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.
The paper uses EVT to improve tail risk measures under ambiguity sets.
problem Misspecification of tail risk measures leads to inflated risk estimates.
method Applies Extreme Value Theory to derive worst-case tail risk under ambiguity sets.
result Proposes a tail-calibrated ambiguity design that preserves nominal tail asymptotic scaling.
This paper solves a financial portfolio selection problem in incomplete markets.
problem Portfolio selection in incomplete financial markets with ambiguity.
method Constructing an efficient frontier, simplifying the problem, introducing a new distorted Legendre transformation, and proving the bipolar relation and distorted duality theorem.
result The existence and uniqueness of optimal strategies are shown for different utility functions under specific conditions.
Paper proposes monotonous NMF to handle scale and ordering ambiguities in nonnegative matrix factorization.
problem Scale and ordering ambiguities in nonnegative matrix factorization.
method Alternating least-squares algorithm for monotonous NMF, relaxed mixing matrix assumption.
result Monotonous NMF estimates monotonous signals better than standard NMF when sources are monotonous.
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.
Agents learn state ambiguity from non-linear sensor data using Gaussian approximations.
problem Learning state representation from non-linear sensor data.
method Second-order Taylor approximation of Gaussian distribution for non-linear measurement functions.
result Induces a preference for states based on inferability from observations.
Characterizes preferences for decision-making under uncertainty using a leader-follower game model.
problem Decision-making under uncertainty and ambiguity aversion.
method Characterizes niveloidal preferences through a leader-follower game model, satisfying specific axioms.
result The leader's strategy space can serve as an ambiguity aversion index.
Expands newsvendor model with moment constraints using Wasserstein distance.
problem Optimizing order quantity under distributional ambiguity.
method Formulates infinite dimensional primal problem, derives finite dimensional dual problem using problem of moments duality.
result Distributional ambiguity affects optimal order quantity and profits/costs.
New method calibrates ambiguity sets for robust decision-making under contamination.
problem Minimizing worst-case expected loss over distributional shifts in out-of-sample environments.
method Bulk-calibrated credal ambiguity sets that learn a high-mass bulk set from data and bound tail contributions.
result Closed-form, finite robust objective and tractable optimization for various losses and geometries.
We generalize the Lagrangian-Hamiltonian formalism of Skinner and Rusk to higher order field theories on fiber bundles. As a byproduct we solve the long standing problem of defining, in a coordinate free manner, a Hamiltonian formalism for higher order Lagrangian field theories. Namely, our formalism does only depend o…
Speech emotion recognition improved with simpler machine learning models.
problem Identifying emotions from speech is ambiguous and challenging.
method Feature-engineering approach using hand-crafted audio features and text features. Comparison of traditional machine learning and deep learning models.
result Lighter machine learning models outperform deep learning models for emotion recognition.
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.
Study dynamic pricing with ambiguity using G-expectation.
problem Dynamic pricing under ambiguity preferences.
method Introduce dynamic expected utility with ambiguity via G-expectation.
result Obtain dynamic consistency of indifference pricing.
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.
Optimal insurance strategy for maximizing RDEU under various premium principles.
problem Maximizing a risk-averse individual's RDEU with insurance priced by a distortion-deviation principle.
method Proved necessary and sufficient conditions for the optimal solution, considered ambiguity orders, and analyzed specific examples.
result Conditions for no insurance or deductible insurance to be optimal.
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.
Deep RL model learns 2.5D fighting games with height ambiguity.
problem Ambiguity in character height/depth and sequential action orders in 2.5D fighting games.
method Modified A3C network with Recurrent Info network for combo skill observation.
result Successfully learned and played Little Fighter 2 (LF2) 2.5D fighting game.
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.
Paper develops online learning-based risk-averse MPC for uncertain systems.
problem Designing robust MPC for systems with unknown but inferable stochastic disturbances.
method Proposes a novel online learning framework using CVaR constraints and Dirichlet process mixture models.
result Demonstrates improved robustness and adaptability of MPC in handling time-varying disturbance distributions.
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
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 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.
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.
At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose …
CoSCA improves unsupervised domain adaptation by better aligning ambiguous target samples.
problem Missing alignment of ambiguous target samples in unsupervised domain adaptation.
method CoSCA explicitly incorporates intra- and inter-class domain discrepancy, estimating label hypotheses and optimizing a contrastive loss with MMD for better global alignment.
result CoSCA outperforms state-of-the-art approaches in producing more discriminative features.
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.
A new model improves machine translation by accounting for translation ambiguity.
problem Translation ambiguity in parallel corpora leads to inconsistent models.
method A deep generative model with latent variables to handle local variation.
result The model consistently outperforms existing baselines on multiple language pairs.
Neural networks provide bounds for risk aggregation under ambiguous dependence.
problem Quantifying joint effects of risks with uncertain dependencies.
method Dual representation and neural network solution.
result Robust bounds for risk aggregation are derived.
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.