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.
ContrastiveVI+ models CRISPR screens with noisy guide efficiency.
Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational distribution and minimizes its KL divergence to the posterior. As the minimization can only be carried out approximately, this approximation i…
The paper learns perturbation sets from data to improve robustness in machine learning.
SAMS-VAE models cellular perturbations using sparse additive mechanisms.
Novel framework predicts cell responses to perturbations using GRNs.
This paper analyzes -Variational Classifiers for robustness and adversarial perturbation detection.
SVAT reduces investment risks by making stock models sensitive to adversarial perturbations.
Eigenvalues of Steklov eigenproblems change predictably with boundary tweaks.
Investigates fluid flow perturbations using geometric theory.
The paper modifies Vafa-Witten equations on 4-manifolds for better solution estimates.
The paper constructs new bimetric conformal invariants using metric perturbations.
Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to analyti…
New method enhances neural network robustness against adversarial attacks.
New model accounts for scale variation and noise in pairwise comparisons.
DualVDT improves time-series forecasting with a novel dual reparametrized structure.
We investigate the dynamic stability of the indirect utility process associated with a (possibly suboptimal) trading strategy under perturbations of the market. Establishing the reverse conjugacy characterizations first, we prove continuity and first-order convergence of the indirect-utility process under simultaneous …
Black box variational inference (BBVI) with reparameterization gradients triggered the exploration of divergence measures other than the Kullback-Leibler (KL) divergence, such as alpha divergences. In this paper, we view BBVI with generalized divergences as a form of estimating the marginal likelihood via biased import…
We present another proof of the sharp inequality for Paneitz operator on the standard three sphere, in the spirit of subcritical approximation for the classical Yamabe problem. To solve the perturbed problem, we use a symmetrization process which only works for extremal functions. This gives a new example of symmetriza…
The paper connects neural network ensembles to Bayesian inference using variational methods.
Proposes a robust VIB approach using soft labels and mutual info estimation.
The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years. They have been developed as denoising, clustering and generative tools, highlighting a large potential in a wide range of fields. Their embeddings are able to extract relevant information from highly dim…
New framework robustly handles outliers in Wasserstein DRO for better decision-making.
DefenseVGAE defends graph neural networks against adversarial attacks.
In this paper we introduce a family of stochastic gradient estimation techniques based of the perturbative expansion around the mean of the sampling distribution. We characterize the bias and variance of the resulting Taylor-corrected estimators using the Lagrange error formula. Furthermore, we introduce a family of va…
Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typically combine a random noise vector with information about the previous poses. This combination, however, is done in a deterministic manner,…
Deep neural networks perform well on real world data but are prone to adversarial perturbations: small changes in the input easily lead to misclassification. In this work, we propose an attack methodology not only for cases where the perturbations are measured by norms, but in fact any adversarial dissimilarit…
Robust statistics traditionally focuses on outliers, or perturbations in total variation distance. However, a dataset could be corrupted in many other ways, such as systematic measurement errors and missing covariates. We generalize the robust statistics approach to consider perturbations under any Wasserstein distance…
Coordinating multiple interacting agents to achieve a common goal is a difficult task with huge applicability. This problem remains hard to solve, even when limiting interactions to be mediated via a static interaction-graph. We present a novel approximate solution method for multi-agent Markov decision problems on gra…
In this paper we consider the coupled system given by the first variation of the conformal Dirac-Einstein functional. We will show existence of solutions by means of perturbation methods.
The paper calculates variations of Einstein-Hilbert action on CR manifolds.
The paper improves SVM and localized SVM stability under triple perturbations.
Study magnetic geodesics on Kähler potentials using variational methods.
Study on combustion theory solutions, proving nondegeneracy and stability in limit.
A new method for privacy-preserving Bayesian learning in federated learning.
For hyperbolic 3-manifolds, Ruelle zeta function vanishing order is 4 minus Betti number.
Certifiably robust VAEs are trained with bounds on input perturbations.
Variational Optimization forms a differentiable upper bound on an objective. We show that approaches such as Natural Evolution Strategies and Gaussian Perturbation, are special cases of Variational Optimization in which the expectations are approximated by Gaussian sampling. These approaches are of particular interest …
We investigate the linear stability of Kähler-Ricci solitons for perturbations induced by varying the complex structure within a fixed Kähler class. We calculate stability for the known examples of Kähler-Ricci solitons.
Develops a new robustness criterion for VAEs and provides theoretical guarantees.
In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information. We focus on learning classifica…
New optimal surfaces found in Heisenberg group defy Euclidean sphere optimality.
It is shown in the paper "Variational Properties of the Gauss-Bonnet Curvatures" of M.L. Labbi, that metrics with constant 2k-Gauss-Bonnet curvature on a closed n-dimensional manifold, 1<2k<n, are critical points for a certain Hilbert type functional with respect to volume preserving conformal variations. This motivate…
We study the (massless) Dirac operator on a 3-sphere equipped with Riemannian metric. For the standard metric the spectrum is known. In particular, the eigenvalues closest to zero are the two double eigenvalues +3/2 and -3/2. Our aim is to analyse the behaviour of eigenvalues when the metric is perturbed in an arbitrar…
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states befor…
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inference time by applying elaborately designed perturbation to input data. Although several defense methods have been proposed to address specific…
The study bounds the stability of Gaussian mixtures under small perturbations.
SIFG uses noisy particles to efficiently sample from complex distributions.
We prove some existence results for the Webster scalar curvature problem on the Heisenberg group and on the unit sphere of , under the assumption of some natural symmetries of the prescribed curvatures. We use variational and perturbation techniques.