arXiv research

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

DRO optimizes decisions under uncertain distributions, considering worst-case scenarios.

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

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

Novel algorithms for online learning with uncertain feedback graphs reduce regret.

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 …

Optimizes decision-making with uncertain variables using auxiliary observations.

Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.

In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…

Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.

Study examines strategic exit timing in uncertain competition.

The paper tackles uncertainty in multi-objective decision-making.

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep r…

MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.

The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. O…

Novel framework for risk-sensitive reinforcement learning using martingale decomposition.

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

A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.

New algorithm solves uncertain Markov decision processes using Wasserstein uncertainty.

This study investigates how Decision-Focused Learning improves stock return predictions for better portfolio optimization.

Study on learning strategies in matching markets with uncertain preferences.

A new method for decision-focused learning reduces computational cost.

Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.

TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.

Develops new methods for risk-aware decision-making in medical bandits.

Paper proposes efficient method for estimating risk measures in complex models.

Existing uncertainty modeling approaches try to detect an out-of-distribution point from the in-distribution dataset. We extend this argument to detect finer-grained uncertainty that distinguishes between (a). certain points, (b). uncertain points but within the data distribution, and (c). out-of-distribution points. O…

Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.

Diffusion approximations optimize sequential experimentation for uncertain parameters.

Real-world problems typically require the simultaneous optimization of several, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization. …

Integrates estimation and optimization for uncertain parameters.

Optimization is becoming increasingly common in scientific and engineering domains. Oftentimes, these problems involve various levels of stochasticity or uncertainty in generating proposed solutions. Therefore, optimization in these scenarios must consider this stochasticity to properly guide the design of future exper…

Optimal fund deployment strategy under uncertain deal arrivals.

In this paper, we present a unified framework for decision making under uncertainty. Our framework is based on the composite of two risk measures, where the inner risk measure accounts for the risk of decision given the exact distribution of uncertain model parameters, and the outer risk measure quantifies the risk tha…

The paper tackles robust design selection for online experiments under uncertain interference mechanisms.

New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.

A new RL model ensures safe learning in uncertain environments.

New method optimizes decision-making in uncertain environments.

In this paper we consider stochastic optimization problems for an ambiguity averse decision maker who is uncertain about the parameters of the underlying process. In a first part we consider problems of optimal stopping under drift ambiguity for one-dimensional diffusion processes. Analogously to the case of ordinary o…

Framework for responsible LLM deployment with human involvement and decentralized technologies.

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

New method calculates Shapley values for uncertain functions.

A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.

Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cy…

Nowadays, the use of machine learning models is becoming a utility in many applications. Companies deliver pre-trained models encapsulated as application programming interfaces (APIs) that developers combine with third party components and their own models and data to create complex data products to solve specific prob…

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…

The Surprise index assesses autonomous systems' competency in uncertain environments.