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.
Method controls extrapolation in prediction profiles for statistical and machine learning models.
Profile entropy measures learnability and compressibility of discrete distributions.
Model trains agents to optimize saving and investment strategies for diverse retirement needs.
We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The …
Study lampshuffler groups' isoperimetric profiles, refining previous estimates.
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
Unified plug-in approach for estimating symmetric properties of distributions efficiently.
Study of fastest paths in anisotropic media via Finsler geometry.
New algorithm radVI improves variational inference by optimizing radial profiles.
Dimensionality-reduction methods are a fundamental tool in the analysis of large data sets. These algorithms work on the assumption that the "intrinsic dimension" of the data is generally much smaller than the ambient dimension in which it is collected. Alongside their usual purpose of mapping data into a smaller dimen…
We study the local Szegö-Weinberger profile in a geodesic ball centered at a point in a Riemannian manifold $(\M,g)$. This profile is obtained by maximizing the first nontrivial Neumann eigenvalue of the Laplace-Beltrami Operator on $\M$ among subdomains of with fixed vol…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
Daydream predicts DNN optimization efficacy efficiently.
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptiv…
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider …
Modeling correlated mutations in cancer for personalized treatment.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
A novel criterion selects optimal distance metrics for cell profile analysis.
Estimates population profile from small random samples.
The paper improves stability estimates for soap bubble theorem in curved domains.
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and prop…
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
A new Bayesian framework simplifies stochastic optimization by focusing on key parameters.
A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings fr…
A method to describe Riemann surfaces using graph profiles is proposed.
Study compares MoE and RNN models for stock price prediction across volatility profiles.
In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirica…
Background: While machine learning (ML) models are rapidly emerging as promising screening tools in critical care medicine, the identification of homogeneous subphenotypes within populations with heterogeneous conditions such as pediatric sepsis may facilitate attainment of high-predictive performance of these prognost…
We equip many non compact non simply connected surfaces with smooth Riemannian metrics whose isoperimetric profile is smooth, a highly non generic property. The computation of the profile is based on a calibration argument, a rearrangement argument, the Bol-Fiala curvature dependent inequality, together with new result…
We introduce a spectrum of monotone coarse invariants for metric measure spaces called Poincaré profiles. The two extremes of this spectrum determine the growth of the space, and the separation profile as defined by Benjamini--Schramm--Timár. In this paper we focus on properties of the Poincaré profiles of groups with …
Logarithmic separation profile in hyperbolic groups shows hierarchical structure.
We study risk-sensitive imitation learning where the agent's goal is to perform at least as well as the expert in terms of a risk profile. We first formulate our risk-sensitive imitation learning setting. We consider the generative adversarial approach to imitation learning (GAIL) and derive an optimization problem for…
Framework detects shape shifts in functional profiles using Fréchet mean and shape invariant model.
The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurat…
Study compares isoperimetric profiles on manifolds with integral Ricci curvature bounds.
There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application domains. This has made profiling and characterization of ML model performance an increasingly pressing task for both hardware designers and system providers, as they would like to offer the b…
Study optimizes perimeter in convex domains with anisotropic constraints.
Estimates lower bounds for isoperimetric profiles and improves on previous estimates for specific manifolds.
In the context of sub-Riemannian Heisenberg groups Hn, n \geq 1, we shall study Isoperimetric Profiles, which are closed compact hypersurfaces having constant horizontal mean curvature, very similar to ellipsoids. Our main goal is to study the stability of Isoperimetric Profiles.
Study optimal algorithms for recovering signals through inhomogeneous low-rank channels.
Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.
Non-Negative Matrix Factorization, NMF, attempts to find a number of archetypal response profiles, or parts, such that any sample profile in the dataset can be approximated by a close profile among these archetypes or a linear combination of these profiles. The non-negativity constraint is imposed while estimating arch…
Random layer-wise pruning profiles are as effective as metric-based ones for various datasets.
Novel NAS method balances performance and hardware metrics efficiently.
The study shows portfolios based on core-periphery stock structure outperform traditional strategies.
The paper classifies Cartan-Hadamard manifolds supporting optimal Sobolev inequalities.
CR-UOT improves matching of heterogeneous single-cell omics profiles.