arXiv research

Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.

AdamNX improves Adam's stability by adjusting its learning rate.

Second-order optimization speeds up deep hedging for complex options.

New proof of Sobolev inequality with constraints on sphere.

New inequality criterion for a mean field equation on spheres.

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many fi…

Detects adversaries in crowdsourcing to improve accuracy.

Derives derivatives of risk measures for various types of portfolio losses.

MOMENT selects and estimates mixed-effects models using moment identities.

Improved prediction algorithm for 'easy' sequences with reduced regret.

Introduces a new price measure and a second-order economic theory for volatility forecasting.

Nonnegative matrix factorization (NMF) has been widely used in machine learning and signal processing because of its non-subtractive, part-based property which enhances interpretability. It is often assumed that the latent dimensionality (or the number of components) is given. Despite the large amount of algorithms des…

Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.

Sharp inequalities in unit ball with constraints on moments.

DualAdam improves generalization of Adam by integrating its update mechanisms.

TSCD is an algorithm for causal discovery using second-order statistics.

Data whitening and second order optimization harm generalization by reducing access to dataset information.

New algorithm learns HMM parameters on Riemannian manifolds.

Study of symplectic Monge-Ampère equations using moment maps and contact structures.

Paper introduces STSL, a second-order Tweedie sampler for efficient posterior sampling in inverse problems.

Second-order estimator improves continuous-time policy evaluation.

In this paper we propose a closed-form approximation for the price of basket options under a multivariate Black-Scholes model, based on Taylor expansions and the calculation of mixed exponential-power moments of a Gaussian distribution. Our numerical results show that a second order expansion provides accurate prices o…

Study differentially private linear regression with heavy-tailed data.

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and t…

The paper examines Nash equilibrium in GANs for stationary Gaussian processes.

We associate certain probability measures on $\R$ to geodesics in the space $\H_L$ of positively curved metrics on a line bundle $L$, and to geodesics in the finite dimensional symmetric space of hermitian norms on $H^0(X, kL)$. We prove that the measures associated to the finite dimensional spaces converge weakly to t…

Double machine learning provides $\sqrt{n}$-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-1/4}$ rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance param…

Method estimates posterior model for boundary value problems with uncertain constraints.

Two-dimensional transition rates improve life insurance reserve calculations.

Unified framework for mean testing under truncation bias.

Realised pay-offs for discretisation-invariant swaps are those which satisfy a restricted `aggregation property' of Neuberger [2012] for twice continuously differentiable deterministic functions of a multivariate martingale. They are initially characterised as solutions to a second-order system of PDEs, then those pay-…

We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order exponential mo…

A new neural network initialization method is proposed for faster and more accurate training.

Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classi…

Expectation Propagation (EP) provides a framework for approximate inference. When the model under consideration is over a latent Gaussian field, with the approximation being Gaussian, we show how these approximations can systematically be corrected. A perturbative expansion is made of the exact but intractable correcti…

SLIM efficiently solves overidentified models in a scalable manner.

This paper presents the nonparametric inference for nonlinear volatility functionals of general multivariate Itô semimartingales, in high-frequency and noisy setting. Pre-averaging and truncation enable simultaneous handling of noise and jumps. Second-order expansion reveals explicit biases and a pathway to bias correc…

Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilistic topic models and latent linear Bayesian networks, using only secon…

Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework f…

Adaptive algorithm AMSGrad converges for weakly convex constrained optimization problems.

Sketchy reduces memory and compute requirements for adaptive regularization in deep learning.

Improved regret bounds for adversarial linear contextual bandits.

We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random retur…

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank $k$ generalized FM of $d$ dimensional input, the previous best known sampling complexity is $\mathcal{O}[k^{3…

Breiman (2001) proposed to statisticians awareness of two cultures: 1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman; 2. Algorithmic predictive culture, pioneered by machine learning research. Parzen (2001), as a part of discussing Breiman (2001), proposed that researchers be aware of many cultu…

Many pattern recognition methods rely on statistical information from centered data, with the eigenanalysis of an empirical central moment, such as the covariance matrix in principal component analysis (PCA), as well as partial least squares regression, canonical-correlation analysis and Fisher discriminant analysis. R…

Proposes debiasing strategy for ill-posed regression problems.

The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models. Of these algorithms, those that rely solely on the simpl…