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Optimization tutorial
17 videos • 4,967 views • by Taylor Sparks
1
Traditional sampling techniques (grid vs random vs sobol vs latin hypercube)
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2
Comparing Bayesian optimization with traditional sampling
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3
Closed-loop optimization of inexpensive functions
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4
ML model tuning with constraints (CNN tuning for MNIST example)
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5
Batch optimization of expensive functions (i.e. simulations)
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6
Asynchronous multi-worker optimization
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7
Multi-objective optimization
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8
Continuous multi-fidelity optimization
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9
Discrete multi-fidelity optimization
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10
Offline optimization (experiments manually performed by humans)
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11
Mixed online offline multi-fidelity optimization (lab experiments guided by simulations)
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12
Closed-loop optimization with self-driving lab demo!
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13
Summary of Adaptive Experimentation Tutorial series
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14
Human-in-the-loop Bayesian Optimization
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15
Interactive sample transfer workflow with slack notifications
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16
Making complex Bayesian Optimization simple with Honegumi
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17
Honegumi tutorial #2. multiobjective polymer formulation
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