arXiv research

This paper tackles over-certainty in test-time adaptation models, proposing a solution to improve calibration.

Study risk-sensitive market making with entropy regularization for better quote control.

A novel approach for semi-supervised learning using regularized optimal transport.

New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.

Study on implied certainty equivalent rates in financial markets and electric vehicles.

Scores measure certainty and doubt in classification predictions.

Study shows certainty equivalent policy minimizes regret in continuous-time systems.

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the cl…

The paper develops a new approach to conditional risk measures using modular convex analysis.

We consider a model in which a trader aims to maximize expected risk-adjusted profit while trading a single security. In our model, each price change is a linear combination of observed factors, impact resulting from the trader's current and prior activity, and unpredictable random effects. The trader must learn coeffi…

Solves Merton's investment-consumption problem with certainty equivalent approach.

Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.

For incomplete preference relations that are represented by multiple priors and/or multiple -- possibly multivariate -- utility functions, we define a certainty equivalent as well as the utility buy and sell prices and indifference price bounds as set-valued functions of the claim. Furthermore, we motivate and introduc…

Paper tackles anomaly detection in restless Markov arms with unknown TPMs.

Deep learning solves dynamic programming with recursive utility.

The paper analyzes risk estimation methods and derives bounds for OCE risk.

Introduces new performance measures using scaled utility functions.

Study scaling limits for option pricing in trinomial models.

CEFOL uses deep learning for dynamic programming with recursive utility.

Proposes a new method to rank risky investments based on Omega measure.

We consider the class of risk measures associated with optimized certainty equivalents. This class includes several popular examples, such as CV@R and monotone mean-variance. Numerical schemes are developed for the computation of these risk measures using Fourier transform methods. This leads, in particular, to a very …

Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying one specific ensemble method. However, very few works have explored complex fusion schemes using het-erogeneous ensembles with new aggregatio…

New multivariate risk measures improve on univariate OCE methods.

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ) control problems with unknown transition dynamics. We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system …

Study risk-sensitive reinforcement learning with optimized certainty equivalents.

This work uses a scalable approach to identify partially observed nonlinear systems.

In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approache…

This study provides an independent, outside-in estimate of the cost and schedule risks of nuclear waste storage projects. Based on a reference class of 216 past, comparable projects, risk of cost overrun was found to be 202% or less, with 80% certainty, i.e., 20% risk of an overrun above 202%. Based on a reference clas…

Study optimal investment decisions for diverse risk-tolerant agents.

We consider the problem of optimal risk sharing in a pool of cooperative agents. We analyze the asymptotic behavior of the certainty equivalents and risk premia associated with the Pareto optimal risk sharing contract as the pool expands. We first study this problem under expected utility preferences with an objectivel…

A framework for cost of belief revision in uncertain agents.

This paper introduces new risk measures for evaluating losses with varying time horizons.

The paper develops robust risk measures for uncertain loss positions.

This paper studies the optimal risk-averse timing to sell a risky asset. The investor's risk preference is described by the exponential, power, or log utility. Two stochastic models are considered for the asset price -- the geometric Brownian motion and exponential Ornstein-Uhlenbeck models -- to account for, respectiv…

Improves policies with high certainty, even in small samples.

Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase lo…

Submodularity is studied for convex risk measures, including Expected Shortfall.

New bounds for adaptive control in high dimensions without fixed state space.

Study of gamma-hedging using rough paths for European and exotic options.

Optimized certainty equivalents (OCEs) is a family of risk measures widely used by both practitioners and academics. This is mostly due to its tractability and the fact that it encompasses important examples, including entropic risk measures and average value at risk. In this work we consider stochastic optimal control…

We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention.…

New methods reduce constraint violations to certainty in stochastic optimization.

This paper tackles adaptive control of unknown Markov jump systems with sample complexity and regret bounds.

This work introduces a new probabilistic process for regularization in machine learning.

Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including t…

We study the dynamic indifference pricing with ambiguity preferences. For this, we introduce the dynamic expected utility with ambiguity via the nonlinear expectation--G-expectation, introduced by Peng (2007). We also study the risk aversion and certainty equivalent for the agents with ambiguity. We obtain the dynamic …

Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.

This paper shows CEM is a special case of TTM, leading to new proofs and improved sample complexity bounds.