On-device research index

arXiv research

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.

168,695 papers · 148 categories

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48 results for “Adaptive Decision Making”

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agentsstat.ML

Geometric methods solve sampling, optimisation, inference, and adaptive decision-making.

problem Efficient solutions for sampling, optimisation, inference, and adaptive decision-making.

method Derive algorithms exploiting geometric structures of Hamiltonian systems, Hilbertian subspaces, and information geometry.

result Wide range of geometric theories emerge in these fields, enabling efficient solutions.

geometric structures sampling optimization inference adaptive decision-making

2022-03-20abs ↗pdf ↗

Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environmentscs.LG

PDTS improves robustness in sequential decision-making.

problem Robust active task sampling for efficient and reliable decision-making.

method Characterizes robust active task sampling as a Markov decision process, proposes PDTS method.

result Significantly improves zero-shot and few-shot adaptation robustness.

sequential decision-making robust active task sampling meta reinforcement learning risk-averse decision-making posterior and diversity synergies

2025-04-27abs ↗pdf ↗

FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Makingcs.LG

FinHEAR combines LLMs with human expertise for better financial decision-making.

problem Challenges in financial decision-making for language models.

method Multi-agent framework with specialized LLMs for historical analysis, event interpretation, and expert retrieval.

result FinHEAR outperforms baselines in financial tasks with higher accuracy and risk-adjusted returns.

financial decision-making language models human expertise temporal reasoning adaptive risk assessment

2025-06-10abs ↗pdf ↗

LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problemcs.LG

LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.

problem Optimizing sequential decisions with a linear threshold under resource limitations.

method LinearAPT, an adaptive algorithm for fixed-budget TLB problem.

result LinearAPT achieves theoretical upper bounds and robust performance on various datasets.

Linear Bandit Thresholding Sequential Decision Making Resource Constraints Fixed Budget

2024-03-10abs ↗pdf ↗

Learning-to-defer for sequential medical decision-making under uncertaintycs.LG

Framework for deferring decisions to experts in sequential medical settings.

problem Myopic and non-adaptive decision-making by ML models in sequential medical contexts.

method Sequential Learning-to-Defer (SLTD) framework using model-based reinforcement learning.

result Adaptive deferral policy improves trade-off between long-term outcomes and deferral frequency.

Sequential decision-making Medical decision-making Learning-to-defer Reinforcement learning Uncertainty quantification

2021-09-13abs ↗pdf ↗

Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Dataeess.SP

New algorithm tracks COVID-19 outbreak phases.

problem Decision-making in pandemic data.

method Developed a new algorithm (BLLR) based on decision theory.

result Demonstrated ability to track different phases of the COVID-19 outbreak.

COVID-19 Decision theory Pandemic Algorithm Learning

2020-12-14abs ↗pdf ↗

Making Decisions under Outcome Performativitycs.LG

New approach tackles decision-making under predictions that shape outcomes.

problem Challenges in learning optimal decision rules when predictions influence outcomes.

method Introduces performative omniprediction, a predictor that encodes optimal decision rules for multiple objectives.

result Efficient performative omnipredictors exist under a natural restriction of outcome performativity.

outcome performativity decision-making predictions optimal decision rules supervised learning

2022-10-04abs ↗pdf ↗

Structure-Adaptive Sequential Testing for Online False Discovery Rate Controlstat.ME

New method controls false discoveries in real-time data streams.

problem Online testing of hypotheses with strict error constraints and no future data.

method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.

result Substantial power gain over existing online testing rules.

false discovery rate sequential testing online decision making alpha-investment time-varying structures

2020-02-28abs ↗pdf ↗

ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordinationq-fin.TR

ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.

problem Adapting LLMs for real-time financial decision-making in noisy markets.

method ATLAS integrates structured market data, uses Adaptive-OPRO for prompt optimization, and employs multi-agent coordination.

result Adaptive-OPRO consistently outperforms fixed prompts in financial trading.

LLM trading prompt optimization multi-agent coordination financial decision-making market noise

2025-10-10abs ↗pdf ↗

The Statistical Complexity of Interactive Decision Makingcs.LG

New complexity measure for interactive learning reduces regret to near-optimal levels.

problem Challenges in sample-efficient, adaptive learning algorithms for interactive decision making.

method Introduces the Decision-Estimation Coefficient and the Estimation-to-Decisions (E2D) principle.

result Unified algorithm design principle E2D achieves optimal sample-efficient learning.

interactive decision making regret minimization statistical complexity reinforcement learning supervised estimation

2021-12-27abs ↗pdf ↗

Adaptive Robust Online Portfolio Selectionq-fin.PM

Adaptive robust strategy improves online portfolio selection by managing market trends and costs.

problem Optimizing sequential investment decisions in volatile markets.

method Robust optimization with adaptive parameter adjustment.

result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.

portfolio selection robust optimization online decision-making transaction costs market trends

2022-06-02abs ↗pdf ↗

Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approachcs.LG

Adaptive spectral RL method enhances RL performance and interpretability.

problem Balancing interpretability and performance in reinforcement learning.

method Spectral based linear RL approach with adaptive regularization.

result Near-optimal bounds for parameter estimation and generalization error.

Reinforcement Learning Interpretability Performance Spectral Filter Regularization

2025-10-04abs ↗pdf ↗

Human-AI Collaborative Uncertainty Quantificationcs.AI

This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.

problem Current AI lacks robust decision-making capabilities under uncertainty, especially in high-stakes contexts.

method Introduces Human AI Collaborative Uncertainty Quantification (HACUQ) framework, formalizing AI-human collaboration and developing calibration algorithms.

result Optimal collaborative prediction sets follow a two-threshold structure, and online adaptation algorithms can adapt to evolving human behavior.

Human-AI collaboration Uncertainty quantification Decision making Conformal prediction Calibration algorithms

2025-10-27abs ↗pdf ↗

Online Statistical Inference in Decision-Making with Matrix Contextstat.ML

New online method for statistical inference with matrix context in decision-making.

problem Statistical inference in decision-making with matrix context.

method Proposes a fully online procedure to conduct statistical inference with adaptive data collection, handling low-rank structure.

result Establishes asymptotic normality of debiased estimators and proves validity of confidence intervals.

online decision-making matrix context statistical inference low-rank structure stochastic gradient descent

2022-12-21abs ↗pdf ↗

Learning Pareto-Efficient Decisions with Confidencestat.ML

The paper tackles uncertainty in multi-objective decision-making.

problem Learning Pareto-efficient decisions with statistical confidence in uncertain outcomes.

method Adapting Pareto-efficient decisions to uncertainty, using conformal prediction.

result Statistical guarantees for efficient decisions in uncertain contexts.

multi-objective decision-making uncertainty quantification conformal prediction tail outcomes safety-critical applications

2021-10-19abs ↗pdf ↗

Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarioscs.LG

In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…

2019-04-12abs ↗pdf ↗

Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyondcs.LG

Adaptive traffic control uses deep RL to improve decision-making.

problem Improving traffic control using deep RL.

method Integrates recent deep RL techniques into a novel DQN-based algorithm (TC-DQN+) for traffic control.

result Proposes a new reward function for traffic control.

traffic control deep reinforcement learning adaptive control reinforcement learning deep Q-networks

2020-07-21abs ↗pdf ↗

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designstat.ML

DAD learns to design experiments quickly, outperforming traditional methods.

problem Real-time decision-making in sequential Bayesian experimental design.

method Amortized design network trained with contrastive information bounds.

result DAD outperforms alternative strategies on various problems.

Bayesian experimental design sequential decision-making amortization real-time experimentation adaptive design

2021-03-03abs ↗pdf ↗

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theorycs.LG

New algorithm achieves instance-optimality in decision making.

problem Develop adaptive algorithms for interactive decision making.

method Introduce Allocation-Estimation Coefficient (AEC) and develop

\mathsf{AE}^2

algorithm.

result First non-asymptotic instance-optimal performance guarantees.

instance-optimality adaptive algorithms reinforcement learning bandits non-asymptotic guarantees

2023-04-24abs ↗pdf ↗

An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systemscs.LG

Information-theoretic bounded rationality describes utility-optimizing decision-makers whose limited information-processing capabilities are formalized by information constraints. One of the consequences of bounded rationality is that resource-limited decision-makers can join together to solve decision-making problems …

2019-07-26abs ↗pdf ↗

Non-Linear Model-Based Sequential Decision-Making in Agriculturestat.ML

Develops adaptive algorithms for sustainable fertilizer use in agriculture.

problem Sustaining high yields while reducing environmental impacts of fertilizer use.

method Nonlinear model-based bandit algorithms linking biological processes to decision-making.

result Faster learning and higher profits with interpretable recommendations.

agriculture fertilizer management decision-making sustainability bandit algorithms

2025-09-02abs ↗pdf ↗

Adaptive Sequence Submodularitycs.LG

In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e.g., guiding an agent through a series of states). Not only do sequences already pose a dauntingly large search space, but…

2019-02-15abs ↗pdf ↗

Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Krigingstat.AP

Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and ass…

2020-02-06abs ↗pdf ↗

Attack and defence in cellular decision-making: lessons from machine learningphysics.bio-ph

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…

2018-07-10abs ↗pdf ↗

SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariatesstat.ML

SPARKLE handles high-dimensional covariates for online decision-making.

problem Complex reward-covariate relationships in high-dimensional settings.

method SPARKLE uses a sparse additive reward model with doubly penalized estimator and adaptive screening.

result SPARKLE achieves sublinear regret bound logarithmic in covariate dimensionality.

contextual bandits nonparametric models high-dimensional covariates online decision-making doubly penalized estimator

2025-03-21abs ↗pdf ↗

DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecastingcs.LG

Enhanced decision-making through Dreamer's anticipatory trajectories and Online Decision Transformer.

problem Efficiently integrating world models with decision transformers.

method Combining Dreamer's trajectory forecasting with Online Decision Transformer's adaptive learning.

result Notable improvements in sample efficiency and reward maximization.

reinforcement learning decision transformers Dreamer trajectory forecasting sample efficiency

2024-10-15abs ↗pdf ↗

INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agentcs.CE

InvestorBench benchmarks LLM agents in financial tasks.

problem Lack of a comprehensive benchmark for LLM-based financial agents.

method Developed a benchmark with diverse financial tasks and datasets.

result Evaluated LLM agents' performance across various financial products and market environments.

LLM Financial Decision-Making Benchmark Agent Evaluation Financial Tasks

2024-12-24abs ↗pdf ↗

Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisioncs.LG

A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users' experiences and cause irreversible damage. A ty…

2020-01-16abs ↗pdf ↗

Isomorphic Functionalities between Ant Colony and Ensemble Learning: Part II-On the Strength of Weak Learnability and the Boosting Paradigmstat.ML

Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.

problem Understanding the mathematical principles behind ensemble learning and ant colony behavior.

method Developed a formal mapping between AdaBoost's adaptive reweighting and ant recruitment dynamics.

result Proved that the fundamental theorem of weak learnability has a direct analog in colony decision-making.

ensemble learning ant colony adaptive reweighting bias reduction variance reduction

2026-03-25abs ↗pdf ↗

Online Multi-Armed Bandits with Adaptive Inferencecs.LG

New algorithm improves online decision making by adaptively inferring arm rewards.

problem Adaptive selection of arms in MAB leads to non-iid data, complicating accurate inference.

method Proposes a doubly adaptive TS algorithm that leverages causal inference for adaptive reweighting.

result Demonstrates superior empirical performance in identifying the best arm compared to UCB and TS.

Multi-Armed Bandits Adaptive Inference UCB Thompson Sampling Doubly Robust Estimator

2021-02-25abs ↗pdf ↗

The Central Role of the Loss Function in Reinforcement Learningstat.ML

The paper explores how different loss functions impact reinforcement learning algorithms.

problem Improving reinforcement learning algorithms by optimizing loss functions.

method Comprehensive survey on loss functions in reinforcement learning, proving the benefits of specific loss functions.

result Binary cross-entropy loss leads to first-order bounds and is more efficient than squared loss.

reinforcement learning loss functions binary cross-entropy squared loss distributional RL

2024-09-19abs ↗pdf ↗

Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Researchcs.AI

Enhances financial analysis with multi-agent collaboration.

problem Limited use of AI-agent collaboration in financial research.

method Proposes a multi-agent system for financial investment research.

result Multi-agent system outperforms single-agent models.

financial analysis AI-agent collaboration investment decision-making market sentiment fundamentals

2024-11-07abs ↗pdf ↗

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiersmath.OC

Deep learning complements OR/MS for decision-making under uncertainty.

problem Sequential decision-making in uncertain environments.

method Integration of deep learning and OR/MS frameworks.

result Deep learning enhances adaptability and scalability in decision systems.

deep learning sequential decision-making uncertainty operations research management sciences

2026-04-13abs ↗pdf ↗

Money as a Tensorq-fin.GN

Money analyzed as a multidimensional tensor for better economic policy.

problem Economic complexity and policy responsiveness.

method Tensor analysis of money dynamics.

result Enhanced economic policy design and resilience.

money tensor economic tensor tensor analysis economic policy adaptive governance

2025-04-01abs ↗pdf ↗

Time Adaptive Reinforcement Learningcs.LG

New RL algorithms adapt to time limits, improving task performance.

problem Fixed RL behaviors cannot adapt to different time restrictions.

method Introduced two algorithms for time adaptive RL: Independent Gamma-Ensemble and n-Step Ensemble.

result Zero-shot adaptation between different time restrictions.

Reinforcement Learning Time Adaptation Markov Decision Processes Value-Based Algorithms

2020-04-18abs ↗pdf ↗

Overfitting in Adaptive Robust Optimizationmath.OC

ARO overfits by making constraints dependent on uncertainty, leading to brittleness.

problem ARO's adaptive policies become brittle when realizations fall outside the uncertainty set.

method Assigning constraint-specific uncertainty set sizes with probabilistic guarantees.

result Regularization through specific uncertainty set sizes ensures stability and flexibility.

overfitting robust optimization machine learning constraint dependency uncertainty sets

2025-09-19abs ↗pdf ↗

Bounded Rational Decision-Making with Adaptive Neural Network Priorscs.AI

Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents' pr…

2018-09-04abs ↗pdf ↗

Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descentstat.ML

New method for online statistical inference in contextual bandits using SGD.

problem Online decision-making in contextual bandits with statistical inference.

method Weighted stochastic gradient descent for adaptive data collection.

result Asymptotic normality of the parameter estimator with improved efficiency.

contextual bandits online learning stochastic gradient descent statistical inference asymptotic normality

2022-12-30abs ↗pdf ↗

Meta-Learning surrogate models for sequential decision makingstat.ML

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the d…

2019-03-28abs ↗pdf ↗

Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Datacs.LG

Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.

problem Jointly satisfying distribution-free coverage guarantees and risk-adaptive precision in clinical decision-making.

method Integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.

result Achieves target coverage (94.3% vs 95% target) with adaptive precision, 21% narrower intervals for low-uncertainty cases.

Bayesian uncertainty conformal prediction healthcare data clinical decision-making risk stratification

2026-01-03abs ↗pdf ↗

Adaptive Resampling with Bootstrap for Noisy Multi-Objective Optimization Problemscs.LG

This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.

problem Challenges in optimizing noisy multi-objective problems, especially trade-offs between exploration and exploitation.

method Adaptive resampling with bootstrapping to estimate probability of dominance and improve precision.

result Demonstrates the efficiency of the resampling approach in NSGA-II algorithm under multiple noise variations.

noisy optimization multi-objective adaptive resampling bootstrapping probability of dominance

2025-03-27abs ↗pdf ↗

Batched Neural Banditscs.LG

Batched Neural Bandits reduces policy updates in sequential decision-making.

problem Sequential decision-making with batched policy changes.

method BatchNeuralUCB algorithm combining neural networks and optimism.

result Achieves similar regret as fully sequential version with fewer policy updates.

batched decision-making stochastic bandits neural networks optimism regret minimization

2021-02-25abs ↗pdf ↗

Active Learning with Safety Constraintscs.LG

Investigates safe decision-making in interactive environments.

problem Learning the best safe decision in real-time systems.

method Reduces to a constrained linear bandits problem, proposes adaptive experimental design-based algorithm.

result First results on best-arm identification in linear bandits with safety constraints.

active learning safety constraints interactive environments bandits problem adaptive design

2022-06-22abs ↗pdf ↗

Minimax-Optimal Policy Regret in Partially Observable Markov Gamescs.LG

Study optimal policy regret in partially observable Markov games with adaptive opponents.

problem Optimal sequential decision-making in partially observable environments against strategic, adaptive opponents.

method An epoch-based optimistic maximum-likelihood algorithm that selects one policy per epoch using confidence sets built cumulatively from past data.

result Achieves

ilde{O}(\sqrt{T})

policy regret for fixed problem parameters, with explicit dependence on horizon, adversary memory, confidence radius, and aggregate Eluder dimension.

Markov Games Sequential Decision Making Adversarial Learning Optimistic Algorithms Partial Observability

2026-06-01abs ↗pdf ↗

Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learningstat.ML

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

(t_1, t_2)

survival analysis individualized treatment rules deep learning bivariate outcomes censoring

2026-05-28abs ↗pdf ↗

Constrained Online Decision-Making: A Unified Frameworkstat.ML

Unified framework for constrained online decision-making.

problem Sequential decisions under stage-wise feasibility constraints.

method Upper counterfactual confidence bounds and generalized eluder dimension.

result Principled foundation for constrained sequential decision-making.

online decision-making constrained learning feasibility constraints upper confidence bounds eluder dimension

2025-05-11abs ↗pdf ↗

Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learningcs.LG

CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.

problem Interpreting dynamic human decision-making processes in medical contexts.

method Develops Contextualized Policy Recovery (CPR) framework for multi-task learning, modeling each context-specific policy as a linear map.

result Achieves state-of-the-art performance in predicting medical decisions, closing the gap between interpretable and black-box methods.

Policy Recovery Adaptive Imitation Learning Medical Decision Making Interpretability Context-Specific Policies

2023-10-11abs ↗pdf ↗

Information-Theoretic Generalization Bounds for Sequential Decision Makingstat.ML

Develops a new framework for analyzing sequential decision-making problems using information theory.

problem Lack of information-theoretic generalization bounds for sequential decision-making problems.

method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.

result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.

information theory sequential decision making generalization bounds supersample construction exchangeability

2026-05-12abs ↗pdf ↗