A new method for modeling event sequences with ambiguous timestamps.
problem Handling event sequences with variable timestamps and time-shifts.
method Time-discounting convolution with dynamic pooling.
result Efficiently models event dependencies and robust against timestamp uncertainty.
Improved financial predictions with OHLC data and timestamps.
problem Improving VWAP predictions in financial markets.
method Investigated the impact of timing features on machine learning models for VWAP prediction.
result Incorporating timing features consistently improves predictive performance across multiple ML architectures.
RE-NET predicts future interactions in temporal knowledge graphs.
problem Predicting future facts in temporal knowledge graphs.
method Autoregressive architecture with recurrent event encoder and neighborhood aggregator.
result State-of-the-art performance on five public datasets.
Temporal threat model defends against data poisoning with timestamps.
problem Adversaries can poison more samples than expected, rendering existing defenses ineffective.
method Leverage timestamps to define earliness and duration metrics for temporal robustness.
result Temporal aggregation provides provable temporal robustness against data poisoning.
NoLBERT avoids lookback and lookahead biases for better econometric inference.
problem Information leakage in language models affects econometric inference.
method Pretrained on text from 1976-1995, avoiding lookback and lookahead biases.
result NoLBERT outperforms domain-specific baselines and predicts higher profit growth.
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.
Proposes models to analyze irregular healthcare time series data.
problem Irregular timestamps in healthcare time series data.
method Data augmentation, temporal coarsening, MultiResolution Ensemble (MRE) model.
result Improves mAP on mortality prediction task from 51.53% to 53.92%.
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.
ForesightFlow detects informed trading on prediction markets using an information leakage score.
problem Detecting informed trading on decentralized prediction markets.
method Developed an Information Leakage Score (ILS) framework to quantify the fraction of terminal information move priced in before public news events.
result The score connects label generation to proper-scoring-rule literature and reveals systematic biases in insider trading documentation.
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.
In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …
New protocol evaluates synthetic data for temporal consistency.
problem Synthetic data generators can produce invalid timestamps and trajectories.
method Characterize datasets by four properties, then measure timestamp validity and dynamics.
result Temporal fidelity must be measured, not inferred from static data.
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.
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 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 …
Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time inte…
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.
Framework improves clinical timeline reconstruction from text and tables.
problem Temporal precision and event timing in clinical narratives and EHRs.
method Retrieval-augmented multimodal alignment framework.
result Consistently improves absolute timestamp accuracy and temporal concordance.
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.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.
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.
VOLARE provides standardized realized volatility measures from financial data.
problem Lack of standardized realized volatility measures from ultra-high-frequency data.
method Asset-specific pipeline for cleaning and sampling data, providing a wide range of realized estimators.
result Comprehensive set of realized estimators for equities, exchange rates, and futures.
For regular particle filter algorithm or Sequential Monte Carlo (SMC) methods, the initial weights are traditionally dependent on the proposed distribution, the posterior distribution at the current timestamp in the sampled sequence, and the target is the posterior distribution of the previous timestamp. This is techni…
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.
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.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
A new method uses sinusoidal functions to represent timestamps as dense vectors for improving irregularly sampled time series learning.
problem Challenges in supervised learning with irregularly sampled time series due to irregular time intervals.
method Proposes a novel method to represent timestamps as dense vectors using sinusoidal functions, called Time Embeddings.
result Improves LSTM-based and classical machine learning models, especially with very irregular data.
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.
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.
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.