Proposes on-the-fly joint feature selection and classification for time-sensitive decisions.
problem Online feature selection and classification for time-sensitive decision making.
method Proposes a framework for joint feature selection and classification on-the-fly, optimizing feature usage and classification accuracy.
result Demonstrates superior performance over state-of-the-art methods on various datasets.
A simple GI loss improves temporal generalization without complex methods.
problem Temporal drift between train and test distributions in evolving data.
method Gradient Interpolation (GI) loss to regularize temporal complexity.
result GI loss outperforms complex methods on real-world datasets.
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.
Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications. However, despite GNNs' impressive performance, it has been observed that carefully c…
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati…
Improves text-to-speech speed by interleaving character reading and audio synthesis.
problem Latency in text-to-speech models limits their use in time-sensitive tasks.
method Reinforcement learning to train an agent to choose the order of character reading and audio synthesis.
result The proposed method successfully balances latency and audio quality.
Sequential hypothesis testing is a desirable decision making strategy in any time sensitive scenario. Compared with fixed sample-size testing, sequential testing is capable of achieving identical probability of error requirements using less samples in average. For a binary detection problem, it is well known that for k…
Study uses DRL with Lagrangian relaxation to solve temporal control tasks with STL constraints.
problem Optimal control problems with temporal logic constraints.
method Extended CMDP formulation, Lagrangian relaxation, two-phase constrained DRL algorithm.
result Demonstrated learning performance of the proposed algorithm through simulations.
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
A Bayesian framework models dynamic probability predictions over time.
problem Dynamic probability predictions over time in various settings.
method Gaussian latent information martingale (GLIM) framework.
result GLIM outperforms baseline methods in predicting future uncertainties.
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the common relations among patients (e.g., share the same doctor) were rarely considered. In this paper, we represent patients and clinicians relati…
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time…
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protrac…
A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period. To improve the efficiency of the emergency response in t…
This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice…
This paper develops a framework for efficient decision-making under time pressure.
problem Efficient decision-making under time pressure and subjective tradeoffs.
method Unified framework for evidence-based decision-making under time pressure.
result Ability to model and understand decision-making behavior under time constraints.
Study minimax-optimal rates for offline decision-making with function approximation.
problem Statistical complexity of offline decision-making with function approximation.
method Near minimax-optimal rates for stochastic contextual bandits and Markov decision processes, using pseudo-dimension and behavior policy.
result Established performance limits and new characterization of behavior policy.
Framework for robust decision making in changing environments with privacy constraints.
problem Interactive decision making in changing environments with constraints.
method Hybrid Decision Making with Structured Observations (hybrid DMSO) framework, local differentially private decision making, query-based learning, robust and smooth decision making.
result Strong connections and bounds derived for DEC, SQ dimension, local minimax complexity, learnability, and joint differential privacy.
Enhances LightGCN for credit bond recommendations with dynamic node embeddings.
problem Challenges in static embeddings for rapidly evolving user interests in finance.
method Causal graph convolution for dynamic node embeddings over chronological user-item interactions.
result Significantly enhances LightGCN performance in financial product recommendations.
Paper tackles risk-sensitive decision-making under uncertainty.
problem Risk-sensitive decision-making problem under uncertainty.
method Formulated as a stochastic control problem, delineated necessary optimality conditions.
result Illustrative examples from optimal betting and inventory management support the theory.
New active learning strategy improves decision-making accuracy.
problem Maximizing decision-making accuracy in sequential data acquisition.
method Introduces a novel active learning criterion that maximizes expected information gain on the posterior decision distribution.
result Improved performance in decision-making accuracy compared to existing alternatives.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.
The Chain-of-Decision approach improves forecasting of financial professionals' trading decisions.
problem Challenges in forecasting professionals' behaviors, especially in trading decisions.
method Integrates an opinion-generator-in-the-loop to provide subjective analysis based on news items.
result Promising improvements in the proposed tasks' performance.
The study examines robust decision-making in volatile financial markets, finding action robustness is more impactful than uncertainty tolerance.
problem Sequential decision making in high-frequency markets under evolving uncertainty.
method Analyzes two dimensions of robustness: uncertainty tolerance and action robustness, using simulations and empirical evidence.
result Action robustness has a larger impact on profitability than uncertainty tolerance, and excessive robustness can reduce profitability in illiquid markets.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these exp…
We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making…
An online decision-making algorithm using stochastic gradient descent for big data.
problem Efficiently updating decision rules in online decision making with big data.
method Stochastic gradient descent for online updates, asymptotic normality of estimators.
result Asymptotic normality of parameter and value estimators, enabling statistical inference.
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…
New algorithms for fast online decision making using neural networks and martingale posteriors.
problem Online sequential decision making under uncertainty.
method Martingale posterior neural networks for fast online learning and decision making.
result Achieves competitive performance-speed trade-offs in non-stationary contextual bandits and Bayesian optimization.
Post-processing predictors reduces calibration errors for decision-making.
problem Predictors with low calibration error for machine learning may have high error for decision-making.
method Post-processing with ε distance to calibration adds noise to make predictions differentially private.
result Post-processing achieves O(√ε) ECE and CDL, asymptotically optimal.
Improving cancer treatment decisions requires considering causal effects, not just model accuracy.
problem Cancer outcome prediction models may cause harm when used for treatment decisions.
method Explains the importance of considering causal effects in model validation and provides guidelines.
result Building and validating models that are useful for decision making requires considering causal effects.
Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes th…
Develops methods for finding counterfactual explanations in sequential decision making.
problem Finding counterfactual explanations for sequential decision making processes.
method Formal characterization of sequential actions and states using Markov decision processes and Gumbel-Max structural causal model. Introduces a polynomial time algorithm based on dynamic programming.
result Algorithm finds optimal counterfactual explanations for sequential decision making.
Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set o…
Paper offers a fast convergence theory for offline decision making.
problem Offline decision making problems, including reinforcement learning and off-policy evaluation.
method Introduces a framework (DMOF) and algorithm (EDD) with a fast convergence guarantee.
result Demonstrates a fast convergence guarantee with a lower bound complement.
New bounds show complexity of adversarial decision making.
problem Understanding sample efficiency in adversarial decision making.
method New upper and lower bounds on Decision-Estimation Coefficient.
result Decision-Estimation Coefficient is necessary and sufficient for low regret in adversarial decision making.
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task i…
Improved model-free reinforcement learning with decision-estimation coefficient.
problem Interactive decision making, including structured bandits and reinforcement learning.
method Combining Estimation-to-Decisions with optimistic estimation to achieve better regret bounds.
result Regret bounds for model-free reinforcement learning with value function approximation.
New method learns decisions from collective preferences without individual covariates.
problem Making decisions online without individual covariates.
method Collaborative filtering, matrix completion bandit, ε-greedy policy, online gradient descent, inverse propensity weighting.
result Method outperforms benchmarks and reveals new discoveries.
Proposes a framework to quantify uncertainty in multi-step decision-making by LLMs.
problem Uncertainty quantification in multi-step decision-making scenarios of LLMs.
method A principled, information-theoretic framework decomposing uncertainty into internal and extrinsic components, and proposing UProp for efficient extrinsic uncertainty estimation.
result UProp significantly outperforms existing single-turn UQ baselines in multi-step decision-making benchmarks.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing Y while ensuring causal fairness. Introduces principal fairness for fair decision-making.
problem Discrimination among similarly affected individuals.
method Uses principal stratification from causal inference.
result Explicitly accounts for decision impacts, not just protected attributes.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
problem Optimal decision making in processes influenced by uncertain random factors.
method Stochastic models for probabilistic forecast evolution, calibrated from ensemble forecasts.
result Optimal decision strategies determined using dynamic probabilistic forecasts.
Human irrationality can improve AI design, study shows.
problem Improving AI by learning from human decision-making biases.
method Developed a novel POMDP model to simulate human decision-making in contextual choice tasks.
result Reinforcement learners can exploit human irrationalities to make better decisions.
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and model…