Experimental Design

137 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

OpenBox: A Generalized Black-box Optimization Service

PKU-DAIR/open-box 1 Jun 2021

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning

clvoloshin/COBS 15 Nov 2019

We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

lamourj/fastcode-gpucb 21 Dec 2009

Many applications require optimizing an unknown, noisy function that is expensive to evaluate.

Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach

2019020826/SDSMCMC 13 Jul 2012

Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such methods rely on simple, local models or on ad hoc inference approaches lacking experimental interpretability.

Pitfalls and Best Practices in Algorithm Configuration

mlindauer/GenericWrapper4AC 17 May 2017

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning).

Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

opencobra/cobratoolbox 11 Oct 2017

This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.

Optimal design of experiments to identify latent behavioral types

shakty/optimal-design 10 Jul 2018

We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements.

Attention is not not Explanation

sarahwie/attention IJCNLP 2019

We show that even when reliable adversarial distributions can be found, they don't perform well on the simple diagnostic, indicating that prior work does not disprove the usefulness of attention mechanisms for explainability.

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

pytorch/botorch NeurIPS 2020

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

With Little Power Comes Great Responsibility

dallascard/NLP-power-analysis EMNLP 2020

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.