arXiv research

Noise can stabilize systemic risk models with uncertain robustness.

New method recovers signals from noisy indirect data, even when noise is uncertain.

This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

Framework for robust control in cooperative systems with uncertain common noise.

We study an optimal execution problem with uncertain market impact to derive a more realistic market model. We construct a discrete-time model as a value function for optimal execution. Market impact is formulated as the product of a deterministic part increasing with execution volume and a positive stochastic noise pa…

AIRBO optimizes robustly under uncertain inputs.

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In …

Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…

Unified framework denoises data and abstains from uncertain predictions.

Safe learning in uncertain systems with state measurements and optimization.

Proposes DenoiseBid to correct uncertain CTR and CVR estimates for autobidding.

New BO method optimizes multiple objectives under input noise.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) D…

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…

Optimizes trading large volumes of volatile assets with fast mean-reverting volatility.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Marginalising out uncertain quantities within the internal representations or parameters of neural networks is of central importance for a wide range of learning techniques, such as empirical, variational or full Bayesian methods. We set out to generalise fast dropout (Wang & Manning, 2013) to cover a wider variety of …

We propose a feed-forward inference method applicable to belief and neural networks. In a belief network, the method estimates an approximate factorized posterior of all hidden units given the input. In neural networks the method propagates uncertainty of the input through all the layers. In neural networks with inject…

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

New algorithm for uncertain time series classification.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

New analysis reveals gaps in selective classifiers, guiding improvements.

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

Agent learns to navigate uncertain 3D maps using a hybrid planner.

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.

Model quantifies uncertainty's impact on European option prices.

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of…

The paper tackles robust control with uncertain dependence using data-driven methods.

Paper proposes online optimization for uncertain systems using machine learning and DRO.

This paper tackles JSSP with uncertain task durations using DRL.

Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture of uncertainty. We therefore use principled Bayesian methods to capture…

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

This paper improves learning uncertain Bayesian networks from incomplete data.

New RL agent learns sparse rewards efficiently.

Proposes a new uncertain volatility model with worst-case scenario analysis.

In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between …

UNREAL selectively ensembles distinct models to improve active learning performance.

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.