arXiv research

Study evaluates reinforcement learning algorithms for sequential experimental design.

CRPS improves GP-based sequential design for chemical space.

Novel Hilbert space Gaussian process improves sequential design accuracy and efficiency.

vsOED optimizes experiment design with reinforcement learning for Bayesian models.

DAD learns to design experiments quickly, outperforming traditional methods.

PASOA optimizes Bayesian design by improving SMC samplers and EIG.

New approach optimizes decisions based on uncertainty in predictions.

Unified framework for robust A/B testing under model misspecification.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…

Novel framework optimizes experiments for implicit models using mutual information.

Adaptive batching improves Gaussian process surrogates for noisy level set estimation.

Paper proposes a new method for efficient hyperparameter optimization.

Bayesian sOED uses PG reinforcement learning for efficient experiment design.

This study improves audit sampling by using sequential procedures with statistical guarantees.

We use deep reinforcement learning to optimize experimental designs efficiently.

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

IFH models graph generation with adjustable sequentiality.

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

Gradient-free framework for Bayesian experimental design in complex systems.

A computer code can simulate a system's propagation of variation from random inputs to output measures of quality. Our aim here is to estimate a critical output tail probability or quantile without a large Monte Carlo experiment. Instead, we build a statistical surrogate for the input-output relationship with a modest …

This study proposes an efficient surrogate for Darcy flow inverse problems.

The chapter compares Gaussian process models for stochastic simulators with varying noise.

Gaussian process (GP) models have become a well-established frameworkfor the adaptive design of costly experiments, and notably of computerexperiments. GP-based sequential designs have been found practicallyefficient for various objectives, such as global optimization(estimating the global maximum or maximizer(s) of a …

We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given $L \ge 2$ response surfaces over a continuous input space $\cal X$, the aim is to efficiently find the index of the minimal response across the entire $\cal X$. The response surfaces are not known and ha…

New method uses diffusion models for Bayesian inverse problems.

Optimized parallel algorithms for identifying strong ties in data.

Novel approach to Bayesian experimental design for non-exchangeable data.

New method optimizes experiments under constraints.

GO-CBED optimizes experiments for specific causal queries, improving efficiency.

Paper introduces IO-NPF for efficient Bayesian experimental design.

Boosted GFlowNets improve exploration by sequentially training GFlowNets with residual rewards.

ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.

Study designs for estimating treatment effects in adaptive experiments.

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The fi…

The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar objects. By comparing the observations from a source at different frequencies with template models, astronomers are able to infer the type of t…

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…

PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and efficient learning of point process models. It can be used for interpretable sequential data modeling and analysis, e.g., Granger causality analysis of multi-variate point processes, point process-based simulation and prediction of…

Improved bounds for unbounded losses using transductive priors.

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.

Combines multi-fidelity and asynchronous batch methods for faster experimental design.

Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…

A new model for sequential prediction handles adversarial examples by allowing abstention.

Adapts MBDOE for real-time parameter estimation in complex systems.

Sequential tests for nonparametric hypotheses using supermartingales.

New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.

Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.

We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimental units while updating treatment assignment probabilities based on past data. We start by defining the efficient treatment-assignment prob…