arXiv research

The paper explores tail diversification in financial markets using entropy and mutual information.

Polynomial-time algorithm learns high-dimensional halfspaces without labels.

Unified framework models graph data as a mixture of graphons using graph moments.

We construct open book structures on all moment-angle manifolds and describe the topology of their leaves and bindings under certain restrictions. II. We also show, using a recent deep result about contact forms due to Borman, Eliashberg and Murphy [6], that every odd-dimensional moment-angle manifold admits a contact …

New method improves estimation of complex models from conditional moment restrictions.

This thesis studies domain adaptation under minimal distribution similarity assumptions using moments.

Letter analyzes training dynamics of a nonlinear contrastive learning model in high dimensions.

MGD combines maximum entropy and diffusion methods for efficient sampling.

A new law limits kurtosis contrast in balanced mixtures.

New method distills discrete diffusion models, maintaining quality and diversity.

Paper identifies tensor ranks via prior predictive matching, solving system of equations.

Corrected moment-based methods improve inference in topic model regression.

Paper provides Edgeworth expansions for network moments, improving accuracy of sampling distributions.

Contrastive divergence (CD) is a promising method of inference in high dimensional distributions with intractable normalizing constants, however, the theoretical foundations justifying its use are somewhat shaky. This document proposes a framework for understanding CD inference, how/when it works, and provides multiple…

Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we cond…

This paper compares VaR estimation methods under tail misspecification, finding importance sampling underestimates VaR.

Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint only informs us how well the solution is approximated numerically but overlooks the sampling nature of the data. In contrast, recognizing the randomness in the dat…

We use the P&L on a particular class of swaps, representing variance and higher moments for log returns, as estimators in our empirical study on the S&P500 that investigates the factors determining variance and higher-moment risk premia. This class is the discretisation invariant sub-class of swaps with Neuberger's agg…

Continuous-time PCD for MLE with explicit error bounds.

Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classification. But, efficient policy evaluation need not yield efficient estimation of policy parameters. We consider the estimation problem give…

A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it…

Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations of their nonparametric counterparts, such as the Jensen-Shannon divergence, which can be derived under the so-called optimal discriminator …

We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least squares optimization procedure. With several numerical examples, we show that such Least Squares Importance Sampling (LSIS) provides efficiency gains comparable to the state of the art techniques, when the latter are known to…

We introduce the problem of learning mixtures of $k$ subcubes over $\{0,1\}^n$, which contains many classic learning theory problems as a special case (and is itself a special case of others). We give a surprising $n^{O(\log k)}$-time learning algorithm based on higher-order multilinear moments. It is not possible to l…

Several new estimation methods have been recently proposed for the linear regression model with observation error in the design. Different assumptions on the data generating process have motivated different estimators and analysis. In particular, the literature considered (1) observation errors in the design uniformly …

A new game-theoretic approach balances downside risk with expected reward.

Paper tackles online control of linear systems with unbounded noise.

A new method calculates fractional moments using the moment-generating function.

For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…

This paper identifies and bounds ICE central moments using PO marginal central moments.

We tackle causal inference under conditional moment restrictions using importance weighting.

Revisits Lee's Moment Formula, relaxing moment assumptions for implied volatility.

Developed moment estimators for affine stochastic volatility models.

Stiefel-Whitney classes of moment-angle manifolds are trivial.

A new method for estimating causal parameters from observables reduces the need for finite moment conditions.

Introduces generalized moment maps for almost Hermitian settings.

Constructs a moment map flow for isotropic maps on surfaces.

We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) called conditional moment embedding (CMME). After transforming the conditional moment restrictions in…

A new method of moments estimator goes beyond data reweighting.

Moment Pooling reduces latent space dimensions in machine learning models.

Deformation quantization yields a new moment map on symplectic diffeomorphisms.

In this paper we show that, for a sub-Laplacian $Δ$ on a $3$-dimensional manifold $M$, no point interaction centered at a point $q_0\in M$ exists. When $M$ is complete w.r.t. the associated sub-Riemannian structure, this means that $Δ$ acting on $C^\infty_0(M\setminus\{q_0\})$ is essentially self-adjoint. A particular …

RankSEG-RMA improves semantic segmentation efficiency and applicability.

The paper derives formulas for moments of a Student t distribution and applies them to quantify $L_p$-quantiles.

We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specif…

Low-degree method fails to predict robust subspace recovery problem.

We discuss the probabilistic properties of the variation based third and fourth moments of financial returns as estimators of the actual moments of the return distributions. The moment variations are defined under non-parametric assumptions with quadratic variation method but for the computational tractability, we use …

Real moment-angle manifolds of combinatorially equivalent simple polytopes are equivariantly diffeomorphic.