A new Metropolis-Hastings method reduces the cost of testing for large datasets.
problem Reducing the cost of Metropolis-Hastings tests for large datasets.
method Uses small minibatches and a novel Barker acceptance test with additive correction.
result Achieves arbitrarily small batch sizes by adjusting proposal step size or temperature.
The theory of acceptance sets and their associated risk measures plays a key role in the design of capital adequacy tests. The objective of this paper is to investigate, in the context of bounded financial positions, the class of surplus-invariant acceptance sets. These are characterized by the fact that acceptability …
A new capital adequacy test is proposed based on value-at-risk.
problem Regulator's need for a capital adequacy test that doesn't depend on firms' surplus or currency.
method Proving that the only surplus-invariant, law-invariant, and conic acceptance set is the set of positions with negative value-at-risk.
result The value-at-risk test is the only possible capital adequacy test under specified conditions.
Proposes a method to estimate acceptance regions for many classes, including new ones.
problem Lack of methods to handle new classes in set-valued classification.
method Generalized Prediction Set (GPS) approach to estimate acceptance regions.
result Achieves a good balance between accuracy, efficiency, and anomaly detection.
First DP MCMC algorithm for arbitrary models.
problem Privacy-preserving Bayesian inference in arbitrary models.
method Decomposition of Barker acceptance test for Rényi DP privacy cost.
result First general DP MCMC algorithm with improved privacy guarantees.
Adversarial configurations improve SPL classifier and implementation.
problem Predicting product acceptability in SPLs is impractical.
method Adversarial Machine Learning to create new configurations.
result Improves classifier and SPL engineering.
Estimates boundaries for acceptable bilateral gamma risk in financial markets.
problem Determining the compensation needed for risky future cash flows to be considered acceptable.
method Statistical inference from market prices and derivatives, using prospect theory.
result Upper and lower boundaries for bilateral gamma risk are estimated and tested against market data.
We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general eligible assets, including defaultable bonds. Since the payoff of these assets is not…
New test determines appropriate number of biclusters in relational data.
problem Determining the correct number of biclusters in relational data matrices.
method Proposes a new statistical test that does not require regular-grid assumptions.
result Derives asymptotic behavior of the test statistic for both null and alternative cases.
Triangulation filters spurious circuits in multilingual models.
problem Unreliable explanations of multilingual models across languages.
method Formalizes reference families and introduces triangulation as a causal acceptance rule.
result Triangulation provides a falsifiable standard for mechanistic claims.
Study controls error rates of binary classifiers using hypothesis testing.
problem Traditional binary classifiers have uncontrolled error rates.
method Combines binary classification with statistical hypothesis testing.
result Trained classifiers can be made to meet target error rate thresholds.
The study develops a test for GARCH models with unknown power.
problem Testing adequacy of asymmetric power GARCH models with unknown power.
method Derive asymptotic behavior of squared residuals autocovariances, deduce portmanteau test.
result Asymptotic results and adequacy test for GARCH models with unknown power.
New star-shaped acceptability indexes generalize existing methods.
problem Generalizing existing acceptability measures.
method Characterizing acceptability indexes through star-shaped risk measures and sets.
result Introducing concrete examples linked to various financial measures.
Private CI tests for continuous Z with privacy constraints.
problem Testing conditional independence under differential privacy constraints.
method Developed two private CI testing procedures based on generalized covariance and conditional randomization tests.
result First private CI tests with rigorous theoretical guarantees for continuous Z.
The concept of an objective spatial direction in special relativity is investigated and theories assuming light-speed isotropy while accepting the existence of a privileged spatial direction are classified. A natural generalization of the proper time principle is introduced which makes it possible to devise experimenta…
Studies acceptable bundles on a partially punctured polydisk.
problem Understanding acceptable bundles in Simpson--Mochizuki theory.
method Expository study with new arguments.
result New arguments differ from Mochizuki's.
Universal tester-learner for halfspaces over structured distributions.
problem Learning halfspaces over a wide class of structured distributions.
method Uses a fully polynomial tester-learner based on hypercontractivity and sum-of-squares (SOS) programs.
result Achieves error O(opt)+ε on any labeled distribution that the tester accepts. Study on acceptable bundles on a punctured disk.
problem Understanding acceptable bundles in Simpson--Mochizuki theory.
method Detailed study and introduction of a new invariant.
result Arguments differ from Simpson and Mochizuki's.
In this paper we build a method to optimize Multi-Year Prospective Budgets. First we present a systemic model of Local Community Finances. Then, from two acceptable Multi-Year Prospective Budgets the method implements a Genetic Algorithm to generate a collection of admissible Multi-Year Prospective Budgets among which …
Study shows Chinese stock market returns are predictable over time, especially during market turbulence.
problem Predicting returns in the Chinese stock market is challenging due to market inefficiency.
method Used wild bootstrap automatic variance ratio test and generalized spectral test.
result Return predictability varies over time, with significant predictability during market turmoils.
Simple conditions for comonotonic additive risk measures from acceptance sets.
problem Conditions for comonotonic additive risk measures from acceptance sets.
method Conditions on acceptance sets for induced comonotonic additive risk measures.
result Acceptance sets induce comonotonic additive risk measures if and only if the acceptance sets and their complements are stable under convex combinations of comonotonic random variables.
The study proposes a framework to accept OOD data based on competence scores.
problem Silent failures in Domain Generalization where models reject OOD data without proper justification.
method A learning to reject framework using proxy incompetence scores to predict trustworthiness.
result Increasing incompetence scores are predictive of reduced accuracy, but not always favorable for accuracy/rejection trade-off.
The paper develops a theory for speculative decoding acceptance criteria.
problem Speculative decoding's acceptance criteria and their rejection regions.
method Characterization of rejection regions as lower level sets of the target distribution, derivation of exact and margin-based certificates.
result Relaxed and tree-based acceptance criteria substantially enlarge the region of certified acceptance.
We revisit the Kolmogorov-Smirnov and Cramér-von Mises goodness-of-fit (GoF) tests and propose a generalisation to identically distributed, but dependent univariate random variables. We show that the dependence leads to a reduction of the "effective" number of independent observations. The generalised GoF tests are not…
New diagnostic method detects misspecified models in inverse PDE problems.
problem Misleading residual-norm diagnostics in inverse PDE problems.
method Structure-sensitive sequential diagnostic using e-processes.
result Rejects fitted models that produce biased predictions.
We introduce a new methodology for forecasting which we call Signal Diffusion Mapping. Our approach accommodates features of real world financial data which have been ignored historically in existing forecasting methodologies. Our method builds upon well-established and accepted methods from other areas of statistical …
Dual representations for systemic risk measures using acceptance sets.
problem Measuring systemic risk in financial systems.
method Developed dual representations for systemic risk measures based on acceptance sets.
result Simple and self-contained proof of dual representations for utility-based risk measures.
In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypoth…
Cactus improves auto-regressive decoding speed without sacrificing quality.
problem Accelerating auto-regressive decoding while maintaining output quality.
method Formalizes speculative sampling as constrained optimization and proposes Cactus for controlled divergence from the verifier distribution.
result Empirically validated effectiveness across various benchmarks.
New risk measures use internal resources to make positions acceptable.
problem Monetary risk measures can lead to infinite values and lack flexibility.
method Intrinsic risk measures use internal resources and a free choice of eligible assets.
result Intrinsic risk measures avoid infinite values and preserve key properties.
When assessing group solvency, an important question is to what extent intragroup transfers may be considered, as this determines to which extent diversification can be achieved. We suggest a framework to describe the families of admissible transfers that range from the free movement of capital to excluding any transac…
Proposes new deviation measures using Minkowski gauges.
problem Lack of suitable acceptance sets for deviation measures.
method Derives deviation measures through Minkowski gauges of acceptable sets.
result Any positive homogeneous deviation measure can be accommodated in the framework.
Improves algorithmic recourse to guide towards both acceptance and improvement.
problem Algorithmic recourse recommendations may not lead to improvement.
method Improvement-Focused Causal Recourse (ICR) requires recommendations to guide towards improvement and leverages causal knowledge to design accurate decision systems.
result ICR guides towards both acceptance and improvement given correct causal knowledge.
Generative Adversarial Networks simulate elevator group control without extensive data.
problem Lack of historical real-world data for system testing.
method Used GANs to generate simulation data for a multi-car elevator system.
result GANs can be used as substitutes for expensive simulation runs, but fine-tuning is needed.
Benchmarking recursive collapse claims with a new framework under false-positive control.
problem Evaluating recursive systems for failure patterns and warning claims.
method Developed Loopzero framework for testing recursive failures, specified claim boundaries in Lean, evaluated under FP constraint, and compared with standard detectors.
result No standard detectors or Loopzero's pre-registered quantile detector achieved the required operating point under the false-positive contract.
The paper studies optimal investment using acceptability indices to maximize portfolio performance.
problem Optimal investment problem using coherent acceptability indices.
method Numerical algorithm approximating the original problem, dynamic coherent risk measures, set-valued Bellman's principle.
result Acceptability maximization problem reduces to a one-period problem under certain conditions.
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.
New method allows reparameterization for acceptance-rejection sampling.
problem Applying reparameterization trick to complex distributions.
method Proposes a new method for reparameterization gradients with acceptance-rejection sampling.
result Significantly lower variance in gradient estimator.
A novel ML verification technique using manifold learning.
problem Ensuring trust in machine learning systems.
method Variational autoencoder for extracting a low-dimensional manifold from high-dimensional training data.
result The manifold provides diverse test data, fault-revealing test cases, and independent runtime trust assessment.
New property of Kendall correlation tested for stock markets.
problem Adequacy of elliptical model for stock returns distribution.
method Proved new property, constructed tests, applied Holm procedure.
result Elliptical model rejected for Chinese stock market but accepted for others.
New index for evaluating cash flow processes over a fixed horizon.
problem Evaluating performance of cash flow processes over a fixed investment horizon.
method Extended acceptability indices to càdlàg processes, providing a new index based on Average Value-at-Risk and running minimum.
result Suggested index represents a RAROC-type model for performance evaluation.
New method assesses financial risk using model testing.
problem Assessing risk in financial positions with uncertain pricing rules.
method Interpreting quasiconvex duality in Knightian uncertainty, using a basket of derivatives to test pricing models.
result Introduced Value&Risk measures to assess additional capital needed for financial positions.
Study reveals bias in machine learning conference reviews.
problem Bias in machine learning conference review process.
method Comprehensive analysis of ICLR papers from 2017-2020.
result Strong institutional bias in accept/reject decisions.
Interpretable machine learning faces acceptance challenges.
problem Acceptance issues in interpretable machine learning.
method Analyzing stakeholder concerns and desires.
result Need deeper consideration of stakeholders' needs.
Introduces Star-Shaped deviation measures for risk analysis.
problem Risk measurement and analysis in finance.
method Characterizes Star-Shaped deviation measures through acceptance sets and convex deviation measures.
result Exposes the relationship between Star-Shaped risk measures and deviation measures.
We consider a trader who wants to direct his portfolio towards a set of acceptable wealths given by a convex risk measure. We propose a black-box algorithm, whose inputs are the joint law of stock prices and the convex risk measure, and whose outputs are the numerical values of initial capital requirement and the funct…
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
problem Predicting and optimizing gig worker acceptance in labor markets.
method Preisach hysteresis model applied to neural network and XGBoost classifier for binary transaction outcomes.
result Model reduces total wage bill by 21.3% and increases expected fill rate by 9.7 pp.