arXiv research

Algorithm solves two-sided matching markets with unknown preferences and constraints.

Optimal reinsurance contracts designed for a continuum of risk types.

The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the mini…

In this paper, we study two classes of optimal reinsurance models from perspectives of both insurers and reinsurers by minimizing their convex combination where the risk is measured by a distortion risk measure and the premium is given by a distortion premium principle. Firstly, we show that how optimal reinsurance mod…

We introduce a probabilistic model of labor markets for university graduates, in particular, in Japan. To make a model of the market efficiently, we take into account several hypotheses. Namely, each company fixes the (business year independent) number of opening positions for newcomers. The ability of gathering newcom…

Many new models for measuring financial contagion have been presented recently. While these models have not been specified for investment funds directly, there are many similarities that could be explored to extend the models. In this work we explore ideas developed about financial contagion to create a network of inve…

Study examines how insurance affects households prone to proportional losses, especially those near poverty.

The paper calculates ruin probabilities for insurers with phase-type distributed claims.

Study optimal reinsurance and investment to minimize drawdown risk.

Study finds optimal board gender diversity for emissions performance.

The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive f…

Although recent studies have shown that electricity systems with shares of wind and solar above 80% can be affordable, economists have raised concerns about market integration. Correlated generation from variable renewable sources depresses market prices, which can cause wind and solar to cannibalise their own revenues…

A new model shows fairness mechanisms can improve selection utility even without implicit bias.

A new algorithm tackles submodular bandit problems with multiple constraints.

This work proposes an online learning approach to tighten constraints in stochastic control problems.

We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (…

Simplifies neural network constraints with computationally efficient method.

Reduces Lie (bi-)algebroids and Dirac manifolds using constraint vector bundles.

Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.

Holistic GLMs add constraints for better model quality.

A new ML method teaches constraints directly to models.

Paper tackles constrained bandit problems with a new learning framework.

In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…

Survey of Gaussian process constraints for modeling expensive data.

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj…

We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …

Iterative method learns unknown constraints for MPC control.

We reformulate data-dependent constraints to ensure they are always met with high probability.

New algorithm reduces regret and constraint violation in online convex optimization with complex constraints.

Algorithm ensures privacy while strictly adhering to constraints.

Proposes NUV priors for half-space and box constraints.

Meta-gradient D4PG optimizes performance and constraint adherence in RL.

Geometrically characterizes virtual nonlinear nonholonomic constraints using symplectic methods.

This work is a further study on the Generalized Constraint Neural Network (GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to embed any type of prior information and to select its imposing schemes. The work focuses on the second challenge and studies a new constraint imposing scheme for equa…

The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.

Develops a new method for optimizing with uncertain data.

Efficient algorithms decide algebraic constraints of causal graphs.

The paper introduces MU for NMF with $β$-divergences and disjoint constraints.

FISAR uses neural networks to optimize safe reinforcement learning with forward-invariant constraints.

Solves Einstein constraint equations on compact manifolds with specified boundaries.

We propose a general method for deformation quantization of any second-class constrained system on a symplectic manifold. The constraints determining an arbitrary constraint surface are in general defined only locally and can be components of a section of a non-trivial vector bundle over the phase-space manifold. The c…

Algorithm finds real line mapping from points under ordinal constraints.

HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.

Unified approach adjusts classifiers to meet system-level constraints.

Study optimizes portfolio allocation policies using off-policy data and constraints.

Algorithm minimizes regret while adhering to unknown safety constraints.

CoCoRL learns safe constraints from demonstrations with unknown rewards.

The paper trains neural networks with robustness guarantees using semidefinite constraints.