A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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.
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
problem Learning optimal policies from observed data with asymmetric counterfactual utilities.
method The approach involves identifying and minimizing the maximum expected utility loss using statistical decision theory and solving intermediate classification problems.
result One can learn minimax loss decision rules from observed data.
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees tha…
Given a task of predicting Y from X, a loss function L, and a set of probability distributions Γ on (X,Y), what is the optimal decision rule minimizing the worst-case expected loss over Γ? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying t…
Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics. This paper offers a consolidated presentation of a theory of bounded rationality based on i…
We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated prediction in this setting. By using PAC-Bayes analysis on these rules, we obtain data-dependent performance guarantees without distributio…
Study improves distributional regression evaluation with CRPS, finding optimal rates of convergence.
problem Improving probabilistic forecasts in meteorology using distributional regression.
method Extends theoretical properties of CRPS evaluation to include covariates and finite sample sizes, analyzing convergence rates for different methods.
result Optimal minimax rate of convergence for distributional regression methods is achieved by k-nearest neighbor and kernel methods.
We study the problem of selling an asset near its ultimate maximum in the minimax setting. The regret-based notion of a perfect stopping time is introduced. A perfect stopping time is uniquely characterized by its optimality properties and has the following form: one should sell the asset if its price deviates from the…
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.
We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified {\it average} feature acquisition b…
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level …
Study one-shot strategic classification under unknown costs, improving worst-case accuracy.
problem Learning robust decision rules in strategic settings with unknown user costs.
method Formal study of one-shot strategic classification, framing as a minimax problem, designing efficient algorithms for full-batch and stochastic settings.
result Proves efficient algorithms converge to minimax solution, revealing dual norm regularization's value.