Proves uniqueness theorem for mean value sets of elliptic operators.
problem Understanding mean value sets for elliptic operators.
method Proves equivalence between mean value sets and noncontact sets of obstacle problems involving Green's functions.
result Establishes a uniqueness theorem for mean value sets of elliptic divergence form operators.
Generalizes mean-value inequality to orbifold setting.
problem Mean-value inequality for orbifold setting.
method Generalizes fundamental results in Kähler geometry to orbifolds.
result Shows mean-value inequality is insensitive to quotient singularities.
The logcosh loss function helps neural networks learn set-valued functions better.
problem Learning set-valued functions with neural networks.
method Using artificial neural networks with logcosh loss.
result Neural networks with logcosh loss can classify samples based on set-valued functions.
Develops a dynamic mean field theory for reinforcement learning.
problem Finite state and action Bayesian reinforcement learning in large state spaces.
method Analogies with statistical physics, interpreting probabilities as couplings and values as spins, solving mean field equations.
result State-action values are statistically independent in the asymptotic state space limit, with exact or approximate equations for computation.
New method uses CNNs to estimate graph means.
problem Estimating the mean of graph-valued data.
method Convolutional Neural Networks (CNNs) for graph morphology learning.
result CNNs reliably recover the sample Frechet mean.
Impact of chosen behavioural factors on imprecision of present value is discussed here. The formal model of behavioural present value is offered as a result of this discussion. Behavioural present value is described here by fuzzy set. These considerations were illustrated by means of extensive numerical case study. Fin…
New algorithm solves mean-field control problems using actor-critic learning with moment neural networks.
problem Solving mean-field control problems in continuous time reinforcement learning.
method Gradient-based policy and value function learning with moment neural networks on the Wasserstein space.
result Effective solution for diverse mean-field control problems, including multi-dimensional and nonlinear settings.
Study expands classical harmonic function results to Riemannian manifolds.
problem Classical harmonic function properties in domains of Riemannian manifolds.
method Generalized classical results to Riemannian manifolds, including pinched negative curvature.
result Generalized results for Riemannian manifolds, including pinched negative curvature.
Sharp bounds for distortion risk metrics under uncertain distributions.
problem Modeling risk metrics under distributional uncertainty.
method Established bounds for distortion risk metrics using specific features of underlying distributions.
result Identified worst- and best-case values of distortion risk metrics.
Study uses actor-critic method for continuous-time mean-field control with entropy regularisation.
problem Continuous-time mean-field control in reinforcement learning.
method Actor-critic approach with entropy regularisation, value function alternation, and Wasserstein space parametrisation.
result Derives exact parametrisation of actor and critic functions in linear-quadratic mean-field framework.
Study examines harmonic functions in sub-Riemannian and RCD settings.
problem Characterizing harmonic functions in sub-Riemannian and RCD settings.
method Analyzes weak and strong asymptotically mean value harmonic functions.
result Weakly amv-harmonic functions are equivalent to harmonicity in Carnot groups.
The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field. The asynchronicity of the process is incorporated into the mean field to produce convergence results which remain similar to those of a…
One of the goals of this article is to define a an unified setting adapted to the description of means (normalized integrals or invariant means) on an infinite product of measured spaces with infinite measure. We first remark that some known examples coming from the theory of metric measured spaces and also from oscill…
The paper computes an approximation to the sample Frechet mean of graph sets using spectral information.
problem Characterizing the location of a set of graphs in a metric space.
method The Frechet mean is computed for sets of large graphs using the pseudometric defined by the norm between eigenvalues of adjacency matrices.
result An algorithm to approximate the sample Frechet mean of undirected unweighted graphs is described.
We apply the theory of continuous time random walks to study some aspects of the extreme value problem applied to financial time series. We focus our attention on extreme times, specifically the mean exit time and the mean first-passage time. We set the general equations for these extremes and evaluate the mean exit ti…
In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing valu…
Paper accelerates K-means clustering for large sparse document data.
problem Efficiently clustering large-scale sparse document data.
method Designs an AFM algorithm leveraging UCs and inverted-index structure.
result Significantly improves clustering speed for large-scale documents.
Quantum algorithm estimates multivariate mean with near-optimal efficiency.
problem Estimating the mean of multivariate random variables efficiently in quantum computing.
method Combines amplitude amplification, quantum singular value transformation, and Bernstein-Vazirani algorithm.
result Quantum estimator outperforms classical estimators outside low-precision regime.
The paper assesses conditions for the uniqueness of k-means clustering.
problem Conditions for the uniqueness of k-means clustering.
method Analyzes the choice of k and provides necessary and sufficient conditions for uniqueness.
result Determines the asymptotic distribution of the within cluster sum of squares (WCSS) and provides a bootstrap test for uniqueness.
A new method estimates multi-dimensional value distributions using Hilbert space embeddings.
problem Estimating value distributions in complex, multi-dimensional reinforcement learning settings.
method Hilbert space mappings and kernel mean embeddings to estimate the kernel mean embedding of multi-dimensional value distributions.
result Uniform convergence guarantees and robust off-policy evaluation demonstrated in simulations.
New game approximates mean curvature flow evolution.
problem Approximating geometric mean curvature flow evolution.
method Two-player zero-sum game with probabilistic elements.
result Value function approximates mean curvature flow.
New collaborative algorithm improves personalized mean estimation in online settings.
problem Online estimation of means from multiple, possibly overlapping distributions.
method Novel collaborative strategy for active querying and mean estimation.
result Algorithm improves mean estimates through communication among agents.
Quantile TD learning outperforms classical TD learning for value estimation.
problem Temporal-difference learning in reinforcement learning.
method Quantile Temporal-Difference Learning (QTD) for policy evaluation.
result QTD offers superior performance to classical TD learning, even in tabular settings.
Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.
problem Optimizing long-term Defined Contribution plans with realistic constraints and dynamic dynamics.
method Formulates and solves bilevel optimization problems for pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization.
result Time-consistent Mean-bPoE strategies maintain investor preferences for minimum terminal wealth, unlike Mean-CVaR.
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
problem Optimizing portfolios with a new risk measure under known or uncertain distributions.
method Existence results for mean-risk optimal portfolios under different distributional assumptions.
result Portfolio selection under Recovery Average Value at Risk provides better control over liabilities.
Revisits superhedging under proportional costs in continuous time markets.
problem Superhedging in markets with proportional transaction costs.
method Set-valued stochastic analysis, continuous trading schemes, dynamic risk measure.
result Dynamic set-valued risk measure with multi-portfolio time-consistency.
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
The paper solves a specific Dirichlet problem for constant mean curvature surfaces in a particular manifold.
problem Existence and uniqueness of constant mean curvature graphs with prescribed asymptotic values.
method Defined a new product compactification for the homogeneous manifold and proved the existence of entire H-graphs.
result Existence and uniqueness of entire H-graphs with prescribed asymptotic values.
We develop some of the basic theory for the obstacle problem on Riemannian Manifolds, and we use it to establish a mean value theorem. Our mean value theorem works for a very wide class of Riemannian manifolds and has no weights at all within the integral.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
problem Estimating excursion sets of vector-valued Gaussian processes.
method Clarifying the connection between continuous Gaussian processes and Gaussian measures in Banach spaces, extending concepts and properties from scalar-valued settings to vector-valued settings.
result Consistency results for sequential design strategies can be applied to vector-valued Gaussian processes.
Unified q-learning for mean-field jump-diffusion models with unobservable population distribution.
problem Continuous-time q-learning in mean-field jump-diffusion models with unobservable population distribution.
method Proposed decoupled Iq-function for unified policy evaluation in MFG and MFC problems; unified q-learning algorithm based on test policies and averaged martingale orthogonality condition.
result Unified policy evaluation rule for MFG and MFC problems based on decoupled Iq-function.
Efficient algorithm for estimating target mean under known sampling distribution.
problem Statistical estimation under known sampling distribution without distributional assumptions.
method Worst-case analysis of weighted combination of sample values.
result Worst-case expected error is at most a π/2 factor worse than optimal.
Two-dimensional Lagrangian mean curvature equation solved with new inequality.
problem Solving the two-dimensional Lagrangian mean curvature equation.
method Using Warren-Yuan's super isoperimetric inequality and simplified approach.
result Derivation of a modified Hessian bound for solutions.
We present evidence, that if a large enough set of high resolution stock market data is analyzed, certain analogies with physics -- such as scaling and universality -- fail to capture the full complexity of such data. Despite earlier expectations, the mean value per trade, the mean number of trades per minute and the m…
New method speeds up k-means clustering using sketch-and-solve.
problem Efficiently solving k-means clustering for large datasets.
method Sketch-and-solve approach with Peng-Wei semidefinite relaxation.
result Provides high-confidence lower bounds on k-means optimal value.
Study on estimating Gaussian mean from coarse data, resolving identifiability and computational efficiency questions.
problem Estimating the mean of a Gaussian distribution from coarse data (sets containing true samples rather than exact values).
method Analyzes the conditions for mean identifiability and computable estimation under convex partitions.
result Resolves the identifiability and computational efficiency questions for Gaussian mean estimation from coarse data.
Selective inference controls Type I error in k-means clustering tests.
problem Inflated Type I error in classical hypothesis tests for k-means clusters.
method Selective inference approach to control Type I error.
result Proposes a computable finite-sample p-value for selective inference.
Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are popular risk measures from academic, industrial and regulatory perspectives. The problem of minimizing CVaR is theoretically known to be of Neyman-Pearson type binary solution. We add a constraint on expected return to investigate the Mean-CVaR portfolio sele…
The paper generalizes a mean value theorem for solutions of the ultrahyperbolic equation.
problem Establishing a mean value property for solutions of the ultrahyperbolic equation.
method Using conformal maps of the pseudo-Euclidean space of signature 2+2, the paper extends Asgeirsson's theorem to a more general class of pairs of curves.
result The mean value property is proven for non-degenerate conjugate conics, including conjugate circles, hyperbolae, parabolae, and line-empty pairs.
Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.
problem Estimating smooth Hilbert-valued parameters with theoretical guarantees.
method Pathwise differentiable Hilbert-valued parameters, efficient influence functions, regularized one-step estimators.
result Theoretical guarantees for efficient estimators even when nuisance functions are arbitrary.
A new framework for robust risk measurement and portfolio optimization.
problem Uncertainty in mean-covariance space and portfolio optimization challenges.
method Modeling uncertainty with Gelbrich distance and prior structural information, related to optimal transport theory.
result Mean-covariance robust portfolio optimization simplifies to Markowitz model with a regularization term.
The average and median income dependence on work experience and time is analyzed and modeled for the USA. The original data set providing the mean and median income estimates in 10 year long intervals spans a long time period of almost 35 years ? from 1967 to 2003. A microeconomic model linking personal income, populat…
This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…
The regulator is interested in proposing a capital adequacy test by specifying an acceptance set for firms' capital positions at the end of a given period. This set needs to be surplus-invariant, i.e., not to depend on the surplus of firms' shareholders, because the test means to protect firms' liability holders. We pr…
We solve a portfolio selection problem with four objectives, finding convex scalarizations for part of the Pareto front.
problem Portfolio selection with four objectives: mean, variance, skewness, and kurtosis.
method Linearly scalarize MVSK objectives into a convex polynomial Fλ over the probability simplex, compute optimizers for each λ. result Identify a set of hyper-parameters for which the scalarization is convex, allowing computation of part of the Pareto front.
Model selection for time series forecasting can be biased by the distribution of scores.
problem Model selection for probabilistic forecasting on time series data.
method Using proper scoring rules to aggregate scores across multiple time series.
result The mean score is immune to the skewness of the score distribution.
In this paper, we prove a mean value formula for bounded subharmonic Hermitian matrix valued function on a complete Riemannian manifold with nonnegative Ricci curvature. As its application, we obtain a Liouville type theorem for the complex Monge-Ampère equation on product manifolds.
Instead of controlling "symmetric" risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial …