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Eric Laber

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7 papers
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7

RLC Conference 2025 Conference Paper

Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits

  • Piotr M. Suder
  • Eric Laber

Information-directed sampling (IDS) is a powerful framework for solving bandit problems which has shown strong results in both Bayesian and frequentist settings. However, frequentist IDS, like many other bandit algorithms, requires that one have prior knowledge of a (relatively) tight upper bound on the norm of the true parameter vector governing the reward model in order to achieve good performance. Unfortunately, this requirement is rarely satisfied in practice. As we demonstrate, using a poorly calibrated bound can lead to significant regret accumulation. To address this issue, we introduce a novel frequentist IDS algorithm that iteratively refines a high-probability upper bound on the true parameter norm using accumulating data. We focus on the linear bandit setting with heteroskedastic subgaussian noise. Our method leverages a mixture of relevant information gain criteria to balance exploration aimed at tightening the estimated parameter norm bound and directly searching for the optimal action. We establish regret bounds for our algorithm that do not depend on an initially assumed parameter norm bound and demonstrate that our method outperforms state-of-the-art IDS and UCB algorithms.

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RLJ Journal 2025 Journal Article

Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits

  • Piotr M. Suder
  • Eric Laber

Information-directed sampling (IDS) is a powerful framework for solving bandit problems which has shown strong results in both Bayesian and frequentist settings. However, frequentist IDS, like many other bandit algorithms, requires that one have prior knowledge of a (relatively) tight upper bound on the norm of the true parameter vector governing the reward model in order to achieve good performance. Unfortunately, this requirement is rarely satisfied in practice. As we demonstrate, using a poorly calibrated bound can lead to significant regret accumulation. To address this issue, we introduce a novel frequentist IDS algorithm that iteratively refines a high-probability upper bound on the true parameter norm using accumulating data. We focus on the linear bandit setting with heteroskedastic subgaussian noise. Our method leverages a mixture of relevant information gain criteria to balance exploration aimed at tightening the estimated parameter norm bound and directly searching for the optimal action. We establish regret bounds for our algorithm that do not depend on an initially assumed parameter norm bound and demonstrate that our method outperforms state-of-the-art IDS and UCB algorithms.

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UAI Conference 2024 Conference Paper

Hidden Population Estimation with Indirect Inference and Auxiliary Information

  • Justin Weltz
  • Eric Laber
  • Alexander Volfovsky

Many populations defined by illegal or stigmatized behavior are difficult to sample using conventional survey methodology. Respondent Driven Sampling (RDS) is a participant referral process frequently employed in this context to collect information. This sampling methodology can be modeled as a stochastic process that explores the graph of a social network, generating a partially observed subgraph between study participants. The methods currently used to impute the missing edges in this subgraph exhibit biased downstream estimation. We leverage auxiliary participant information and concepts from indirect inference to ameliorate these issues and improve estimation of the hidden population size. These advances result in smaller bias and higher precision in the estimation of the study participant arrival rate, the sample subgraph, and the population size. Lastly, we use our method to estimate the number of People Who Inject Drugs (PWID) in the Kohtla-Jarve region of Estonia.

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NeurIPS Conference 2023 Conference Paper

Experimental Designs for Heteroskedastic Variance

  • Justin Weltz
  • Tanner Fiez
  • Alexander Volfovsky
  • Eric Laber
  • Blake Mason
  • Houssam Nassif
  • Lalit Jain

Most linear experimental design problems assume homogeneous variance, while the presence of heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$ that can be probed to receive noisy linear responses of the form $y=x^{\top}\theta^{\ast}+\eta$. Here $\theta^{\ast}\in \mathbb{R}^d$ is an unknown parameter vector, and $\eta$ is independent mean-zero $\sigma_x^2$-sub-Gaussian noise defined by a flexible heteroskedastic variance model, $\sigma_x^2 = x^{\top}\Sigma^{\ast}x$. Assuming that $\Sigma^{\ast}\in \mathbb{R}^{d\times d}$ is an unknown matrix, we propose, analyze and empirically evaluate a novel design for uniformly bounding estimation error of the variance parameters, $\sigma_x^2$. We demonstrate this method on two adaptive experimental design problems under heteroskedastic noise, fixed confidence transductive best-arm identification and level-set identification and prove the first instance-dependent lower bounds in these settings. Lastly, we construct near-optimal algorithms and demonstrate the large improvements in sample complexity gained from accounting for heteroskedastic variance in these designs empirically.

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RLDM Conference 2019 Conference Abstract

Parameterized Exploration

  • Jesse Clifton
  • Lili Wu
  • Eric Laber

We introduce Parameterized Exploration (PE), a simple family of methods for model-based tun- ing of the exploration schedule in sequential decision problems. Unlike common heuristics for exploration, our method accounts for the time horizon of the decision problem as well as the agent’s current state of knowledge of the dynamics of the decision problem. We show our method as applied to several common exploration techniques has superior performance relative to un-tuned counterparts in Gaussian multi-armed bandits, as well as a Markov decision process based on a mobile health (mHealth) study. We also examine the effects of model accuracy on the performance of PE.

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RLDM Conference 2017 Conference Abstract

Sufficient Markov Decision Processes with Alternating Deep Neural Networks

  • Longshaokan Wang
  • Eric Laber
  • Katie Witkiewitz

Advances in mobile computing technologies have made it possible to monitor and apply data- driven interventions across complex systems in real time. Markov decision processes (MDPs) are the pri- mary model for sequential decision problems with a large or indefinite time horizon. Choosing a repre- sentation of the underlying decision process that is both Markov and low-dimensional is non-trivial. We propose a method for constructing a low-dimensional representation of the original decision process for which: 1. the MDP model holds; 2. a decision strategy that maximizes cumulative reward when applied to the low-dimensional representation also maximizes cumulative reward when applied to the original process. We use a deep neural network to define a class of potential process representations and estimate the process of lowest dimension within this class. The method is evaluated using a suite of simulation experiments, and applied to data from a mobile health intervention targeting smoking and heavy episodic drinking among college students.

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RLDM Conference 2015 Conference Abstract

Online, semi-parametric estimation of optimal treatment allocations for the control of emerg- ing epidemics

  • Eric Laber

A key component in controlling the spread of an epidemic is deciding where, when, and to whom to apply an intervention. Here, we conceptualize the epidemic as spreading across nodes in an network. A treatment allocation strategy formalizes this process as a sequence of functions, one per treatment period, that map up-to-date information on the epidemic to a subset of nodes to receive treatment. An optimal treatment allo- cation strategy minimizes the expectation of some cumulative measure of harm, e. g. , the number of infected individuals, the geographic footprint of the disease, the estimated total cost of the disease, or a composite outcome weighing several important measures. One approach to estimating an optimal allocation strategy is to model the underlying disease dynamics and then use to simulation—optimization. However, constructing a high-quality estimator of the complete system dynamics is difficult especially in the context of emerging epidemics where there is little scientific theory to inform a class of models. We derive estimating equations for the optimal allocation strategy that does not require a model the system dynamics. Furthermore, be- cause this estimator does not require simulation of the disease process it is computationally tractable even for very large problems. We demonstrate the proposed methodology using data on the spread of white-nose syndrome in bats.

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