Study on macroeconomic model with wealth distribution constraints.
problem Modeling macroeconomics with nonuniform wealth distribution.
method Stock Flow Consistent (SFC) model, nonuniform distribution, econometric data constraints, Monte Carlo simulations.
result Reduced constraint to a single equation, relating to mass transport and random variables.
We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP).The set of solutions is a polytope, which volume depends on the constraintsapplied and reveals the potential fragility of the economic circuit,with no need to study the dynamics. Several …
Negative interest rates stabilize economies, but physical cash limits their effectiveness.
problem Lack of effectiveness of negative interest rates in stabilizing economies.
method Simplified stock-flow consistent model, simulation evidence, discussion of alternative solutions.
result Negative interest rates can stabilize economies, but physical cash limits their effectiveness.
Modeling bank panics and financial crises with contagion channels.
problem Understanding and predicting financial crises and contagion effects.
method Develops a comprehensive model for systemic risk that includes stock-flow consistency and Asset-Liability symmetry.
result Identifies and models the dangerous spillover effects that dominate future financial crises.
Model explains stock price bubbles through debt crises and financial crashes.
problem Analyzing financial fragility and stock price bubbles.
method Stock-flow consistent model integrating macroeconomic and financial market dynamics.
result Model demonstrates how credit expansion and crash risk lead to recurrent boom-bust cycles.
We present a macroeconomic agent-based model that combines several mechanisms operating at the same timescale, while remaining mathematically tractable. It comprises enterprises and workers who compete in a job market and a commodity goods market. The model is stock-flow consistent; a bank lends money charging interest…
Investigates the impact of narrow banking on macroeconomics.
problem The risks and benefits of a full reserve requirement on demand deposits.
method Extended Goodwin-Keen model with time deposits and central bank reserves; numerical examples.
result Narrow banking does not reduce economic growth but improves financial stability.
Model analyzes inventory growth cycles with debt-financed investment.
problem Understanding inventory growth cycles in economies with debt financing.
method Continuous-time stock-flow consistent model for inventory dynamics.
result Model reveals Kitchin cycles in short-run dynamics.
Recently, in order to explore the mechanism behind wealth or income distribution, several models have been proposed by applying principles of statistical mechanics. These models share some characteristics, such as consisting of a group of individual agents, a pile of money and a specific trading rule. Whatever the trad…
D2D converts CLDs into SDMs to explore leverage points under uncertainty.
problem Limited dynamic analysis of CLDs for intervention strategies.
method Minimal user input protocol to convert CLDs into SDMs, simulating interventions.
result D2D helps distinguish leverage points and provides uncertainty estimates.
Study of loss functions for learning to defer, proving consistency.
problem Learning to defer in machine learning.
method Introduced a family of surrogate losses parameterized by Ψ and proved their consistency. result Proved realizable H-consistency and Bayes-consistency of specific surrogate losses. In this paper we study the consistency of an empirical minimum error entropy (MEE) algorithm in a regression setting. We introduce two types of consistency. The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the…
Paper analyzes consistency between neural networks at different levels.
problem Understanding knowledge consistency between neural networks.
method Generic definition and task-agnostic method to disentangle consistent knowledge.
result Knowledge consistency provides new insights and can improve network performance.
This paper improves deep learning model consistency through ensemble methods.
problem Consistency and correct-consistency issues in deep learning models.
method Formal definition of consistency and correct-consistency, proving ensemble improvement, proposing dynamic snapshot ensemble method.
result Ensemble methods can improve correct-consistency of deep learning models.
Empirical study shows consistent meta-RL algorithms adapt to OOD tasks.
problem Theoretical consistency of meta-RL algorithms and its practical implications.
method Empirical investigation of representative meta-RL algorithms, focusing on consistency and adaptation to out-of-distribution tasks.
result Theoretical consistent algorithms can adapt to OOD tasks, while inconsistent ones cannot, but can still fail for poor exploration.
Paper explores grafting consistent estimators to improve Random Forest consistency.
problem Ensuring Random Forests are consistent despite their performance.
method Grafting consistent estimators onto a shallow CART.
result Grafted estimators provide a consistency guarantee and perform well empirically.
Paper proves LCVB method's consistency in Bayesian posteriors and decision rules.
problem Approximating Bayesian posteriors and decision rules.
method Loss-calibrated variational Bayes (LCVB) method.
result LCVB method's consistency in both approximate posterior and decision rules.
Unified model improves sampling speed and quality.
problem Difficult to balance sampling speed and quality.
method Multistep Consistency Models combining consistency and diffusion models.
result Improved sampling quality with reduced steps.
Quick Shift gets consistent at clustering and mode recovery.
problem Consistency in clustering and mode recovery for Quick Shift.
method Statistical consistency guarantees for Quick Shift under mild assumptions.
result Constructed a consistent modal regression algorithm.
Paper connects risk consistency to L_p consistency for broader loss functions.
problem Establishing risk consistency for a wider class of loss functions.
method Analyzes the connection between risk consistency and L_p-consistency for various loss functions.
result Shifted loss functions do not reduce assumptions as much as other results.
Improves GAN-based semi-supervised learning with consistency regularization.
problem Lack of consistency in class probability predictions under local perturbations.
method Introduces consistency regularization to GANs, leveraging both local and interpolation consistency.
result Significantly improves performance and achieves new state-of-the-art results.
New risk measures for multivariate data, consistent and decomposable.
problem Developing consistent risk measures for multiple variables.
method Showed strong consistency leads to decomposition into aggregation and univariate risk.
result Multivariate risk measures are conditional certainty equivalents under strong consistency.
This paper establishes a theoretical foundation for consistency training in diffusion models.
problem Lack of a comprehensive theoretical understanding of consistency training in diffusion models.
method Demonstrates the necessity of a number of steps in consistency learning exceeding d5/2/ε for generating samples within ε proximity to the target distribution. result Establishes rigorous insights into the validity and efficacy of consistency models, offering theoretical underpinnings for their utility.
Paper introduces new actuarial-consistent valuations for insurance liabilities.
problem Valuation of insurance liabilities considering both financial and actuarial risks.
method Proposes two-step actuarial valuations and actuarial-consistent procedures.
result Actuarial-consistent valuations are equivalent to two-step actuarial valuations under coherence.
New clustering method avoids problematic properties of existing algorithms.
problem Existing clustering algorithms cannot satisfy all natural clustering properties.
method Developed Morse Clustering using Morse Theory to satisfy Kleinberg's axioms with a new property, Monotonic Consistency.
result Morse Clustering satisfies Kleinberg's original axioms with Consistency replaced by Monotonic Consistency.
Universal Bayes consistency proved in metric spaces.
problem Proving universal Bayes consistency in metric spaces.
method Extending a multiclass learning algorithm and proving its Bayes-consistency in all metric spaces.
result First learning algorithm universally strongly Bayes-consistent in all metric spaces.
We improve random forest consistency and performance with DMRF, a new variant.
problem Improving the consistency and performance of random forest models.
method Developed DMRF, a data-driven multinomial random forest, by modifying proof methods and improving data utilization.
result DMRF achieves strong consistency with probability 1, surpassing previous models in classification tasks.
Fisher consistency improves class probability estimation under dataset shift.
problem Lack of Fisher consistency can lead to unreliable class probability estimates.
method Introduced Fisher consistency as a desirable property for class prior probability estimators.
result CDE-Iterate is not Fisher consistent and cannot be trusted for reliable estimates.
This paper tackles deferral learning with multiple experts, providing strong theoretical guarantees.
problem Optimizing input assignment to experts balancing accuracy and computational cost.
method Introducing new surrogate loss functions and efficient algorithms with strong theoretical learning guarantees.
result Realizable H-consistency, H-consistency bounds, and Bayes-consistency for deferral learning. Unified surrogate loss framework for multi-label learning with strong consistency guarantees.
problem Improving consistency and accounting for label correlations in multi-label learning.
method Introducing multi-label logistic loss and extending it to comprehensive multi-label comp-sum losses, proving strong consistency guarantees for any multi-label loss.
result Unified surrogate loss framework benefiting from strong consistency guarantees for any multi-label loss.
Study of hill-climbing clustering methods and their consistency.
problem Consistency of hill-climbing clustering methods.
method Continuous-space and discrete-space hill-climbing approaches.
result Established consistency of the methods.
Learning rule consistency tied to non-existence of real-valued measurable cardinals.
problem Consistency of k-NN learning rule in metric spaces.
method Analyzing separable subspaces and density conditions.
result The k-NN classifier's consistency depends on the absence of real-valued measurable cardinals.
The paper proves consistency of GVI posteriors under minimal conditions.
problem Consistency of generalized variational inference posteriors.
method Proves consistency using Γ-convergence theory. result GVI posteriors are consistent and collapse to the population-optimal parameter value.
New sampling method makes fictitious play consistent in repeated games.
problem Fictitious play fails to be Hannan consistent in repeated games.
method Introduced sampled fictitious play with Bernoulli sampling, proving it is Hannan consistent.
result Sampled fictitious play is Hannan consistent using anti-concentration results.
New method evaluates language model forecasters by checking consistency of predictions.
problem Evaluating the performance of language model forecasters is difficult due to lack of ground truth.
method Developed a consistency check framework based on arbitrage to evaluate forecasters.
result Consistency metrics correlate with ground truth performance of LLM forecasters.
Study improves H-consistency bounds for regression analysis.
problem Improving H-consistency bounds for regression analysis. method Generalized theorems and novel H-consistency bounds for various surrogate loss functions. result Derives principled surrogate losses for adversarial regression.
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
Self-consistent models improve reinforcement learning by aligning predictions with future values.
problem Improving reinforcement learning by aligning model predictions with future values.
method Proposes multiple self-consistency updates to encourage a learned model and value function to be consistent with each other.
result Self-consistency helps both policy evaluation and control in both tabular and function approximation settings.
Consistent estimation of constrained autoregressive processes.
problem Estimating autoregressive processes with coefficients constrained to an ellipsoid.
method Use of constrained and penalized estimators under different norms.
result Provide consistency results for estimation of constrained autoregressive processes.
Prefix consistency improves model reliability by weighting answers based on their reproducibility.
problem Improving the reliability of large language models' reasoning traces.
method Use prefix consistency to weight candidate answers based on their reproducibility during regeneration.
result Prefix consistency is the best correctness predictor, reaching Standard MV plateau accuracy with up to 21x fewer tokens.
The paper concerns primal and dual representations as well as time consistency of set-valued dynamic risk measures. Set-valued risk measures appear naturally when markets with transaction costs are considered and capital requirements can be made in a basket of currencies or assets. Time consistency of scalar risk measu…
The paper explores time consistency for scalar multivariate risk measures in markets with transaction costs.
problem Time consistency of scalar multivariate risk measures in markets with transaction costs.
method Presented dual representations and derived an equivalent recursive formulation for multivariate scalar risk measures.
result Developed a direct notion of a 'moving scalarization' for scalar time consistency.
Consistency regularization improves robustness to noisy labels.
problem Improving model robustness to noisy labels in machine learning.
method Empirical study of consistency regularization on noisy datasets.
result Consistency regularization improves model robustness to label noise.
The paper establishes conditions for Bayesian consistency in supremum metric.
problem Ensuring Bayesian consistency in the supremum metric.
method Using a triangle inequality and weak convergence, the paper establishes conditions for Bayesian consistency.
result Demonstrates supremum consistency with weaker conditions than previously used.
Enhances random forest consistency and introduces DMRF for improved performance.
problem Improving the consistency and efficiency of random forest algorithms.
method Strengthened proof methods and propose DMRF algorithm.
result DMRF achieves better theoretical and experimental performance than previous variants.
Adversarial consistency depends on the uniqueness of adversarial Bayes classifiers.
problem Consistency of adversarial surrogate losses is not guaranteed.
method Connected consistency of adversarial surrogate losses to the uniqueness of adversarial Bayes classifiers.
result A convex surrogate loss is statistically consistent for adversarial learning if and only if the adversarial Bayes classifier is unique.
Develops PLL methods that are provably consistent and compatible with any deep network.
problem Lack of theoretical understanding and consistency in partial-label learning methods.
method Proposes a generation model for candidate label sets and develops two PLL methods that are risk- and classifier-consistent.
result Two novel PLL methods are guaranteed to be provably consistent.
New method improves consistency in preference learning for neural networks.
problem Inconsistent surrogate losses in preference learning for neural networks.
method Formulated a margin-shifted ranking framework and introduced Structure-Aware H-consistency. result Proved superior consistency guarantees for capacity-bounded models using heavy-tailed surrogates.