity estimation.
With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from resea…
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on CNNs for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epo…
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coe…
New loss function improves classification for imbalanced and sensitive groups.
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…
Paper proposes GEG to enhance fairness in binary and multi-class classification.
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences. Standard supervised learning algorithms assume accurate in…
Improved signal classification using multiple wavelets and their smooth coefficients.
We study the classification problem of singularities of function-germs with harmonic leading terms of two variables under the right-equivalence. We observe that the multiple actions of Laplacian appear for the classifications of such class of function-germs.
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we …
New classifier combines locally linear kernels for fast and accurate non-linear classification.
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probabil…
Proposes sigmoidF1 loss for multilabel classification, improving performance metrics.
Tree-based algorithm for functional data analysis reduces generalization error.
The paper develops distribution-free methods for ordinal classification.
Let be a connected symplectic manifold on which a connected Lie group acts properly and in a Hamiltonian fashion with moment map $μ:M \lra \mf g^*$. Our purpose is investigate multiplicity-free actions, giving criteria to decide a multiplicity freenes of the action. As an application we give the complete cl…
Expands weak supervision by allowing partial labels from multiple noisy sources.
In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain cla…
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying late…
We study the classification problem of singularities of function-germs with harmonic leading terms of two variables under the right-equivalence. We study the classification in the cases that the order of function-germs is at most 7. Moreover, we observe that the multiple actions of Laplacian appear for the classificati…
Efficient algorithm for evaluating hierarchical classification methods at multiple operating points.
This paper classifies Hamiltonian actions by symplectic groupoids using Delzant subspaces.