Train policies in simulators with varying ambiguity to maintain robustness over time.
problem Robust control policies optimized in simulators perform poorly in real systems due to parameter uncertainty.
method Train policies in simulators where ambiguity varies with the system's state, ensuring a stationary filter process over latent state.
result Policies trained under stationary ambiguity preserve robustness to latent factors over time, leading to strong performance on real market data.
New definition resolves ambiguity in non-stationary bandit classification.
problem Ambiguity in classifying non-stationary bandits using existing definitions.
method Introducing a formal definition that resolves ambiguity and provides a unified approach.
result Unified approach applicable to both Bayesian and frequentist formulations, resolves classification issues.
Bayesian convolutional deep sets improve ambiguity in stationary process modeling.
problem Ambiguity in translation equivariant functional representations due to insufficient data points.
method Introduce Bayesian convolutional deep sets with task-dependent stationary prior.
result Improves representation quality compared to kernel smoother and non-parametric models.
Study non-rectangular robust MDPs for average-reward, finding optimal policies and transient values.
problem Non-rectangular robust Markov decision processes under average-reward criterion.
method Proves history-dependent policies are robust-optimal, introduces transient-value framework, constructs epoch-based policy.
result Existence and properties of robust optimal policies, transient value bounds.
Investigates how Knightian uncertainty affects timing decisions in multidimensional stochastic models.
problem Impact of Knightian uncertainty on optimal timing decisions in multidimensional stochastic models.
method Characterizes the value and worst-case measure of optimal timing policies using excessive functions and supermartingales.
result Knightian uncertainty can accelerate timing but also lead to stationarity in non-stationary models.
Study improves adversarial classification using distributionally robust models.
problem Improving robustness against adversarial attacks in classification models.
method Distributionally robust chance constraints with Wasserstein ambiguity, reformulated as a regularized ramp loss minimization problem.
result Standard descent methods can converge to the global minimizer for the distributionally robust adversarial classification model.
New estimator robust to adversarial noise and data heterogeneity.
problem Sensitive to adversarial noise and poor performance with heterogeneous data.
method Distributionally robust estimator minimizing worst-case conditional expected loss over adversarial distributions.
result Efficiently finds non-parametric local estimates via convex optimization.
Model-free preference under ambiguity defined and applied.
problem Understanding and quantifying ambiguity aversion and prudence.
method Introduces a new model-free definition of ambiguity attitudes and applies it in various contexts.
result New definition of ambiguity prudence equivalent to specific mathematical functions.
Investment strategy optimized for ambiguity and interest rate risk.
problem Dynamic asset allocation with interest rate risk and ambiguity.
method Closed-form solution for optimal investment strategy.
result Ambiguity affects speculative motives, not hedging of interest rate risk.
Study optimal switching under ambiguity in finance.
problem Optimal switching problems under ambiguity in finance.
method Use multidimensional reflected backward stochastic differential equations (RBSDEs) to characterize the optimal switching.
result Value function of optimal switching under ambiguity coincides with solutions to multidimensional RBSDEs with negative switching costs.
New approach for optimal stopping under model ambiguity, considering agent's attitude towards ambiguity.
problem Optimal stopping under model ambiguity and varying levels of ambiguity aversion.
method Introduces a time-inconsistent stopping problem with an α-maxmin nonlinear expectation and seeks subgame perfect equilibrium policies through fixed-point iterations. result Equilibrium stopping policies can be obtained through fixed-point iteration and vary based on an agent's ambiguity attitude.
Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.
problem Nonconcave portfolio choice under smooth ambiguity and Bayesian learning.
method Developed a general framework for dynamic, non-concave asset allocation.
result Dynamic consistency achieved through a robust representation.
A framework for robust exploration in reinforcement learning under ambiguity.
problem Optimal stopping under ambiguity in reinforcement learning.
method Continuous-time robust reinforcement learning framework using g-expectation and backward stochastic differential equations. result Constructs a robust exploratory stopping time approximating the optimal stopping time under ambiguity.
Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.
problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.
New formulations capture aversion to ambiguity about volatility.
problem Capturing aversion to ambiguity about unknown and time-varying volatility.
method Introduces novel preference formulations and compares them with existing models.
result Illustrates the impact of ambiguity aversion in static and dynamic models.
Study inert and ambiguous classes in modular group using combinatorial methods.
problem Counting inert and ambiguous conjugacy classes in modular group.
method Purely combinatorial approach using word length in free product representation.
result Exact counting formulas and asymptotic growth rates for inert and ambiguous classes.
New method optimizes ambiguity sets for robust MDPs, improving policy robustness.
problem Sensitivity of optimal policies to model misspecification in MDPs.
method Proposes a new approach to optimize the shape of ambiguity sets, departing from norm-bounded uniform and symmetric sets.
result Empirical results show practical promise of weighted near-optimal ambiguity sets.
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
problem Ambiguous data and cognitive biases impact the interpretability of machine learning models in humanitarian decision making.
method The study will explore the effects of data ambiguity and cognitive biases on machine learning algorithms in humanitarian contexts.
result The research aims to uncover the specific ways in which ambiguous data and cognitive biases influence the interpretability of machine learning models in humanitarian decision making.
Investment strategy in ambiguous financial markets with learning
problem Continuous time investment problem in multi-asset Black-Scholes market with model ambiguity
method Optimal dynamic investment strategy within the class of all adapted strategies which allow for learning
result Ambiguity averse investors invest less in risky assets
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 …
Model cash management under ambiguity using maxmin preferences and diffusion.
problem Optimizing cash reserves in the presence of ambiguity.
method Singular control model with maxmin preferences, verified using Dynkin games.
result Higher expected costs and narrower inaction region under increased ambiguity.
Study shows ambiguity affects optimal timing in a two-dimensional model.
problem Understanding how ambiguity influences optimal timing in a two-dimensional setting.
method Analyzes a two-dimensional optimal stopping problem with ambiguity in a multifactor model.
result Ambiguity affects the rate at which the problem is discounted, not just the growth rate of underlying processes.
Paper investigates Lambda Value-at-Risk under ambiguity and risk sharing.
problem Investigates Lambda Value-at-Risk under ambiguity and risk sharing.
method Establishes equivalence of robust ΛVaR and traditional ΛVaR under ambiguity sets, analyzes properties, derives explicit formulas, and explores risk sharing. result Unified and extended the concept of Value-at-Risk under ambiguity, derived explicit formulas for specific ambiguity sets, and explored risk sharing.
The paper explores continuous inverse ambiguous functions on various Lie groups.
problem Existence of continuous inverse ambiguous functions on Lie groups.
method Investigation of continuous inverse ambiguous functions on specific Lie groups.
result Existence of continuous inverse ambiguous functions on various Lie groups.
Improves DRO with Bayesian Ambiguity Sets for model misspecification.
problem Overly conservative decisions due to misspecified models in DRO.
method Introduces DRO-RoBAS with robust posterior predictive distribution.
result Outperforms other Bayesian and empirical DRO approaches in out-of-sample performance.
Study optimal timing to divest from assets with uncertain future scenarios.
problem Optimal timing to divest from assets with uncertain future scenarios.
method Smooth model of decision making under ambiguity aversion, optimal stopping problem with learning.
result Proves a minimax result reducing the problem to standard optimal stopping problems with learning.
New method improves PCA robustness using Wasserstein distances.
problem Uncertainty in probability distribution affects PCA robustness.
method Distributionally robust optimization with Wasserstein distances.
result Explicit reformulation leads to efficient smoothing algorithm.
New method improves robust MDP solutions without confidence regions.
problem Computing policies with provable worst-case guarantees in reinforcement learning.
method Optimizes ambiguity sets using Bayesian inference to achieve better solutions.
result Achieves better solutions with the same robustness guarantees.
RSVF improves robust MDPs by relaxing ambiguity set constraints.
problem Computing robust policies with provable worst-case guarantees in uncertain environments.
method RSVF uses a Bayesian prior to optimize ambiguity set size and location, relaxing the requirement that the set be a confidence interval.
result RSVF achieves less conservative solutions with the same worst-case guarantees.
New classifiers handle ambiguous labels with set-valued outputs, balancing confidence and ambiguity.
problem Handling ambiguous labels in classification tasks.
method Developed a framework for multiclass set-valued classification with oracle and estimator approaches.
result Optimal classifiers can output empty sets, but solutions are provided for practical needs.
Study optimal liquidation under price impact ambiguity.
problem Optimal liquidation under uncertainty about price impact parameters.
method Characterization of value function and optimal strategy via semi-linear PDE.
result Increased liquidation rates due to ambiguity aversion.
This paper compares average-K and top-K classification methods under ambiguity.
problem Choosing a single label in ambiguous cases leads to low precision.
method Formally characterizes ambiguity profiles and compares average-K and top-K classification methods.
result Average-K can achieve lower error rates than top-K in some ambiguous cases.
Proposes handling ambiguity in sequential data predictions.
problem Handling uncertainty in sequential data predictions.
method Extension of MHP model to recurrent architectures, introducing a novel metric.
result Achieved promising results on various sequential data tasks.
This paper compares different DRO formulations for pension fund management.
problem Navigating uncertainty in asset liability management for pension funds.
method Three DRO formulations: mixture, box, and Wasserstein ambiguity sets.
result Wasserstein and box ambiguity sets outperform traditional approaches in fund performance.
Adapts AUM to identify ambiguous tasks in crowdsourced learning, improving generalization.
problem Discerning ambiguous tasks in crowdsourced labels to prevent mislabeling.
method Introduces Weighted Areas Under the Margin (WAUM) to average AUMs weighted by task-specific scores.
result Improves generalization performance by discarding ambiguous tasks.
New risk measures for quantiles under ambiguity improve risk sharing.
problem Risk optimization under ambiguity using quantiles.
method Introducing Choquet quantiles and Choquet Expected Shortfall.
result Optimal allocations for quantile agents under ambiguity.
Paper tackles ambiguity in relevance labels for neural list-wise learning to rank.
problem Ambiguity in relevance labels in learning to rank data.
method Proposes a novel sampling technique for computing a list-wise loss.
result Significantly outperforms other methods on validation and test sets.
Researchers resolved ambiguities in gravitational radiation charges.
problem Ambiguities in charges related to gravitational radiation.
method Addressed supertranslation ambiguities in classical and extended BMS algebras.
result Proposed and proved an invariant charge free from supertranslation ambiguity.
Paper tackles robust control of SDEs with ambiguity, proving value function existence and applying to investment problems.
problem Robust control of SDEs with ambiguity parameters and non-Lipschitz coefficients.
method Existence and uniqueness of value function established through BSDEs with non-linear growth conditions.
result Existence and uniqueness of value function in proper space, verified through BSDEs.
Quantum framework explains human choices in ambiguity and risk.
problem Testing human choices in ambiguity and risk.
method Quantum-theoretic framework for decision-making under uncertainty.
result Quantum framework faithfully models human choices in ambiguity and risk.
A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.
problem Optimizing dividends for a firm with heterogeneous shareholders under ambiguity aggregation.
method Characterizing equilibrium dividends using a partition of the state space.
result Time-homogeneous equilibrium dividend law characterized by a partition of the state space.
Study examines insurance demand under ambiguity aversion.
problem Demand for insurance indemnification under ambiguity aversion.
method Characterizes optimal indemnity functions using Maxmin-Expected Utility model.
result Optimal indemnity functions involve full insurance on low-probability events.
Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.
problem Investment in financial markets with unknown drift coefficients.
method Optimization under KMM approach, considering risk and ambiguity preferences.
result Optimal investment strategy can be adjusted based on prior drift distribution.
We introduce a measure to quantify ambiguity in deep learning models, improving their reliability.
problem Deep learning models make mistakes on seemingly trivial cases and fail in recognizing what they don't know.
method We define ambiguity based on decision boundaries and convex hulls in feature space, developing a theoretical framework to identify unknowns.
result A single ambiguity measure can detect a significant portion of model mistakes, including adversarial and out-of-distribution inputs.
Dynamic pricing model considers ambiguity in endowment growth rate.
problem Dynamic asset pricing under ambiguous endowment growth rate.
method α-maxmin expected utility model for ambiguity, intra-personal equilibrium strategies, market equilibrium.
result Asset prices reflect ambiguity in endowment growth rate.
Investment strategy without fixed horizon in ambiguous market conditions.
problem Dynamic portfolio choice in an ambiguous market.
method Formulated as a robust forward performance process reflecting dynamic investor preference.
result Market risk premium and utility risk premium determine trading direction and worst-case scenarios.
New theory extends rank-dependent utility for risk and ambiguity.
problem Modeling decision-making under risk and ambiguity.
method Axiomatizes a new preference relation with ambiguity index, probability weighting, and utility function.
result Extends rank-dependent utility to risk and ambiguity, reducing to existing models under specific conditions.
New method simplifies analysis of exercise timing for ambiguous integral option contracts.
problem Impact of ambiguity on optimal exercise timing of integral option contracts.
method Parameterized family of excessive functions generating supermartingales, simplifying multidimensional problem to one-dimensional static optimization.
result Value of optimal policy and worst case measure expressed in terms of these processes.