LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
problem Combining flexibility and interpretability in personalized treatment rules.
method Uses variational autoencoders and a mixture of interpretable experts.
result Accurately recovers true local coefficients and optimal treatment strategies.
Kernel method optimizes personalized dose rules for patients.
problem Finding optimal individualized dose rules for patients.
method Kernel assisted learning method for estimating optimal dose rules.
result The method identifies the optimal individualized dose rule and produces favorable outcomes.
GEAR uses auxiliary data to estimate optimal decisions in studies with limited primary outcomes.
problem Estimating optimal decisions when primary outcomes are not available in experimental samples.
method GEAR uses augmented inverse propensity weighting to estimate optimal decisions based on auxiliary data.
result GEAR estimators and value estimators have established asymptotic properties and are validated in simulations and a real application.
The paper develops deep learning models for personalized treatment rules in survival analysis.
problem Deriving optimal treatment rules for bivariate survival outcomes in randomized trials.
method Adaptive prediction-powered learning using deep neural networks and stochastic policies.
result Maximizes joint survival probability beyond fixed time points (t1,t2). Paper uses stats to predict treatment choice based on illness probability.
problem Improving treatment decision-making in personalized medicine.
method Statistical decision theory with maximum regret evaluation.
result Estimates illness probability for better treatment choice.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Develops deep jump learning for continuous treatment OPE.
problem Estimating mean outcomes under new treatment rules using historical data from different rules.
method Adaptive deep discretization of continuous treatment space using deep learning and multi-scale change point detection.
result Validated method through theoretical results, simulations, and real application to Warfarin Dosing.
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.
Estimates individual causal effects in the presence of unobserved confounders.
problem Learning CATE under unconfoundedness violations.
method Develops a functional interval estimator that predicts bounds on individual causal effects.
result Sharp interval estimator converges to tightest bounds on CATE.
The paper proposes a policy learning framework for interpretable personalization.
problem Effective personalization of goods and services to improve revenues and maintain competitive edge.
method Policy learning with linear decision boundaries using causal inference and Bayesian optimization.
result The learned policy improves net sales revenue by 88.2% and provides insights into important features.
Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…
RLMM extends psychometric models to larger tasks.
problem Sequential process data from interactive assessments are not well handled by conventional models.
method RLMM decouples person-level choice sensitivity from task-level value representation through a shared parametric action-value function.
result RLMM achieves higher estimation accuracy and lower runtime than MDP-MM in peg-solitaire simulations and AQUALAB gameplay logs.
Proposes a framework to create fair IDRs by enforcing demographic parity constraints.
problem Discrimination in IDRs trained on biased data.
method Incorporates DP and CDP constraints into IDR estimation.
result Theoretically optimal IDRs can be efficiently obtained through perturbations.
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of sim…
Boosting algorithms improve estimation of personalized treatment rules.
problem Estimating optimal personalized treatment rules in clinical settings.
method Additive regression trees with boosting technique based on XGBoost.
result Efficient and accurate estimation of complex treatment rules.
Unified stopping rules ensure accurate policies in contextual learning.
problem Stopping data collection to ensure accurate policies in personalized decision problems.
method Developed unified stopping rules based on GLR statistics for pairwise action comparisons.
result Unified stopping rules achieve target precision with fewer samples than benchmarks.
A new method improves efficiency in finding optimal personalized treatment rules.
problem Heteroscedasticity and misspecified treatment-free effect models affect optimal ITR estimation.
method E-Learning framework that accounts for covariate-treatment dependent variance of residuals.
result E-Learning framework improves efficiency of optimal ITR estimation.
Estimates personalized treatment response curves using covariates.
problem Flexible estimation of personalized treatment response curves.
method Sieve based nonparametric estimator of smoothed regimen-response curve function.
result Asymptotic linearity and undersmoothing criteria for efficient estimation.
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…
Developed a new algorithm to improve dynamic treatment regimens.
problem Non-convergence of Q-learning-based Q-shared algorithm in dynamic treatment regimens.
method Penalized Q-shared algorithm to address convergence issues.
result The penalized Q-shared algorithm converges and outperforms the original in various settings.
Proposes a method to learn from historical data for personalized decision-making.
problem Sample hunger in sequential decision-making algorithms for personalized medicine.
method Identifiable latent bandit framework using nonlinear independent component analysis.
result Optimal decision-making with shorter exploration time than classical bandits.
Vanguard uses AI to create personalized financial plans.
problem Challenges in choosing features for complex financial planning.
method Reinforcement learning for identifying optimal savings rates.
result Trains algorithms to model financial success trajectories.
Guarantees for third-person imitation learning from offline data.
problem Improving generalizability in imitation learning.
method Problem-dependent statistical learning guarantees for third-person imitation from offline observation.
result Strong performance guarantees for transferred policies in the offline setting.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
Algorithm learns from human demonstrations to schedule tasks efficiently.
problem Efficient resource scheduling in dynamic environments.
method Personalized apprenticeship learning framework infers decision-making criteria from heterogeneous human demonstrations.
result Achieves high accuracy in synthetic and real-world domains, outperforming baselines.
The paper aims to reduce bias in online decision-making by optimizing fairness and regret.
problem Achieving fair and justified real-time decisions in online systems.
method Adapting the learning-from-experts scheme to optimize fairness and regret for multiple label classes and sensitive groups.
result Approximately equalized odds can be achieved without significant loss in regret.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Personalized explanations improve understanding of machine learning models.
problem Improving human understanding of machine learning models and decisions.
method Deriving a conceptualization of personalized explanation, categorizing explainee data, identifying key properties, and introducing new measures.
result Identification of three key properties amendable to personalization: complexity, decision information, and presentation.
TS-UCB improves Thompson Sampling with minimal extra computation.
problem Online decision problems with bandit feedback.
method TS-UCB uses posterior samples and upper confidence bounds to select arms.
result TS-UCB achieves lower regret on various datasets.
Summarizes financial news for better investment decisions.
problem Information overload from financial news hinders timely investment decisions.
method Personalized Chain-of-Thought summarization framework integrating user-specified keywords.
result Personalized summaries highlight relevant market signals, improving investment narratives.
Paper extends transfer learning for decision rules, improving treatment rule estimation.
problem Estimating optimal individualized treatment rules under changing conditions.
method Bayes decision rules and low-dimensional empirical risk minimization.
result Consistent estimators and risk bounds established under mild conditions.
Paper develops privacy-preserving dynamic pricing policy for e-commerce.
problem Protecting customer privacy in dynamic pricing with personalized information.
method Uses differential privacy framework to develop a privacy-preserving policy.
result Achieves both privacy and performance guarantees in dynamic pricing.
Study assesses whether RL algorithm personalizes treatment sequences.
problem Evaluate if RL algorithm truly personalizes treatment sequences.
method Resampling-based methodology to investigate personalization.
result RL algorithm's personalization may be due to stochasticity.
Efficient algorithm for learning from indirect feedback in complex decision-making scenarios.
problem Learning from indirect feedback in realistic scenarios with personalized mechanisms.
method IGW algorithm for policy optimization, extending reward-estimator construction from single-step to multi-step.
result Achieves sublinear regret guarantee for contextual episodic MDPs with personalized feedback.
IRL models human risk decisions based on past outcomes.
problem Understanding human risk decisions under risk.
method Inverse Reinforcement Learning (IRL) with features reflecting state history.
result Human reward function explains risk-prone and risk-averse decisions.
New metrics evaluate personalized treatment rules.
problem Comparing performance of personalized treatment rules.
method Neyman's repeated sampling framework, random sampling of units, random assignment of treatment.
result Proposed metrics (PAPE, AUPEC) generalize QINI coefficient and AUROC.
Proposes a new criterion for selecting Nash equilibria considering both utility and inequality.
problem Finding a fair Nash equilibrium in group decision-making.
method Introduces entropy-norm space for geometric selection of strict Nash equilibria.
result The closest entropy-norm pair to the largest entropy-norm pair in rescaled space is the most suitable equilibrium.
New decision-theoretic characterization separates belief and decision posteriors.
problem Understanding the conditions under which loss-based updating coincides with Bayesian updating.
method Decision-theoretic approach to distinguish belief and decision posteriors.
result Generalized Bayes coincides with ordinary Bayesian updating only if the loss is proportional to negative log-likelihood.
We introduce a class of financial contracts involving several parties by extending the notion of a two-person game option (see Kifer (2000)) to a contract in which an arbitrary number of parties is involved and each of them is allowed to make a wide array of decisions at any time, not restricted to simply `exercising t…
A new method learns interpretable decision rules using submodular optimization.
problem Learning interpretable decision rules from data.
method Submodular optimization approach for selecting rules from a large set.
result The method effectively learns interpretable rule sets from real datasets.
Active learning improves decision-making from imbalanced observational data.
problem Reliability of prediction-based decisions in imbalanced observational data.
method Estimate Type S error rate to assess reliability, use active learning to collect new data.
result Active learning improves decision-making reliability in imbalanced data.
From doctors diagnosing patients to judges setting bail, experts often base their decisions on experience and intuition rather than on statistical models. While understandable, relying on intuition over models has often been found to result in inferior outcomes. Here we present a new method, select-regress-and-round, f…
New algorithms optimize decision rules in strategic scenarios, minimizing prediction risk and incentivizing better outcomes.
problem Strategic agents manipulate features to improve outcomes, complicating decision-making models.
method Efficient algorithms for learning decision rules that minimize prediction risk, incentivize better outcomes, and estimate true model coefficients.
result Optimal decision rules can be learned through testing and observing agent responses, circumventing hardness results.
AI models assess psychological risks in currency trading.
problem Identifying psychological risks in currency traders.
method Developed a decision tree model to identify patterns in historical data.
result Enhanced decision-making through real-time alerts.
This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To p…
OTSS learns personalized decision weights from logged decisions and outputs.
problem Learning context-specific decision weights from logged decisions and outputs.
method Output-targeted soft-segmentation model that deploys personalized decision-ready weight vectors.
result OTSS achieves the lowest mean regret in benchmark settings.
Two algorithms for interpreting and boosting tree-based models using rule covering.
problem Interpreting and boosting tree-based ensemble methods.
method Mathematical programming models constructed from decision tree rules.
result Selects a few rules that closely match the accuracy of the model.
This work proposes optimal decision rules for hierarchical classifiers to better align with evaluation metrics.
problem Heuristic decision rules in hierarchical classification do not align with evaluation metrics.
method Derives optimal decision rules for various prediction settings, focusing on hierarchical hFβ scores. result Optimal decision rules enhance the performance and reliability of hierarchical classifiers.