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.
FJS method improves multinomial classification accuracy.
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
Estimates joint causal effects using single-variable interventions on nonlinear models.
SJS model predicts label shifts in multinomial datasets.
We discuss possible extensions of the classical Chern-Weil formalism to an infinite dimensional setup. This is based on joint work with Steven Rosenberg, joint work with Simon Scott and joint work with Jouko Mickelsson.
Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class of the sample and another one that is assumed independent across samples and models the within-c…
NullSpaceNet maps inputs to a joint-nullspace for clearer class separability.
Study approximates top Lyapunov exponents for surface mapping classes.
Proposes method for eliciting non-parametric joint priors using normalizing flows.
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only. Instead, we consider the extended setup of …
Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal processing techniques rely on such a simplified model. As this model is often unknown for many classes of the signals, we need to select su…
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes shou…
The paper evaluates joint life insurance risk under dependence uncertainty using copulas and convex risk measures.
New model learns from missing modalities and class labels.
The paper explores the relationship between joint mixability and negative dependence structures.
Develops noncommutative Cowen-Douglas theory for noncommuting operators.
COBRA reduces modality gap in cross-modal tasks.
Paper proposes a new method to evaluate joint risk under uncertainty.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
This paper introduces a neural operator for probabilistic conditioning.
Generative model for joint discrete distributions using randomized assignment flows.
Unified theorem for deep and shallow joint-equivariant machines.
Introduces joint exclusivity (JE), a new form of negative dependence.
We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domain are aligned, but the labels as wel…
LSDM uses unpaired data to match latent space distributions for generative modeling.
Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the condi…
The paper provides formulas for volatility in various models, including rough volatility.
A new model fits SPX and VIX volatility surfaces and term structures efficiently.
In a stochastic volatility framework, we find a general pricing equation for the class of payoffs depending on the terminal value of a market asset and its final quadratic variation. This allows a pricing tool for European-style claims paying off at maturity a joint function of the underlying and its realised volatilit…
LADD models improve discrete diffusion for faster language generation.
This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination of the previous two classes) are placed in a common framework. While model classes used in the litera…
Directed latent variable models that formulate the joint distribution as have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify , often with a simple fixed prior that limits the expressiveness of the model. Undirected latent variabl…
Study proposes a new model for joint survival annuity valuation.
CEBMs learn flexible latent mappings from data.
Introduces joint Shapley values to measure feature importance in models.
The paper studies Fourier-Laplace transforms in polynomial OU volatility models for option pricing.
This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature …
We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classes of such samples. The JCM procedure has been proposed as a method for the unsupervised classification of cells within a sample into a numbe…
Joint diffusion models improve data representation for both generation and prediction.
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the formulation of this problem as group graphical lasso. This paper proposes a novel hybrid covariance thresholding algorithm that can effecti…
Causal discovery predicts unobserved joint statistics from observed data.
Paper establishes identifiability and elicitability of tail risk measures.
In an earlier work joint with X. X. Chen and G. Tian, we introduced the weak Kähler-Ricci flow for various geometric motivations. In the current work, we take further consideration on setting up the weak flow. Namely, the initial class is allowed to be no longer Kähler.
We formulate a new class of conditional generative models based on probability flows. Trained with maximum likelihood, it provides efficient inference and sampling from class-conditionals or the joint distribution, and does not require a priori knowledge of the number of classes or the relationships between classes. Th…
Fisher auto-encoders use Fisher divergence for more robust generative modeling.
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structu…
We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures ma…
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the generative mo…