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.
JRFs improve semi-supervised learning by balancing generation and classification.
Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse map…
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
The paper compares theoretical and empirical performance of imputation methods for missing data.
We propose a Bayesian methodology for one-mode projecting a bipartite network that is being observed across a series of discrete time steps. The resulting one mode network captures the uncertainty over the presence/absence of each link and provides a probability distribution over its possible weight values. Additionall…
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are notoriously hard to train and can suffer from the problem of missing modes where the model is not able to produce examples in certain regions o…
New model clusters mixed-type data with missing values, improving air quality analysis.
Many generative models have to combat . The conventional wisdom to this end is by reducing through training a statistical distance (such as -divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distanc…
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.
New method for tensor classification with missing data.
Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can …
The Dynamic Mode Decomposition (DMD) extracted dynamic modes are the non-orthogonal eigenvectors of the matrix that best approximates the one-step temporal evolution of the multivariate samples. In the context of dynamical system analysis, the extracted dynamic modes are a generalization of global stability modes. We a…
We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as particular cases where the pattern of missing data is independent of the sequence index. The problem is hard because it involves possibly m…
A new method combines MCMC results to avoid failures in parallel computing.
Paper proposes a new model for imputing missing spatiotemporal traffic data.
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without re…
missForestPredict fills missing data for prediction models quickly and accurately.
We study the problem of low-rank tensor factorization in the presence of missing data. We ask the following question: how many sampled entries do we need, to efficiently and exactly reconstruct a tensor with a low-rank orthogonal decomposition? We propose a novel alternating minimization based method which iteratively …
Paper proposes a new tensor imputation method for spatiotemporal traffic data with missing patterns.
Measures mode separation in high-dimensional densities via a reversible diffusion process.
New method for tensor completion from specific mode observations.
Paper introduces a method to evaluate abstaining classifiers by considering missing predictions as counterfactuals.
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifact…
Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both …
MISS identifies influential subsets in ML models.
LLMs generate answers under incomplete context, and their uncertainty should scale with missing information.
Proposes a method to recover sparse tensors with covariate info.
Coupled tensor decomposition reveals the joint data structure by incorporating priori knowledge that come from the latent coupled factors. The tensor ring (TR) decomposition is invariant under the permutation of tensors with different mode properties, which ensures the uniformity of decomposed factors and mode attribut…
Feedback alignment methods need to be evaluated for accuracy and gradient cosine similarity.
Deep latent variable models (DLVMs) combine the approximation abilities of deep neural networks and the statistical foundations of generative models. Variational methods are commonly used for inference; however, the exact likelihood of these models has been largely overlooked. The purpose of this work is to study the g…
Modal regression estimates the local modes of the distribution of given , instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of …
A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by mult…
SNI framework for mixed-type data imputation interprets and explains missing values.
LoRA-Curve connects independent LoRA optima through continuous low-loss valleys, improving Bayesian model averaging.
The two key players in Generative Adversarial Networks (GANs), the discriminator and generator, are usually parameterized as deep neural networks (DNNs). On many generative tasks, GANs achieve state-of-the-art performance but are often unstable to train and sometimes miss modes. A typical failure mode is the collapse o…
Graph convolutional networks fail to use eigenvectors beyond the first, unlike spectral embedding.
Enhances diffusion-based sampling for molecular systems.
Periodic surfaces have a limited number of bending modes, equal to their membrane modes.
In this article, we study the convergence of Mirror Descent (MD) and Optimistic Mirror Descent (OMD) for saddle point problems satisfying the notion of coherence as proposed in Mertikopoulos et al. We prove convergence of OMD with exact gradients for coherent saddle point problems, and show that monotone convergence on…
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
Motivated by advantages of current-mode design, this brief contribution explores the implementation of weight matrices in neuromemristive systems via current-mode memristor crossbar circuits. After deriving theoretical results for the range and distribution of weights in the current-mode design, it is shown that any we…
For dynamical systems that can be modelled as asymptotically stable linear systems forced by Gaussian noise, this paper develops methods to infer or estimate their modes from observations in real time. The modes can be real or complex. For a real mode, we wish to infer its damping rate and mode shape. For a complex mod…
Characterizes neutral deformation modes of minimal surfaces.
Mathematical analysis shows annealing prevents mode collapse in Gaussian mixtures.
Parsimonious Dynamic Mode Decomposition selects sparse modes robustly.
BSFP method reveals latent patterns in multi-omic data for predicting lung function in HIV-associated OLD.
Geodesics connect model modes in neural network loss landscapes.