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.
Doubly-stochastic normalization improves robustness to heteroskedastic noise.
We propose an efficient method for estimating covariate effects in doubly-stochastic spatial models.
This technical report proves components consistency for the Doubly Stochastic Dirichlet Process with exponential convergence of posterior probability. We also present the fundamental properties for DSDP as well as inference algorithms. Simulation toy experiment and real-world experiment results for single and multi-clu…
Paper proposes Sinkformers for Transformers with doubly stochastic attention.
Doubly SGD improves convergence for intractable objective optimization.
New method reduces variance in complex probabilistic model optimization.
FDSKL algorithm trains vertically partitioned data with kernels securely and efficiently.
Geometric approach for unsupervised word embedding alignment.
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy …
This paper discusses properties of a Doubly Stochastic Poisson Process (DSPP) where the intensity process belongs to a class of affine diffusions. For any intensity process from this class we derive an analytical expression for probability distribution functions of the corresponding DSPP. A specification of our results…
The stochastic gradient descent has been widely used for solving composite optimization problems in big data analyses. Many algorithms and convergence properties have been developed. The composite functions were convex primarily and gradually nonconvex composite functions have been adopted to obtain more desirable prop…
The paper improves boundary detection and density estimation on noisy data.
When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the missingness depends on some covariates may lead to biased estimation of parameters and even harm the fairness of decision outcome. This paper …
Doubly stochastic learning algorithms are scalable kernel methods that perform very well in practice. However, their generalization properties are not well understood and their analysis is challenging since the corresponding learning sequence may not be in the hypothesis space induced by the kernel. In this paper, we p…
We study a doubly reflected backward stochastic differential equation (BSDE) with integrable parameters and the related Dynkin game. When the lower obstacle and the upper obstacle of the equation are completely separated, we construct a unique solution of the doubly reflected BSDE by pasting local solutions and…
Robustly infers manifold density and geometry under high-dimensional noise.
DSVNP uses global and local latent variables for improved neural process predictions.
The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional grad…
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations. Our main motivation is the acceleration of the optimization of the regularized Cox partial-likelihood (the core model used in survival an…
Graph alignment problem solved with convex relaxations for correlated matrices.
Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to infe…
ADSGD method speeds up model identification in sparse optimization.
π-GNN learns soft permutations for graph representations, improving graph classification and regression.
New algorithms learn graph structures privately, matching best results.
We propose a doubly stochastic primal-dual coordinate optimization algorithm for empirical risk minimization, which can be formulated as a bilinear saddle-point problem. In each iteration, our method randomly samples a block of coordinates of the primal and dual solutions to update. The linear convergence of our method…
S2M optimizes mining for diverse data subpopulations.
Estimates outcomes under hypothetical scenarios using a flexible framework.
Accelerates Birkhoff projection for manifold-constrained hyper-connections with high accuracy and speed.
Proposes a new simulator for complex arrival processes.
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. Most of the existing algorithms have regret proportional to a polynomial function of the context dimension, . In many applications ho…
The paper examines Einstein doubly warped product manifolds with a semi-symmetric metric connection.
Characterizes spacetimes using doubly torqued vectors.
New invariant measures doubly slice links, disproving previous bounds.
Enhances DGPs with adaptive RKHS Fourier features for better non-stationary pattern modeling.
Characterizes a specific type of spacetime using vector fields.
Characterizes and examines gradient solitons on doubly warped product manifolds.
The article enumerates doubly symmetric diagrams for knots up to 18 crossings.
Proposes DR-ACI for causal effect intervals with temporal dependence.
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garne…
Simplified tutorial on doubly robust learning for causal inference.
We show that if the connected sum of two knots with coprime Alexander polynomials is doubly slice, then the Ozsváth-Szabó correction terms as smooth double sliceness obstructions vanish for both knots. Recently, Jeffrey Meier gave smoothly slice knots that are topologically doubly slice, but not smoothly doubly slice. …
Method uses deep learning to estimate traffic intensity.
In this article, we present a complete study of two disjoint classes of conformal vector fields on doubly warped product manifolds as well as on doubly warped space-times. Then we study Ricci solitons on doubly warped product manifollds admitting these types of conformal vector fields.
We model messaging activities as a hierarchical doubly stochastic point process with three main levels, and develop an iterative algorithm for inferring actors' relative latent positions from a stream of messaging activity data. Each of the message-exchanging actors is modeled as a process in a latent space. The actors…
We propose a unified framework for equity and credit risk modeling, where the default time is a doubly stochastic random time with intensity driven by an underlying affine factor process. This approach allows for flexible interactions between the defaultable stock price, its stochastic volatility and the default intens…
A new method for sparse linear bandits reduces exploration-exploitation tradeoff.
Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regularization is added to the problem, the transportation plan can be…
Identifies doubly slice genera for 2909 prime knots with up to 12 crossings.