arXiv research

New algorithm for sequential off-policy learning improves performance over batch methods.

The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.

New method optimizes decision-making in uncertain environments.

Heterogeneous SVO leads to diverse policies in sequential social dilemmas.

Bayesian sOED uses PG reinforcement learning for efficient experiment design.

New method estimates policy performance under unobserved confounding.

Study optimizes portfolio allocation policies using off-policy data and constraints.

This paper simplifies OPE in large state spaces using state abstractions.

New algorithm reduces regret in sequential decision-making problems.

The paper develops methods to estimate optimal treatment sequences under policy constraints.

New method for robust policy evaluation in offline reinforcement learning with sequentially exogenous unobserved confounders.

Study evaluates reinforcement learning algorithms for sequential experimental design.

This research improves interpretability in sequential explanations using mental models.

In treatment allocation problems the individuals to be treated often arrive sequentially. We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes. At the outset, the total number of treatment assign…

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construc…

New method for evaluating sequential recommendations with lower variance.

DiPS learns to optimize sketching policies for better recommendation quality.

This paper proposes a method to evaluate policies using quantile metrics, improving upon existing mean-based approaches.

The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.

Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution can then be constructed b…

SNAPO optimizes policies for complex sequential decisions using differentiable simulation.

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

PyCFRL helps ensure fair reinforcement learning policies from offline data.

The paper addresses treatment recommendation problems by optimizing distributional characteristics.

Framework for deferring decisions to experts in sequential medical settings.

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of $n$ actions, given some partial observations. It has been shown that in many applicat…

We connect a broad class of generative models through their shared reliance on sequential decision making. Motivated by this view, we develop extensions to an existing model, and then explore the idea further in the context of data imputation -- perhaps the simplest setting in which to investigate the relation between …

Combines experimental and historical data for robust policy evaluation.

RL teaches agents to make decisions through trial and error.

Paper uses RL to optimize ICU load during COVID-19.

vsOED optimizes experiment design with reinforcement learning for Bayesian models.

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

New reinforcement learning bound improves generalization for sequential data.

New method for estimating value of optimal policies in uncertain scenarios.

Paper proposes a new method for sampling from complex distributions.

Paper proposes a sequential statistical test for comparing imitation learning policies with near-optimal stopping.

We study the problem of learning sequential decision-making policies in settings with multiple state-action representations. Such settings naturally arise in many domains, such as planning (e.g., multiple integer programming formulations) and various combinatorial optimization problems (e.g., those with both integer pr…

New methods improve robust decision-making under uncertainty in off-policy evaluation.

New algorithm reduces complexity in multi-agent reinforcement learning.

Efficiently selects top-m designs for various contexts using sequential sampling.

Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper is to construct confidence intervals…

This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier within the policy in the form of its response to a designated sequence of state transitions, while incurring minimal impact on the nominal pe…

Optimal online data collection for semiparametric inference reduces regret.

We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose differ…

Study efficient resource allocation for detecting extreme values.

Efficient RL method uses SSL for small labeled data in health outcomes.

SEEK algorithm selects minimal state in reinforcement learning for better policy learning.

Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mo…