arXiv research

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

iDAD uses neural networks to quickly adapt experiments without likelihoods.

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

DAD learns to design experiments quickly, outperforming traditional methods.

New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.

JADAI optimizes design and inference for parameter estimation.

Study designs for estimating treatment effects in adaptive experiments.

DABS uses a policy network to select experiments in high-dimensional design spaces.

Step-DAD improves BED by periodically updating a design policy during experiments.

Paper proposes a new method to optimize robot body structure and control policy.

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…

Paper optimizes experimental design for estimating treatment effect.

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

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

GBS uses machine learning to design products based on consumer preferences.

Study on adaptivity constraints in linear contextual bandits with optimal design.

In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cu…

Adaptive IP approach optimizes intervention design for causal graph recovery.

Adaptive calibration improves model accuracy with fewer simulations.

Syntax designs adaptive trials for subpopulations with potential benefits.

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…

New algorithm for adaptive experimental design in scientific settings.

Algorithm identifies Pareto optimal designs efficiently for noisy, multi-objective functions.

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…

Adaptive querying learns user psychometrics with AI personas.

This study optimizes covariate density and propensity score for efficient ATE estimation.

Bayesian optimization reduces hyperparameters for mixed variable design problems.

Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals…

Adaptive PCR improves panel data analysis with uniform guarantees.

Shared Keyboard design improves phase I clinical trials by borrowing information across doses.

New robust control method for uncertain systems using bootstrapped noise.

Entropy-based GP adaptive design improves failure probability estimation.

TAD efficiently finds optimal settings for advanced manufacturing.

Learn to automatically plug domain-specific modules into a common network.

Proposes a flexible tournament design combining knockout and round-robin.

Study optimizes experimental design for best treatment arm identification.

Study adaptive clinical trial methods for identifying patient subpopulations with treatment benefit.

Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges are exacerbated when our parameter of interest differs from the pa…

We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problem-dependent randomization. We first present a series of new regret guarantees that hold at any time and under …

Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often un…

In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiri…

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

Automates design of lightweight neural networks for image classification.

Proposes novel losses for fine-grained categorical domain adaptation.

DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.

The paper develops classifiers that encourage positive adaptation in machine learning settings.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.