arXiv research

Third part of a study on liquidity risk in asset management, focusing on managing the asset-liability liquidity risk.

Paper proposes a RL approach for ALM with superior performance.

The Monte Carlo pathwise sensitivities approach is well established for smooth payoff functions. In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions. Our main tool is to combine the one-step survival idea of Glasserman and Staum wi…

The paper introduces deep learning for ALM, enhancing asset and liability management.

A framework tackles model uncertainty in ALM, providing robust investment strategies.

A new method tackles nonconvex optimization with penalties and proximal terms.

Study validates Libor model for insurance benefits calculation.

New insights into convergence of optimization methods for DAG structure learning.

We study a continuous-time asset-allocation problem for an insurance firm that backs up liabilities from multiple non-life business lines with underwriting profits and investment income. The insurance risks are captured via a multidimensional jump-diffusion process with a multivariate compound Poisson process with depe…

We discuss the role of integrated chance constraints (ICC) as quantitative risk constraints in asset and liability management (ALM) for pension funds. We define two types of ICC: the one period integrated chance constraint (OICC) and the multiperiod integrated chance constraint (MICC). As their names suggest, the OICC …

Automated model tracks mouse behavior in home cages.

Efficiently estimates hub graphical models with structured sparsity.

A new method solves large-scale sparse group square-root Lasso problems efficiently.

In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likelihood term and a Trace norm term. The Bi-factorization approach and the Augmented Lagrangian Method (ALM) are applied to find the global minimi…

Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict…

Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selection methods usually cannot keep more local structure information. To address the defects previously mentioned, we propose a novel supervised o…

New model predicts financial market abnormalities using stock index uncertainties.

Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficie…

Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method…

A scalable PyTorch framework for non-crossing quantile regression.

Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than minimizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the …

This paper compares different DRO formulations for pension fund management.

Paper uses MLMC for SCR calculation and stress tests, showing computational efficiency.

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features straightforward. However, feature concatenation is a natural way to combine multi-view da…

The aim of this paper is to introduce a synthetic ALM model that catches the main specificity of life insurance contracts. First, it keeps track of both market and book values to apply the regulatory profit sharing rule. Second, it introduces a determination of the crediting rate to policyholders that is close to the p…

AGNN improves network localization accuracy by 37-53% in NLOS conditions.

We consider triholomorphic maps from an almost hyper-Hermitian manifold $\mathcal{M}^{4m}$ into a hyperKähler manifold $\mathcal{N}^{4n}$. This means that $u \in W^{1,2}$ satisfies a quaternionic del-bar equation. We work under the assumption that $u$ is locally strongly approximable in $W^{1,2}$ by smooth maps: then s…

LATM framework uses LLMs to create and reuse tools for efficient problem-solving.

UniFeat is an open-source Java tool for feature selection.

New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.

We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learning as a concept. To realize this, we train sensory-motor data recorded during a t…

A study ranks critical Lean Six Sigma tools for implementation in Portuguese companies.

Interactive tool helps RL researchers debug and understand their models.

Survey of tools for studying hierarchical hyperbolic spaces.

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…

ToolChain-CRC addresses the risk-control problem for retrieval-augmented and tool-using agents under drift.

New methods improve tool-to-tool matching in semiconductor manufacturing.

Survey of software developers' experience with Github Copilot tool.

In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools…

TIR expands LLM capabilities by enabling problem-solving strategies.

New L0 norm added to TDA for market analysis.

Interview study reveals considerations for designing semi-automated bias detection tools.

We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines…

Develops tools to audit ML models for bias and unfairness.

FiNCAT tool automatically identifies financial numerals in documents.

Teaching tool simplifies Monte Carlo simulation for project risk analysis.

Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.

A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The W…