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.
MoMA improves model-based RL by using unrestricted policy classes.
New method optimizes policies without assuming known link functions between preferences and rewards.
This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study in this general framework of both restricted and unrestricted payment schemes, wh…
Proposes a new method to generate unrestricted adversarial examples.
Neural networks are vulnerable to adversarially-constructed perturbations of their inputs. Most research so far has considered perturbations of a fixed magnitude under some norm. Although studying these attacks is valuable, there has been increasing interest in the construction of (and robustness to) unrestricted…
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
We study learning problems involving arbitrary classes of functions , distributions and targets . Because proper learning procedures, i.e., procedures that are only allowed to select functions in , tend to perform poorly unless the problem satisfies some additional structural property (e.g., that is co…
Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose unrestricted adversarial examples, a new threat model where the attackers are not restricted to small…
Study of unrestricted virtual braid groups and their properties.
We consider the group of unrestricted virtual braids, describe its structure and explore its relations with fused links. Also, we define the groups of flat virtual braids and virtual Gauss braids and study some of their properties, in particular their linearity.
Generative models create indistinguishable adversarial objects for object detection.
New methods reduce extrapolation errors in feature importance.
Recently, it is proven that generalized Robertson-Walker space-times in all orthogonal subspaces of Gray's decomposition but one(unrestricted) are perfect fluid space-times. GRW space-times in the unrestricted subspace are identified by having constant scalar curvature. Generalized quasi-Einstein GRW space-times have a…
Study compares adaptive vs fixed query learning methods.
R. Kashaev and N. Reshetikhin introduced the notion of holonomy braiding extending V. Turaev's homotopy braiding to describe the behavior of cyclic representations of the unrestricted quantum group at root of unity. In this paper, using quandles and biquandles we develop a general theory for Reshetikhin-Turae…
Paper proposes OPF policy for fair resource allocation with sublinear regret.
We consider an insurance company whose surplus is represented by the classical Cramer-Lundberg process. The company can invest its surplus in a risk free asset and in a risky asset, governed by the Black-Scholes equation. There is a constraint that the insurance company can only invest in the risky asset at a limited l…
The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a kn…
A stochastic model helps maintain insufficiently funded pension funds.
Thanks to recent advances in deep neural networks (DNNs), face recognition systems have become highly accurate in classifying a large number of face images. However, recent studies have found that DNNs could be vulnerable to adversarial examples, raising concerns about the robustness of such systems. Adversarial exampl…
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
ScoreAG generates unrestricted adversarial images maintaining semantic integrity.
The study extends classical results on harmonic functions to Riemannian manifolds with non-tangential boundary limits.
The article has been withdrawn by the author. Wolfgang Lueck and Peter Linnell pointed out that the proof of Lemma 3.8 does not apply to the unrestricted case of wreath product. It is not clear at this stage how to complete the proof of Theorem 3.1 using the present version of Lemma 3.8. The valid results originating f…
New method estimates sparse covariance matrices in logit mixtures.
Defines a new 2+1-G-HQFT using graded skein modules.
Polynomial-time DP algorithm for learning Gaussians with matching sample complexity.
We consider an insurance entity endowed with an initial capital and a surplus process modelled as a Brownian motion with drift. It is assumed that the company seeks to maximise the cumulated value of expected discounted dividends, which are declared or paid in a foreign currency. The currency fluctuation is modelled as…
This paper proposes BAT to balance accuracy and robustness in adversarial training.
Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.
A new way to describe correlation matrices makes modeling easier.
Motivated by the resurgence of neural networks in being able to solve complex learning tasks we undertake a study of high depth networks using ReLU gates which implement the function . We try to understand the role of depth in such neural networks by showing size lowerbounds against such network …
Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Us…
Study on framed surfaces with bounds on Morse index.
We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries. Defenders submit machine learning models, and try to achi…
This paper tackles the computational complexity of finding approximate stationary points in non-convex optimization.
A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these models requires restricting their architectures to allow cheap computation of Jacobian determinants. Alternatively, the Jacobian trace can be u…
We study a dynamic portfolio optimization problem related to convergence trading, which is an investment strategy that exploits temporary mispricing by simultaneously buying relatively underpriced assets and selling short relatively overpriced ones with the expectation that their prices converge in the future. We build…
New method for private density estimation of high-dimensional Gaussian mixtures.
The paper studies properties of stated SL(n)-skein algebras and their centers.
Estimates growth loss in fund models and proposes a shrinkage method.
We propose a restricted class of tensor network state, built from number-state preserving tensors, for supervised learning tasks. This class of tensor network is argued to be a natural choice for classifiers as (i) they map classical data to classical data, and thus preserve the interpretability of data under tensor tr…
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an …
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style regularization where the regularization penalty articulates the way in which the marginal density should constrain otherwise unrestricted co…
The paper characterizes crystallographic groups derived from virtual braid and twin groups.
Study on virtual singular braid groups with algebraic properties and homomorphisms.
Study on the Euler-Plateau energy with elastic modulus, focusing on minimizers and critical surfaces.
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables. LDAGs generalize earlier proposals for allowing local structures in the conditional probability distribution of a node, such that unrestricted label sets determine which edges can be deleted from the underl…