Novel algorithm reduces feature inclusion in online decision-making.
problem Optimizing decision-making for personalized user experiences with fairness.
method Online Batched Sequential Inclusion (OBSI) algorithm for sequential feature inclusion.
result OBSI outperforms other algorithms in terms of regret, relevance of features, and compute.
Universal algorithm learns unknown distribution for various decision-making problems.
problem Various statistical measures in contextual sequential decision-making.
method Infinite-dimensional functional regression oracle for cumulative distribution functions.
result Utility regret rate bounded by polynomial decay of eigenvalue sequence.
The paper proposes a method to learn and leverage contextual preference distributions for better decision-making.
problem Heterogeneous and context-dependent human preferences in decision-making problems.
method A sequential learning-and-optimization pipeline using a bounded-variance score function gradient estimator to train a predictive model mapping contextual features to preference distributions.
result The approach reduces average post-decision surprise by up to 25 times compared to risk-averse baselines in a ridesharing environment.
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.
The paper addresses statistical inference for online decision-making in a contextual bandit setting.
problem Understanding the performance of reward models in online decision-making with contextual information.
method The paper uses the contextual bandit framework with a linear reward model and the ε \varepsilon ε -greedy policy to address the exploration-exploitation dilemma. It employs the martingale central limit theorem and inverse propensity score weighting to establish asymptotic normality of parameter estimators. result The online ordinary least squares estimator and the online weighted least squares estimator are asymptotically normal, providing insights into the performance of the reward model.
New algorithm estimates treatment effects for more efficient contextual bandits.
problem Contextual bandits struggle with action-independent reward redundancies.
method Reduces contextual bandits to heterogeneous treatment effect estimation.
result Heterogeneous treatment effect estimation leads to more efficient model estimation.
New algorithm for partially observable contexts in finance.
problem Decision making based on partially observable, correlated market information.
method EMKF-Bandit algorithm integrating system identification, filtering, and bandit algorithms.
result Sub-linear regret under conditions on filtering.
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
Efficiently handles contextual bandits with diffusion models.
problem Challenges in online decision-making with contextual bandits.
method Leverage pre-trained diffusion models as priors to capture action dependencies.
result Developed an algorithm for efficient posterior approximation.
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.
CB-RL solves complex decision-making problems with contextual information and exogenous events.
problem Optimal policy in strategic decision-making problems that depend on environmental configuration and exogenous events.
method Contextual Bilevel Reinforcement Learning (CB-RL) with a stochastic Hyper Policy Gradient Descent (HPGD) algorithm.
result Demonstrated convergence and performance of the HPGD algorithm for reward shaping and tax design.
Practical algorithm for contextual bandits with large action spaces.
problem Efficient algorithms for decision making in large, continuous action spaces.
method Uses computational oracles for supervised learning and optimization over the action space.
result Achieves sample complexity, runtime, and memory independent of the size of the action space.
Algorithm improves decision-making with partially observed contexts using pretrained models.
problem Improving decision-making with partially observed contexts in online linear contextual bandits.
method PULSE-UCB algorithm that uses pretrained models trained on auxiliary data to impute missing features.
result Achieves near-optimal performance in i.i.d. context case with Hölder-smooth missing features.
Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.
problem Uncertainty quantification limitations in wSAA for contextual stochastic optimization.
method Establish central limit theorems and asymptotic-normality-based confidence intervals for optimal costs.
result Over-optimizing can mitigate misspecification and preserve asymptotic normality, albeit at a slower convergence rate.
A new algorithm balances exploration and exploitation in online decision-making.
problem Balancing exploration and exploitation in online decision-making.
method Proposed C 4 C^4 C 4 -UCB algorithm incorporating conservative mechanism. result Proved n-step upper regret bound for two situations.
Aims to improve personalized treatment decisions through Bayesian experimental design.
problem Evaluating and improving personalized treatment decisions in contexts like customer service.
method Model-agnostic Bayesian Experimental Design to efficiently gather data and avoid highly sub-optimal treatments.
result Our method achieves superior performance in evaluating and improving treatment decisions compared to traditional approaches.
Paper tackles domain adaptation for contextual bandits with sub-linear regret.
problem Adapting contextual bandit algorithms across domains with distribution shift.
method Learn a bandit model for the target domain using feedback from the source domain.
result Sub-linear regret bound maintained across domains.
Thompson Sampling improves decision-making in partially observed contexts.
problem Balancing exploration and exploitation in partially observed contextual bandits.
method Thompson Sampling policy for learning optimal arms from noisy linear functions of unobserved context vectors.
result Thompson Sampling achieves poly-logarithmic regret and square-root consistency of parameter estimation.
Parallelized bandit algorithms speed up decision-making.
problem Sequential exploration limits decision-making speed.
method Developed parallel contextual bandit algorithms.
result Regret nearly identical to sequential algorithms.
The paper tackles decision making problems with funnel structure in email marketing campaigns.
problem Decision making challenges in systems with funnel structure, where fewer observations are received from deeper layers.
method Formulated as a contextual bandit with funnel structure and developed a multi-task learning algorithm.
result Our algorithms offer significant improvement over previous methods in email marketing campaigns.
Paper proposes a risk-aware decision-making framework for real-world sequential decisions.
problem Real-world sequential decision-making problems often have critical constraints that learning solutions often neglect.
method Actor multi-critic architecture with risk characterization.
result Our approach consistently satisfies system constraints with minimal performance toll.
New methods improve robust decision-making under uncertainty in off-policy evaluation.
problem Statistical uncertainty and causal considerations in off-policy evaluation.
method Marginal Ratio (MR) estimator, Conformal Off-Policy Prediction (COPP), causal bounds.
result Improved robustness and uncertainty quantification in off-policy decision-making.
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co…
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.
Bayesian algorithms perform well even with misspecified priors, especially in meta-learning.
problem Performance degradation of Bayesian algorithms with misspecified priors.
method Thompson sampling and meta-learning analysis with misspecified priors.
result Thompson sampling's performance degrades gracefully with misspecification, with a bound of i l d e O ( H 2 ε ) ilde{\mathcal{O}}(H^2 ε) i l d e O ( H 2 ε ) . Kernel method improves cooperative decision-making among agents.
problem Cooperative multi-agent decision making with contextual information.
method Proposed extsc{Coop-KernelUCB} algorithm for near-optimal per-agent regret.
result Near-optimal bounds on per-agent regret with efficient computation and communication.
A new model optimizes portfolios by learning stock return distributions conditioned on factors.
problem Optimizing portfolios with high-dimensional asset-specific factors.
method Conditional Diffusion Transformer architecture linking each asset's return to its factor vector.
result The model outperforms benchmarks in mean-variance and mean-CVaR optimization.
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks. While there is a lot of prior research, tight regret bounds of linear contextual bandit with infi…
New algorithm for contextual dueling bandits achieves nearly optimal regret.
problem Contextual dueling bandits with feedback on preferred options.
method Proposes FGTS.CDB, a Thompson sampling algorithm for linear contextual dueling bandits.
result Achieves nearly minimax-optimal regret of i l d e O ( d T ) ilde{\mathcal{O}}(d\sqrt T) i l d e O ( d T ) . This paper tackles fair online decision-making in contextual bandits, achieving optimal performance and fairness.
problem Fairness in online decision-making systems under strategic manipulation.
method Develops algorithms for linear and smooth reward functions, maintaining fairness and optimal regret.
result Achieves nearly minimax-optimal regret with strong fairness guarantees, even in the presence of attacks.
New method tackles high-dimensional contextual bandits with flexible kernel models.
problem Maximizing rewards in decision-making scenarios with many features.
method Introduces stochastic assumptions and no-regret learning for Gaussian kernels.
result Achieves no-regret learning even with feature dimensions growing with samples.
Develops new optimization techniques for decision-making under uncertainty.
problem Decision-making under uncertainty with complex cost functions and nested expectations.
method Introduces Multistage Conditional Compositional Optimization (MCCO) and develops multilevel Monte Carlo techniques.
result New optimization techniques reduce scenario complexity from exponential to polynomial growth.
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.
Novel framework for contextual anomaly detection models uncertainty.
problem Identifying anomalies in target variables influenced by contextual variables.
method Normalcy score (NS) framework using heteroscedastic Gaussian process regression.
result NS outperforms state-of-the-art methods in detection accuracy and interpretability.
Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.
problem Improving decision-making in contexts with uncertain cost coefficients using partial feedback.
method Unified class of offline learning algorithms for CLO with different types of feedback, using IERM framework.
result Fast-rate regret bound for IERM with partial feedback and misspecified model classes.
Lecture notes on reinforcement learning using statistical methods.
problem Addressing the exploration-exploitation dilemma in decision making.
method Frequentist and Bayesian approaches, function approximation, neural networks.
result Unified framework for decision making and estimation.
Study batch learning in linear bandits with context, achieving near-optimal performance.
problem Sequential batch learning in linear contextual bandits with finite actions.
method Established regret bounds and provided algorithms for two settings: arbitrary contexts and i.i.d. contexts.
result Regret upper bound nearly matches lower bound, showing polynomial and logarithmic batch requirements.
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.
A smart method predicts and optimizes decisions online with resource constraints.
problem Online decision-making with resource constraints.
method Combines prediction and optimization with dual update using mirror descent.
result Regret bounds and convergence rates for general convex feasible regions.
A new DRL model for intraday trading incorporating positional context.
problem Neglecting positional context in existing DRL intraday trading strategies.
method Introducing positional features into the state space of a DRL model.
result Significant improvement in profitability and risk-adjusted metrics.
New Thompson Sampling for partially observed context bandits reduces regret logarithmically with time.
problem Improving Thompson Sampling for partially observed context bandits.
method Proposed a Thompson Sampling algorithm for partially observable contextual multi-armed bandits with theoretical performance guarantees.
result Regret scales logarithmically with time and the number of arms, and linearly with the dimension.
Paper introduces G-LowTESTR for efficient tensor bandits.
problem Efficient decision-making in multi-dimensional data with non-linear reward functions.
method Generalized low-rank tensor contextual bandits model and G-LowTESTR algorithm.
result G-LowTESTR achieves superior regret bound compared to vectorization and matricization methods.
New algorithms protect user data while optimizing personalized decisions.
problem Personalized decision-making with private user data.
method Developed LDP algorithms for stochastic generalized linear bandits using SGD and OLS.
result Achieved the same regret bound as non-privacy settings with LDP.
Brokerage algorithm learns from context to minimize trading regret.
problem Minimizing trading regret in a sequential brokerage problem.
method Design of algorithms with optimal theoretical regret guarantees.
result Optimal theoretical regret guarantees under various assumptions.
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broad…
ContextWIN uses neural networks and reinforcement learning to optimize decisions in dynamic environments.
problem Optimizing decisions in dynamic, context-aware environments like recommendation systems.
method Integrates a mixture of experts within a reinforcement learning framework to compute context-specific weights for decision-making.
result Enhanced efficiency and accuracy in Whittle index computation for each arm in RMABs.
New framework uses user feedback in CB problems for better decision-making.
problem Improving decision-making in contextual bandit problems with user-triggered feedback.
method Developed a new framework to leverage user-triggered feedback in CB problems, robust to feedback bias.
result Improved regret guarantees for CB algorithms using user feedback.
Greedy policies perform poorly in imperfectly observed contextual bandits.
problem Performance of Greedy policies in bandits with partially observed contexts.
method Analysis of Greedy reinforcement learning policies under imperfectly observed contextual bandits.
result Worst-case regret grows poly-logarithmically with the time horizon and the failure probability.