arXiv research

Estimates parameters in a deviated Gaussian mixture model.

The paper combines Bitcoin price models with expert corrections for better predictions.

Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the leas…

New clustering method improves climate data analysis in Lesser Antilles.

uMoE trains NNs with uncertain data by embedding uncertainty into training.

A new CoVaR framework integrates expert views using entropy pooling.

Model explains capital allocation and wealth distribution dynamics in a frictional economy.

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assum…

Paper explores how black box models can deviate from average performance.

TAME learns new tasks without knowing them, outperforming existing methods.

This paper creates a tagging system for paintings using historical descriptions.

A new offline RL framework unifies imitation learning and vanilla offline RL.

Study optimal investment with herd behavior using rational decision decomposition.

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…

Introduces Star-Shaped deviation measures for risk analysis.

Detects project management anti-patterns using code and issue data.

Paper characterizes monotonic mean-deviation risk measures.

Multi-expert L2D underfits more severely, requiring new methods.

We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.

Paper proves large deviation principle for stochastic approximations.

In this paper we propose the notion of dynamic deviation measure, as a dynamic time-consistent extension of the (static) notion of deviation measure. To achieve time-consistency we require that a dynamic deviation measures satisfies a generalised conditional variance formula. We show that, under a domination condition,…

TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.

A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.

Study large deviations in life insurance portfolios without identical distributions.

New estimate reduces overfitting risk in machine learning models.

A Human-in-the-Loop Bayesian Optimization framework for constraint-aware bioprocess development.

Study large deviations for hypoelliptic diffusion on sub-Riemannian manifolds.

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…

Proposes new deviation measures using Minkowski gauges.

In this paper we analyze a dynamic recursive extension of the (static) notion of a deviation measure and its properties. We study distribution invariant deviation measures and show that the only dynamic deviation measure which is law invariant and recursive is the variance. We also solve the problem of optimal risk-sha…

Improved time series forecasting with expert loss integration.

We provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications, and for related integrated functionals. Suitable scaling allows us to transfer these results into small-time, large-time and tail asymptotics for diffusions, as well as for option prices and realised vari…

Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the wei…

HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.

NAMEx merges experts using Nash bargaining for improved performance.

Connections between Lie derivatives and the deviation equation has been investigated in spaces with affine connection. The deviation equations of the geodesics as well as deviation equations of non-geodesics trajectories have been obtained on this base. This is done via imposing certain conditions on the Lie derivative…

Unified approach to stochastic Volterra systems' deviations.

Study large deviations in random walks on Lie groups.

Expert augmentation improves hybrid model generalization.

Deviation inequalities and limit laws for random walks on metric spaces.

Let $M$ be a smooth manifold and $\mathcal{S}$ a semi-spray defined on a sub-bundle $\mathcal{C}$ of the tangent bundle $TM$. In this work it is proved that the only non-trivial $k$-jet approximation to the exact geodesic deviation equation of $\mathcal{S}$, linear on the deviation functions and invariant under an spec…

New method calibrates Gaussian product experts for better predictions.

Meta-algorithm optimizes nonstochastic bandits with infinitely many experts.

Optimizes variance reduction in Heston model using large and moderate deviations.

New algorithm reduces expert prediction regret for two experts.

The article improves prediction by aggregating Kalman recursions online.

System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.

Improved regret bounds for bandits with fixed expert advice using information theory.