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 …
We report on results concerning a partially aggregated Stock Flow Consistent (SFC) macroeconomic model in the stationary state where the sectors of banks and firms are aggregated, the sector of households is dis-aggregated, and the probability density function (pdf) of the wealth of households is exogenous, constrained…
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.
We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function o…
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…
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.
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.
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.
Banking system crises are complex events that in a short span of time can inflict extensive damage to banks themselves and to the external economy. The crisis literature has so far identified a number of distinct effects or channels that can propagate distress contagiously both directly within the banking network itsel…
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.
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.
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.
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.
Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Usually the low-dimensional structure is encoded by the presence of the (unknown) parameters in some low-dimensional model subspace. In such settings, it is desirable for est…
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 establishes MLE consistency for market microstructure models.
problem Estimating parameters in partially observed diffusion models.
method Tractable sufficient condition for MLE consistency based on stationary distribution.
result Maximum likelihood estimators are consistent for market microstructure parameters.
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.
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.
Consistent model selection for spiked Wigner model via AIC-type criteria.
problem Estimating the number of spiked eigenvalues in the spiked Wigner model.
method AIC-type model selection criteria with parameters γ.
result Strong consistency for γ > 2 and weak consistency for γ = 2 + δ_N.
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.
Paper develops a consistent estimator for discrete mixture models.
problem Estimating mixing probability density in discrete mixture models.
method Develops an L1 consistent estimator under specific conditions. result Consistent estimation of mixing probability density for every density f. Study uniform consistency in nonparametric mixture models and mixed regression.
problem Uniform consistency in nonparametric mixture models and mixed regression models.
method Construct uniformly consistent estimators under general conditions, develop novel technical tools.
result Prove uniform consistency results for nonparametric mixtures and mixed regression models.
GES algorithm improves consistency for nonparametric DAG models.
problem Consistent estimation of nonparametric DAG models.
method Greedy equivalence search with new consistency proof for nonparametric families.
result Consistency of GES for general nonparametric DAG models with smooth factorization.
Improved neural network surrogates for ICF using manifold and cycle consistency.
problem Modeling and predicting complex physical processes in inertial confinement fusion.
method Training neural network surrogates that are consistent with the physical manifold and cyclically consistent.
result Surrogates are superior in predictive performance, more resilient to sampling artifacts, and more data efficient.
Develops methods to create consistent surrogate models for agent-based simulators.
problem High computational costs and misjudgment of interventions in agent-based models.
method Causal abstractions to learn interventionally consistent surrogate models.
result Surrogates trained for interventional consistency closely mimic the agent-based model's behavior under interventions.
Study assesses consistency and reproducibility of LLMs in finance and accounting tasks.
problem Consistency and reproducibility of LLM outputs in finance and accounting research.
method Extensive experimentation with 50 independent runs across 5 tasks using 3 OpenAI models.
result Task-specific patterns of consistency and reproducibility, with binary classification and sentiment analysis achieving near-perfect reproducibility.
Lasso proves consistent model selection for high-dimensional Ising models.
problem Model selection consistency of Lasso for high-dimensional Ising models.
method Theoretical analysis of Lasso with and without post-thresholding for Ising models.
result Lasso without post-thresholding is model selection consistent in the whole paramagnetic phase with n=Ω(d3logp). 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.
Stable and consistent model alignment for language models without assuming human preference models.
problem Lack of statistical consistency in existing alignment methods.
method Relative density ratio optimization between preferred and mixture of preferred and non-preferred data distributions.
result Our approach achieves statistical consistency and stability, providing tighter convergence guarantees.
Measures consistency of tabular LLM predictions under fine-tuning multiplicity.
problem Conflicting predictions from fine-tuned tabular LLMs.
method Local stability measure in embedding space.
result Probabilistic guarantees on prediction consistency under multiplicity.
Gini index needs auto-calibration for consistent decision-making.
problem Gini index's inconsistency in decision-making.
method Restrict Gini index to auto-calibrated regression models.
result Gini index becomes strictly consistent with auto-calibration.
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.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
problem Improving self-consistency in language model rankings.
method Adapting Contrast-Consistent Search (CCS) to Contrast-Consistent Ranking (CCR) for ranking tasks.
result CCR probing outperforms prompting techniques across different models and datasets.
Consistent selection of predictors in high-dimensional binary models with misspecified parameters.
problem Selection of predictors in high-dimensional binary models with misspecified parameters.
method Two-step selection procedure: screening and ordering predictors by Lasso, followed by model selection.
result Consistent selection of the support of the minimizer of the associated risk.
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
Paper proposes a method to validate statistical models based on data consistency.
problem Validation of statistical modeling assumptions in scientific inference problems.
method Automatic evaluation of model consistency with observed data.
result The proposed criterion assesses models' ability to generate similar data.
Algorithm estimates nonparametric mixtures from grouped data.
problem Estimating identifiable nonparametric mixture models from grouped observations.
method Oracle inequality for weighted kernel density estimators and general consistency result.
result Consistent estimation of mixture components from grouped observations.
Consistency models generate high-quality samples fast and without iterative sampling.
problem Slow generation in diffusion models.
method Direct mapping of noise to data, supporting fast one-step generation and multistep sampling.
result Consistency models achieve state-of-the-art FID scores and outperform diffusion models in one-step generation.
Develops transparent global models consistent with local explanations.
problem Creating globally interpretable models that align with local explanations from black-box models.
method Custom boolean features from sparse local contrastive explanations are used to train a globally transparent model.
result Custom transparent models have higher local consistency compared to other strategies.
Proposes a method for explaining black-box models with nested feature attributions.
problem Making black-box models transparent and trustworthy.
method Model-agnostic local explanation method exploiting nested feature structure and consistency property.
result Accurate and consistent HiFAs and LoFAs estimated using fewer model queries.
CHIP model detects communities in continuous-time networks efficiently.
problem Detecting communities in large, timestamped networks.
method Spectral clustering on aggregated adjacency matrix of Hawkes process model.
result Consistent community detection for growing networks with efficient estimation.
Efficiently creates label-consistent backdoor attacks without obvious mislabeling.
problem Vulnerability of deep neural networks to backdoor attacks that can be triggered by a backdoor trigger.
method Uses adversarial perturbations and generative models to inject inputs that are consistent with their labels.
result Demonstrates the feasibility of creating undetectable backdoor attacks by maintaining label-consistency.
New models capture dynamic derivatives pricing with efficient simulations.
problem Capturing dynamic features of derivatives' term structures.
method Machine learning techniques to store and efficiently simulate complex drift terms.
result First efficient dynamic term structure models.
In distributed ML applications, shared parameters are usually replicated among computing nodes to minimize network overhead. Therefore, proper consistency model must be carefully chosen to ensure algorithm's correctness and provide high throughput. Existing consistency models used in general-purpose databases and moder…
New framework improves generative models with prediction and consistency constraints.
problem Improving generative models with sparse labeled data.
method Optimizes variational autoencoders with prediction and consistency constraints.
result Promising image classification performance, especially in semi-supervised scenarios.
Chronologically consistent models maintain accuracy with time-restricted data.
problem Training data introduces lookahead bias and training leakage in large language models.
method ChronoBERT and ChronoGPT trained with only available data at each time point.
result Models achieve strong performance and competitive with larger models, mitigating lookahead bias.
Consistent partial identification of causal effects proved for neural models.
problem Consistency of neural causal partial identification methods.
method Proving consistency for neural models with continuous and categorical variables, considering architecture design and Lipschitz regularization.
result Proven consistency of partial identification via neural causal models in a general setting.