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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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67134201268 · May 202619922001200920172026
48 results for Liu's credibility theory

Paper solves portfolio optimization with fuzzy risk and credibility theory.

problem Optimizing investment in risky assets with fuzzy risk and credibility theory.
method Formulated as an optimization problem with credibilistic expected utility. Derived formulas for optimal allocation using various moments and utility function parameters.
result Different formulas for optimal allocation of risky assets are derived, considering fuzzy risk and utility function parameters.

In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…

2009-09-15abs ↗pdf ↗

Credibility theory provides tools to obtain better estimates by combining individual data with sample information. We apply the Credibility theory to a Uniform distribution that is used in testing the reliability of forecasting an interest rate for long term horizons. Such empirical exercise is asked by Regulators (CRR…

2014-09-17abs ↗pdf ↗

Paper introduces exact credible sets for classification problems.

problem No general way to construct exact credible sets for classification.
method Generalized credible set with connection to Neyman--Pearson lemma and randomized decision rule.
result Achieves any preassigned credible level for classification problems.

We present a proof of Milnor conjecture in dimension 3 based on Cheeger-Colding theory on limit spaces of manifolds with Ricci curvature bounded below. It is different from [Liu] that relies on minimal surface theory.

2017-03-23abs ↗pdf ↗

The paper proposes a method to assess survey data credibility without needing many samples, regardless of data dimension.

problem Assessing the credibility of survey data across different dimensions.
method Task-based approach and model-specific distance metric for verifying survey data credibility in regression models.
result The sample complexity of the proposed algorithm is independent of the data dimension, making it more efficient.

CP4SBI improves the calibration of credible sets in SBI models.

problem Inaccurate credible sets in SBI models lead to underestimation of true parameters.
method Develops a local conformal calibration framework for SBI models.
result Improves the quality of uncertainty quantification for neural posterior estimators.

CREX makes deep neural networks more credible by focusing on relevant evidence.

problem Deep neural networks often use incorrect evidence for decisions, leading to mistrust and poor generalization.
method CREX regularizes DNN training with rationales to encourage correct local explanations.
result DNNs trained with CREX are more credible and perform better on unseen data.

In many settings, it is important that a model be capable of providing reasons for its predictions (i.e., the model must be interpretable). However, the model's reasoning may not conform with well-established knowledge. In such cases, while interpretable, the model lacks \textit{credibility}. In this work, we formally …

2017-11-08abs ↗pdf ↗

New auction design uses statistical learning to reduce costs and improve fairness.

problem Designing efficient multi-item auctions with reduced implementation costs and fairness.
method Nonparametric density estimation for credible intervals, two new strategies.
result Strategies consistently outperform alternative methods in revenue maximization and cost reduction.

Mannheim partner curves are studied by Liu and Wang [1,2]. Orbay and others extended the theory of the Mannheim curves to the ruled surface in Euclidean 3-space[3]. We obtain the relationships between the curvatures and the torsions of the dual Mannheim partner curves with respect to each other.

2010-01-26abs ↗pdf ↗

The Kashaev invariants of 3-manifolds are based on 6j6j-symbols from the representation theory of the Weyl algebra, a Hopf algebra corresponding to the Borel subalgebra of $U_q(sl(2,\C))$. In this paper, we show that Kashaev's 6j6j-symbols are intertwining operators of local representations of quantum Teichmüller space…

2007-06-14abs ↗pdf ↗

BCPO optimizes offline RL policies by converting uncertainty into conservative bounds.

problem Offline RL's fragility under distribution shifts and model errors.
method Bayesian approach with credible lower bounds and KL regularization.
result BCPO yields an uncertainty-calibrated policy that avoids exploiting model errors.

We use refined spectral sequence arguments to calculate known and previously unknown bi-Hamiltonian cohomology groups, which govern the deformation theory of semi-simple bi-Hamiltonian pencils of hydrodynamic type with one independent and \( N\) dependent variables. In particular, we rederive the result of Dubrovin-Liu…

2016-11-28abs ↗pdf ↗

Develops a new method for sampling from Bayesian credible sets using deep generative quantile learning.

problem Sampling from posterior distributions in high-dimensional spaces with intractable likelihoods.
method Uses deep neural networks to implicitly sample from Bayesian credible sets via a push-forward mapping and Monge-Kantorovich depth.
result Demonstrates improved performance and theoretical consistency of the quantile learning framework.

The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.

problem Analyzing uncertainty in sparse Gaussian process regression with a Brownian motion prior.
method Theoretical guarantees and limitations for pointwise credible sets are derived for a rescaled Brownian motion prior with a sparse variational Gaussian process method.
result Theoretical characterization of asymptotic frequentist coverage for credible sets, distinguishing conservative and overconfident cases.

Bayesian approach improves uncertainty in deep learning models.

problem Uncertainty quantification in deep learning models.
method Bayesian point of view, Gaussian approximability, semi-parametric Bernstein-von Mises theorems.
result Bayesian credible regions have valid frequentist coverage, providing theoretical justification for deep learning.

In this short note, we compute the Betti numbers of the moduli stack of flat SU(3)-bundles over a Klein bottle. We also handle the general compact group case over RP^2. In all cases the cohomology is found to be equivariantly formal, supporting a conjecture from the author's doctoral thesis. Our results also verify con…

2009-01-12abs ↗pdf ↗

The paper tests the credibility of public and private surveys using linear regression and differential privacy.

problem Ensuring the validity of data analysis results from sample surveys using linear regression.
method Designing an algorithm to test the credibility of surveys and extending it to handle LDP.
result The algorithm achieves optimal estimation error bound for 1\ell_1 linear regression and reduces sample complexity.

New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.

problem No method for uncertainty decomposition in TabPFN.
method Casted as a Bayesian predictive inference problem, derived variance estimators using predictive CLT.
result Fast to compute credible bands that target epistemic uncertainty and achieve near-nominal frequentist coverage.

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworth…

2017-05-06abs ↗pdf ↗

BIGUE algorithm provides credible intervals for hyperbolic network embeddings.

problem Uncertainty in hyperbolic network embeddings.
method Markov chain Monte Carlo (MCMC) algorithm for Bayesian hyperbolic random graph model.
result Samples from the posterior distribution provide credible intervals for hyperbolic coordinates and network properties.

In this paper we prove that for Gromov-Witten theory of P1P^1 orbifolds of ADE type the genus-2 G-function introduced by B. Dubrovin, S. Liu, and Y. Zhang vanishes. Together with our results in [LW], this completely solves the main conjecture in their paper [DLZ]. In the process, we also found a sufficient condition fo…

2014-02-16abs ↗pdf ↗

Maximum likelihood is the most widely used statistical estimation technique. Recent work by the authors introduced a general methodology for the construction of estimators for functionals in parametric models, and demonstrated improvements - both in theory and in practice - over the maximum likelihood estimator (MLE), …

2014-09-26abs ↗pdf ↗

The paper proves a relation between four types of invariants.

problem Proving a precise relation between four types of invariants.
method Analyzing pseudo-Anosov homeomorphisms and cusped hyperbolic 3-manifolds at roots of unity.
result A precise relation between the Baseilhac-Benedetti invariants and the Bonahon-Liu-Wong-Yang invariants.