CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.
problem Optimizing solutions under uncertainty with reduced conservatism.
method CADRO uses a cost-aware ambiguity set to reduce DRO's conservatism.
result CADRO provides high-confidence upper bounds and consistent estimators of out-of-sample expected cost.
Unified neural network framework for context-aware Gaussian overbounds in uncertainty propagation.
problem Uncertainty quantification in safety-critical settings requires conservative bounds, but existing methods often fail to compose and are overly conservative.
method Proposes a learning framework that trains neural networks to produce context-aware Gaussian overbounds with provable conservatism.
result The method yields tighter bounds while maintaining conservatism on the enforced grid and in experiments.
A new method, Count-MORL, improves offline reinforcement learning by using state-action frequency.
problem Improving offline reinforcement learning performance.
method Integrates count-based conservatism into model-based offline reinforcement learning.
result The learned policy is near-optimal and outperforms existing methods.
Higher conservative training increases reward-hacking in reasoning models.
problem Reward hacking during online adaptation in reasoning models.
method Conservative offline training with varying levels of conservatism (β) was applied to a Qwen3-14B policy, and online adaptation was measured against a reward ensemble.
result Higher conservatism (β) increases reward-hacking damage, measured by the Goodhart gap and AUGC.
Study shows local governments smooth fiscal shocks from property tax revenues.
problem Impact of revenue shocks on local fiscal policy.
method Causal machine learning strategies and post-double-selection LASSO estimator.
result Local policymakers predominantly smooth fiscal shocks, but react differently to positive and negative shocks.
A new method improves efficiency of conformal prediction for ensemble models.
problem Efficiently estimating uncertainty for ensemble models without distributional assumptions.
method Proposes a multivariate score function to merge prediction regions of individual models, reducing conservatism.
result Demonstrates more efficient prediction regions compared to existing methods.
For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of…
We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice. The first approach replaces optimism with a simple randomization when using confidence sets. The second one adds random perturbations to its current estimate before maximizing the expe…
RORL improves offline RL robustness with conservative smoothing.
problem Distribution shift and robustness issues in offline RL.
method RORL introduces regularization and conservative smoothing for robustness.
result RORL achieves state-of-the-art performance and robustness to adversarial perturbations.
Offline RL policies should adapt to unknown aspects of the environment.
problem Uncertainty in offline RL datasets leads to suboptimal policies.
method Adaptive policies that consider all transitions seen so far, solving an implicit POMDP.
result Optimal adaptive policies improve offline RL performance.
Study compares model-free valuation to actual financial outcomes, finds it slightly conservative.
problem Evaluating the quality of model-free valuation approaches for financial derivatives.
method Empirical analysis using historical option prices from S&P 500 constituents.
result Model-free valuation approaches are only marginally more conservative than industry-standard models.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
The purpose of this paper is to identify a relevant statistical correlation between rate of default, RD, and loss given default, LGD, in a major Brazilian financial institution Retail Home Equity exposure rated using the IRB approach, so that we may find a causal relationship between the two risk parameters. Therefore,…
Paper improves neural network robustness analysis for safety-critical systems.
problem Uncertainty in neural network outputs for safety-critical systems.
method Unified propagation and partition approaches to provide tighter bounds.
result Proposed algorithms give tighter bounds than existing methods for the same computation time.
After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed disc…
New offline RL algorithms tackle partial data coverage with optimal performance and practicality.
problem Partial data coverage in offline RL datasets.
method Augmented Lagrangian method applied to MIS formulation for optimal offline RL.
result Statistically optimal offline RL with practical performance, eliminating conservatism.
A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.
problem Improving generative modeling by addressing non-conservatism issues.
method Proposes a conservative drifting method using kernel density estimator gradients to address non-conservatism.
result Proves finite-particle convergence rates for the conservative method, providing explicit quadrature constants.
New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.
problem Computing robust policies for high-stakes decisions with limited data.
method Soft-robust criterion using risk measures, two algorithms for optimization.
result Our algorithms produce less conservative solutions than existing methods.
A general theory of innovation and progress in human society is outlined, based on the combat between two opposite forces (conservatism/inertia and speculative herding "bubble" behavior). We contend that human affairs are characterized by ubiquitous ``bubbles'', which involve huge risks which would not otherwise be tak…
Deep active learning improves solvability prediction in power systems.
problem Inconclusive conservatism in traditional solvability region analysis methods.
method Proposes a deep active learning framework to reduce labeled dataset size.
result Significantly reduces the size of labeled dataset for training.
Paper introduces new risk measures for default risk and model uncertainty.
problem Model uncertainty and default risk in rating systems.
method Introduces default risk measures and discusses their properties and impacts.
result Different default risk measures and margins of conservatism affect risk-weighted assets.
Proposes a method to avoid excessive exploration in reinforcement learning.
problem Avoiding excessive exploration in reinforcement learning to deploy it in practice.
method Designs a novel algorithm using UCB reinforcement learning policy with adaptive exploration constraints.
result Proves that the approach remains conservative while minimizing regret in tabular settings and validates on real-world tasks.
Paper introduces a new risk measure for multivariate residual estimation.
problem Quantifying residual estimation risk in complex financial models.
method Developed a multivariate framework for residual estimation risk, defined using various risk measures, and proposed a back-testing criterion.
result Demonstrated the effectiveness of the new measure through back-testing on retail credit portfolios.
Adapts safe policies for exploration in high-risk settings.
problem Balancing safety and exploration in high-risk environments.
method Uses conformal calibration on a safe reference policy to determine aggressive action limits.
result Safe exploration improves performance without requiring model class identification or hyperparameter tuning.
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feasibility under a variety of operating conditions. In this paper, we explore an optimistic, or best-case view of uncertainty and show that it …
Improved stability analysis of neural network systems using Zames-Falb multipliers.
problem Analyzing stability of linear systems with neural network nonlinearities.
method Using integral quadratic constraints, sector-bounded and slope-restricted structure, and acausal Zames-Falb multipliers.
result Flexible and versatile framework for stability analysis with improved computational efficiency.
Study of linear classifiers in infinite imbalance scenarios.
problem Behavior of linear discriminant functions in extreme imbalance conditions.
method Analysis of linear classifiers under infinite imbalance, focusing on weight function properties and limit behavior.
result Limiting coefficient vectors reflect robustness or conservatism, optimizing against worst-case alternatives.
Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert inf…
Paper extends Bayes Theorem for interval probability estimates.
problem Real-world input probabilities are often interval estimates, not precise.
method Developed IT2 version of Bayes Theorem and a novel algorithm for encoding intervals.
result Conservative method avoids invalid output results from inconsistent input.
MCP extends conformal prediction to vector-valued score functions without data splitting.
problem Fixed prediction set shapes in scalar score functions limit coverage guarantees.
method MCP uses a single optimization problem for prediction set design and calibration, eliminating data splitting.
result RemMCP and RelMCP achieve target coverage with smaller or comparable prediction set sizes, reducing variance.
Study compares statistical properties and power of divergence measures for credit risk monitoring.
problem Detecting distributional shifts in credit risk models.
method Derives statistical properties and chi-square benchmark values for Jensen-Shannon Divergence and Kullback-Leibler Divergence, demonstrating their applicability in credit risk monitoring.
result Jensen-Shannon Divergence and Kullback-Leibler Divergence follow chi-square distributions and reveal practical trade-offs in minimizing false positives vs. detecting changes.
Paper studies S-rectangular DR-RL models for robust reinforcement learning with near-optimal sample complexity.
problem Addressing distributional discrepancies in reinforcement learning environments.
method Empirical value iteration algorithm for divergence-based S-rectangular DR-RL models.
result Near-optimal sample complexity bound of O(∣S∣∣A∣(1−γ)−4ε−2). Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
Proposes a method to learn adaptive ambiguity sets for robust optimization.
problem Misspecification in distributionally robust optimization (DRO).
method Learned predictive ambiguity sets (LPAS) using deep contextual models.
result Significantly improves portfolio optimization performance compared to baselines.
This paper identifies knot projections with reductivity two.
problem Determining knot projections with a specific reductivity level.
method Examined four types of reductivity (Seifert type splice, non-Seifert type splice, recursively, simultaneously) and their combinations.
result Identified all knot projections with reductivity two for the four definitions.
This paper classifies instantons with closed reductions and provides examples of non-closed reductions.
problem Understanding the geometry of toric Kähler instantons with and without closed reductions.
method Sharp geometric criteria and examples of instantons with different reduction types.
result Established geometric criteria for closed reductions and classified asymptotic geometries.
We consider locally conformal Kaehler geometry as an equivariant (homothetic) Kaehler geometry: a locally conformal Kaehler manifold is, up to equivalence, a pair (K,Γ) where K is a Kaehler manifold and Γa discrete Lie group of biholomorphic homotheties acting freely and properly discontinuously. We define a new invari…
In this paper we describe Routhian reduction as a special case of standard symplectic reduction, also called Marsden-Weinstein reduction. We use this correspondence to present a generalization of Routhian reduction for quasi-invariant Lagrangians, i.e. Lagrangians that are invariant up to a total time derivative. We sh…
Two reduction schemes for symplectic manifolds are shown equivalent.
problem Reduction of Hamiltonian systems on exact symplectic manifolds.
method Modified Marsden-Meyer-Weinstein reduction theorem for exact symplectic manifolds and contact manifolds.
result Reduction schemes are equivalent for exact symplectic manifolds and energy hypersurfaces.
Study extends Kobayashi's method to non-reductive subgroups for homogeneous spaces.
problem Existence of compact Clifford-Klein forms in homogeneous spaces.
method Extend Kobayashi's method to non-reductive subgroups and compare Cartan projections and non-compact dimensions.
result Examples of homogeneous spaces without compact Clifford-Klein forms.
The purpose of this paper is to generalize the regular Optimal Reduction Theorem to general proper Dirac actions, formulated both in terms of point and orbit reduction. A comparison to general standard singular Dirac reduction is given emphasizing the desingularization role played by optimal reduction.
We show that the contact reduction can be specialized to Sasakian manifolds. We link this Sasakian reduction to Kähler reduction by considering the Kähler cone over a Sasakian manifold. We present examples of Sasakian manifolds obtained by S1 reduction of standard Sasakian spheres.
Study characterizes naturally reductive metrics on homogeneous manifolds.
problem Characterizing naturally reductive (α1,α2) metrics on homogeneous manifolds. method Characterization through local f-products and equivalence of properties. result Explicit flag curvature formula for naturally reductive metrics.
Abstract: Generalized reduction methods for symmetries in graded geometry.
problem Generalized reduction of symmetries in graded geometry.
method Graded symplectic reduction for Courant, Dirac, and generalized complex structures.
result Systematic recovery of reduction schemes for exact cases.
In this note we give conditions which ensure the reduction of a symplectic connection in the process of a Marsden-Weinstein reduction and of the reduction of a presymplectic manifold.
This work introduces a unified approach to the reduction of Poisson manifolds using their description by graded symplectic manifolds. This yields a generalization of the classical Poisson reduction by distributions (Marsden-Ratiu reduction). Further it allows one to construct actions of strict Lie 2-groups and to descr…
The reduction of nonholonomic systems is formulated in terms of Dirac reduction. An optimal reduction method for a class of nonholonomic systems is formulated. Several examples are studied in detail.
This paper extends symplectic reduction to cosymplectic groupoids and explores their properties.
problem Cosymplectic groupoids and their reductions.
method Analogous to symplectic reduction, the authors extend the Marsden-Weinstein-Meyer reduction to cosymplectic groupoids.
result Integration commutes with reduction for algebroids associated with cosymplectic groupoids.