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.
SGLD proves geometric ergodicity via reflection coupling for nonconvex log-concave distributions.
We study gradient bounds and other functional inequalities for the diffusion semigroup generated by Kolmogorov type operators. The focus is on two different methods: coupling techniques and generalized -calculus techniques. The advantages and drawbacks of each of these methods are discussed.
Random features are improved by variance-reducing couplings, enhancing machine learning models.
ARK improves knockoffs robustness to feature distribution misspecification.
A new watermarking method corrects bias in language models using maximal coupling.
The paper extends statistical estimation techniques under differential privacy.
This paper shows how to approximate any log-concave distribution using well-conditioned affine coupling flows.
NSBI approach detects Higgs trilinear coupling with high luminosity upgrade constraints.
Improved neural network training by coupled initialization reduces neuron count.
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literature impose regularization, DropOut or DropConnect techniques to induce sparsity, our scheme considers feature importance as a criter…
We develop a stochastic target representation for Ricci flow and normalized Ricci flow on smooth, compact surfaces, analogous to Soner and Touzi's representation of mean curvature flow. We prove a verification/uniqueness theorem, and then consider geometric consequences of this stochastic representation. Based on this …
Study null hypersurfaces in Lorentzian manifolds, proving Riemannian flow structure.
We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficien…
Enhances deep networks robustness with data mollification and label smoothing.
New equations reveal moduli space rigidity in geometric deformations.
In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do …
For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It le…
Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formu…
Lyapunov-based analysis shows polynomial sample complexity for WCMDPs and RBs.
Motivated by the study of coupled Kähler-Einstein metrics by Hultgren and Witt Nyström and coupled Kähler-Ricci solitons by Hultgren, we study in this paper coupled Sasaki-Einstein metrics and coupled Sasaki-Ricci solitons. We first show an isomorphism between the Lie algebra of all transverse holomorphic vector fields…
New bounds show linear predictors rarely overfit with certain optimization methods.
End-to-end training of DBMs with improved gradient estimation.
Defines coupled embeddability for maps on products of spaces, generating examples and nonexamples.
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from proba…
Solves modified conjecture for Fano manifolds using Ding stability.
UNTIE learns representations of coupled categorical data.
Single sample estimation for hard-constrained models like SAT and coloring problems.
Paper discusses conditions for deforming coupled Kähler-Einstein metrics.
A new framework optimizes fMRI and behavioral data for better understanding of Autism.
Develops non-Markovian couplings for sub-Riemannian Brownian motions.
We identify higher-charge configurations that satisfy Euler-Lagrange equations for the (strong coupling limit of) Faddeev-Hopf model, by means of adequate changes of the domain metric and a reduction technique based on -Hopf construction. In the last case it is proved that the solutions are local minima for the redu…
Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this r…
Numerical observations on martingale couplings are confirmed under certain conditions.
Compositional diffusion models simulate coupled PDEs efficiently.
Study optimal consumption and investment strategies with constraints in a market with random coefficients.
Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving the hash functions or binary codes. These objective functions encode neighborhood information between data points and are often inspired by…
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…
The paper studies the question of whether the classical mirror and synchronous couplings of two Brownian motions minimise and maximise, respectively, the coupling time of the corresponding geometric Brownian motions. We establish a characterisation of the optimality of the two couplings over any finite time horizon and…
The paper provides statistical guarantees for SGD and ASGD in high-dimensional settings.
Joint analysis of data from multiple information repositories facilitates uncovering the underlying structure in heterogeneous datasets. Single and coupled matrix-tensor factorization (CMTF) has been widely used in this context for imputation-based recommendation from ratings, social network, and other user-item data. …
We consider the optimal control problem for null curves in de Sitter 3-space defined by a functional which is linear in the curvature of the trajectory. We show how techniques based on the method of moving frames and exterior differential systems, coupled with the reduction procedure for systems with a Lie group of sym…
This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed,…
Unified analytic account of correlation emergence and Epps effect in coupled limit order books
In this paper we derive the optimal execution trajectory for a trader who wishes to buy or sell a large position of shares which evolve as a geometric Brownian process in contrast to the arithmetic model which prevails in the existing literature, and with a general temporary impact . We provide a couple of examples …
We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial couplin…
Study on kinetic Langevin diffusions and their couplings, showing subtle TV bounds and new non-Markovian couplings.
C-DPS improves diffusion posterior sampling for inverse problems without projection or likelihood approximation.
We introduce the coupled Ricci-Calabi functional and the coupled H-functional which measure how far from a coupled Kähler-Einstein metric in the sense of Hultgren-Witt Nyström. We first give corresponding moment weight type inequalities which estimate each functional in terms of algebraic invariants. Secondly, we give …