arXiv research

Study compares adaptive vs fixed query learning methods.

Method compares driver performance using behavioral advantage, removing environmental factors.

Hydrodynamic model explains trade between two countries.

Paper proposes FedQ-Advantage for federated Q-learning with near-optimal regret and low communication cost.

New method learns time-varying home field advantage in football.

This study improves quantum classifiers by optimizing data preprocessing.

We compare and contrast two approaches to the structure theory for Lie pseudo-groups, the first due to Cartan, and the second due to the first two authors. We argue that the latter approach offers certain advantages from both a theoretical and practical standpoint.

Quantum Boltzmann Machines trained on quantum annealers produce noisy synthetic data.

Bayesian estimators for causal inference using hierarchical Gaussian Processes.

Paper introduces DTAE to optimize RL algorithms, balancing exploration and exploitation.

Quantum algorithms accelerate financial risk computation.

Deep networks learn hierarchical functions more efficiently than shallow ones.

Study benchmarks methods for learning non-Cartesian k-space trajectories and reconstruction.

Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This pr…

Proposes tail-adaptive f-divergences for better inference.

Distributed Lion optimizes large model training by reducing communication costs.

Replace softmax with interpolating function in neural nets.

RQMC improves kernel-based learning by reducing deterministic error and offering computational advantages.

Conditional meta-learning improves meta-learning performance in diverse task environments.

Quantum algorithms improve VaR and CVaR estimation for financial derivatives.

Quantum networks offer exponential communication savings for large machine learning models.

Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency…

Paper learns versatile balancing and recovery motions for humanoid robots.

New algorithm achieves logarithmic regret for adversarial online control.

Paper studies binary random projections with controllable sparsity patterns for computational and accuracy advantages.

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the …

We consider a standard optimal investment problem in a complete financial market driven by a Wiener process and derive an explicit formula for the optimal portfolio process in terms of the vertical derivative from functional It^o calculus. An advantage with this approach compared to the Malliavin calculus approach is t…

Empirical study compares finite- and infinite-width BNNs, revealing performance differences under model mismatch.

New method combines score lists using joint CDFs, improving computation.

Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…

We present novel minibatch stochastic optimization methods for empirical risk minimization problems, the methods efficiently leverage variance reduced first-order and sub-sampled higher-order information to accelerate the convergence speed. For quadratic objectives, we prove improved iteration complexity over state-of-…

The importance of counterparty credit risk to the derivative contracts was demonstrated consistently throughout the financial crisis of 2008. Accurate valuation of Credit value adjustment (CVA) is essential to reflect the economic values of these risks. In the present article, we reviewed several different approaches f…

New algorithm optimizes exploration in unknown MDPs.

In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general noncon…

Under several geometric conditions imposed below, the existence of the discrete spectrum below the essential spectrum is shown for the Dirichlet Laplacian on the quantum layer built over a spherically symmetric hypersurface with a pole embedded in the Euclidean space R4. At the end of this paper, we also show the advan…

Paper optimizes neural networks for Bermudan option pricing with faster convergence and risk management tools.

Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation tim…

New language models improve essay scoring accuracy.

Pricing and hedging rainbow options using Bayesian MS-VAR process.

We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably \emph{improves} with the data size, requiring an essentially constant number of…

Recent literature on online learning has focused on developing adaptive algorithms that take advantage of a regularity of the sequence of observations, yet retain worst-case performance guarantees. A complementary direction is to develop prediction methods that perform well against complex benchmarks. In this paper, we…

Unified framework combines trace-induced quantum kernels for improved machine learning models.

We present a new convolutional neural network-based time-series model. Typical convolutional neural network (CNN) architectures rely on the use of max-pooling operators in between layers, which leads to reduced resolution at the top layers. Instead, in this work we consider a fully convolutional network (FCN) architect…

We develop a semi-analytic approach to the valuation of auto-callable structures with accrual features subject to barrier conditions. Our approach is based on recent studies of multi-assed binaries, present in the literature. We extend these studies to the case of time-dependent parameters. We compare numerically the s…

Braid theory optimizes neural network structures.

Paper compares modern regression methods for time series data.

BraidNet uses braid theory to optimize neural networks for image classification.

A novel deep learning method for chemometric data improves performance over transfer learning.