Third part of a study on liquidity risk in asset management, focusing on managing the asset-liability liquidity risk.
problem Managing the asset-liability liquidity risk in asset management.
method Develops a methodological and practical framework for liquidity stress testing programs.
result Proposes measurement, management, and monitoring tools for controlling the liquidity gap.
Paper proposes a RL approach for ALM with superior performance.
problem Dynamic asset-liability management in financial markets.
method Continuous-time RL with LQ formulation, policy gradient, adaptive and scheduled exploration.
result Method outperforms traditional and state-of-the-art RL algorithms in ALM.
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.
problem Optimizing asset and liability management for treasurers and other applications.
method Deep learning applied to ALM for optimal decision making.
result Enhanced ALM approach for better asset and liability management.
A framework tackles model uncertainty in ALM, providing robust investment strategies.
problem Model uncertainty in asset liability management (ALM).
method Wasserstein barycenter approach to handle various information sources and uncertainties.
result The proposed framework selects robust investment portfolios that remain optimal under various uncertainties.
A new method tackles nonconvex optimization with penalties and proximal terms.
problem Nonconvex optimization problems with equality and inequality constraints.
method Inexact proximal augmented Lagrangian method (P-ALM) with adaptive penalty and proximal parameters.
result Effective convergence properties and numerical superiority over traditional methods.
Study validates Libor model for insurance benefits calculation.
problem Valuation of long-term insurance guarantees.
method Mean-field Libor market model, numerical ALM, aggregated life insurance data.
result Derives estimators for future discretionary benefits.
New insights into convergence of optimization methods for DAG structure learning.
problem Unclear convergence properties of optimization methods for structure learning.
method Examined the convergence of augmented Lagrangian method (ALM) and quadratic penalty method (QPM) for structure learning.
result Standard convergence result of ALM does not hold in various cases, and QPM is prone to ill-conditioning.
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.
problem Confounding analysis in specialized arenas.
method Group Behaviour Model (GBM) using permutation matrix.
result Model captures individual and group behavior.
Efficiently estimates hub graphical models with structured sparsity.
problem Computational difficulty in fitting graphical models with hub nodes, especially in high-dimensional data.
method Two-phase algorithm: ADMM for initial point generation and SSN-ALM for accurate solution.
result Significantly improves estimation accuracy and efficiency compared to existing methods.
A new method solves large-scale sparse group square-root Lasso problems efficiently.
problem Large-scale linearly constrained sparse group square-root Lasso problems.
method Dual semismooth Newton based augmented Lagrangian method (ALM).
result The proposed method efficiently solves the problem with numerical experiments demonstrating its effectiveness.
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…
A new method selects features for better model performance.
problem Improving model performance by selecting effective features.
method Supervised orthogonal least square regression with feature weighting.
result The method reduces feature dimensionality and improves classification results.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
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.
problem Non-crossing quantile regression to avoid impossible negative probability densities.
method CJQR-ALM combining Augmented Lagrangian Method, differentiable pinball loss, and L-BFGS optimization.
result Achieves near-zero crossing rates on large datasets within minutes.
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.
problem Navigating uncertainty in asset liability management for pension funds.
method Three DRO formulations: mixture, box, and Wasserstein ambiguity sets.
result Wasserstein and box ambiguity sets outperform traditional approaches in fund performance.
Paper uses MLMC for SCR calculation and stress tests, showing computational efficiency.
problem Computing SCR and stress tests for insurance companies.
method Multilevel Monte-Carlo (MLMC) estimator for maximum of conditional expectations.
result MLMC estimator is computationally more efficient and avoids regression issues.
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.
problem Massive network localization under Non-Line-of-Sight conditions.
method Attentional Graph Neural Network (AGNN) with Adjacency Learning Module (ALM) and Multiple Graph Attention Layers (MGAL).
result Significant improvement in localization accuracy, approaching fundamental lower bounds.
We consider triholomorphic maps from an almost hyper-Hermitian manifold M4m into a hyperKähler manifold N4n. This means that u∈W1,2 satisfies a quaternionic del-bar equation. We work under the assumption that u is locally strongly approximable in W1,2 by smooth maps: then s…
LATM framework uses LLMs to create and reuse tools for efficient problem-solving.
problem Improving problem-solving capabilities of LLMs with external tools.
method Closed-loop framework with two phases: tool making and tool using. LLMs act as both tool makers and users. Resource-intensive model for tool making, lightweight model for tool using.
result LATM framework achieves performance equivalent to using a powerful LLM for both roles but with significantly reduced costs.
UniFeat is an open-source Java tool for feature selection.
problem Efficient feature selection in various research areas.
method Provides a set of advanced feature selection methods.
result Facilitates rapid development of new feature selection algorithms.
New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.
problem Limitations of purely memorized models in recalling large amounts of factual information.
method Demonstrated the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall.
result Proved that tool-use enables unbounded factual recall via a simple and efficient circuit construction.
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.
problem Identifying the most important tools for successful Lean Six Sigma implementation in Portugal.
method An online survey with Portuguese consultants evaluated 37 tools based on frequency of use, difficulty, importance, and impact. A ranking was developed using a procedure to assess consultants' know-how.
result Honshin Kanri, VOC, VSM were identified as the most important tools for Lean Six Sigma implementation.
Interactive tool helps RL researchers debug and understand their models.
problem Challenges in debugging reinforcement learning algorithms.
method Design and implementation of an interactive visualization tool.
result Addresses features missing from previous tools for RL.
Survey of tools for studying hierarchical hyperbolic spaces.
problem Understanding hierarchical hyperbolic spaces.
method Various tools developed for studying HHSs.
result Ease of use for non-experts in HHS machinery.
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.
problem Risk-control problem for retrieval-augmented and tool-using agents under drift.
method ToolChain-CRC uses conformal risk-control under exchangeable calibration runs.
result Trajectory-level risk control keeps accepted-trajectory risk below the target.
Model learns tool affordances from vision, enabling tool selection.
problem Learning tool affordances from visual input.
method Vision-based generative model with task predictor.
result Agents can select appropriate tools based on task success criteria.
New methods improve tool-to-tool matching in semiconductor manufacturing.
problem Challenges in obtaining static configuration data and extending methods to heterogeneous equipment.
method Novel TTTM analysis pipelines hypothesizing higher variance and modes for mismatched equipment.
result Best univariate method achieves correlation coefficients >0.95 and >0.5 with variance and modes, respectively.
Survey of software developers' experience with Github Copilot tool.
problem Investigate developers' acceptance of AI-generated code.
method Survey with 18 questions distributed to 42 programmers.
result Mixed developer opinions, mostly positive but reluctance to use.
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.
problem Lack of a principled theory explaining why LLMs with tools are more capable.
method Formal proof and Advantage Shaping Policy Optimization (ASPO) algorithm.
result TIR model decisively outperforms pure-text models on challenging benchmarks.
New L0 norm added to TDA for market analysis.
problem Improving TDA tools for market prediction.
method Defined and applied L0 norm in TDA for four markets.
result Enhanced TDA tools for market analysis.
Interview study reveals considerations for designing semi-automated bias detection tools.
problem Detecting and mitigating machine learning biases.
method Interview study with 11 machine learning practitioners.
result Four considerations identified for tool design.
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.
problem Auditing ML models for individual bias and unfairness.
method Formalizes the task as an optimization problem and develops inferential tools for the optimal value.
result Demonstrates the utility of tools in revealing biases in COMPAS recidivism prediction instrument.
FiNCAT tool automatically identifies financial numerals in documents.
problem Differentiating between in-claim and out-of-claim numerals in financial documents.
method Extracts context embeddings of numerals using BERT, then uses Logistic Regression to classify.
result Achieved a Macro F1 score of 0.8223 on validation set.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
problem Difficulty in students performing Monte Carlo Simulation in risk analysis.
method Introducing MCSimulRisk as a teaching tool.
result Students can perform Monte Carlo simulation and apply it to projects of any complexity.