Experimental Design
147 papers with code • 0 benchmarks • 0 datasets
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OpenBox: A Generalized Black-box Optimization Service
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
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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
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
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
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
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
We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements.
Attention is not not Explanation
sarahwie/attention
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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
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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
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.
