Game Theory in Machine Learning, part 1 - Costantinos Daskalakis - MLSS 2020, Tübingen

Table of Contents (powered by https://videoken.com)
0:00:00 Game Theory in Machine Learning, part 1 - Costantinos Daskalakis - MLSS 2020, Tübingen
0:01:20 Game Theory and Machine Learning
0:02:52 State
0:03:39 Future of A/: learning strategic reasoning
0:04:34 Example 1: Platform Design
0:06:16 Example 2: Recommender Systems
0:07:05 Example 3: Humanoid vs Spider
0:08:37 A dictum
0:11:42 Our focus: min-max optimization
0:13:46 Generative Adversarial Networks (GAN
0:20:50 Training Oscillations: Handwritten Digits
0:21:44 Training Oscillations: even for Gaussian data/bilinear objectives!
0:26:32 Training Oscillations: persistence for variants of Online Gradient Descent
0:27:12 What gives?
0:27:58 Menu
0:34:39 The Min-Max Theorem
0:43:51 Min-Max and No-Regret Learning
0:48:34 [Code Walkthrough]
0:57:19 Training Oscillations
0:59:07 Menu
1:00:22 Gradient Descent w/ Negative Momentum
1:04:04 Negative Momentum: why might it help?
1:07:04 Training Oscillations
1:11:19 Negative Momentum: convergence
1:12:03 Negative Momentum: why might it help?
1:18:54 Negative Momentum: constrained case
1:25:41 Menu
1:27:18 constrained case
1:27:23 Remark 1: Beyond "Last-Iterate"
1:29:39 Remark 3: GAN learning
1:31:48 Thanks!
1:39:16 MLSS 2020 Team

Видео Game Theory in Machine Learning, part 1 - Costantinos Daskalakis - MLSS 2020, Tübingen канала virtual mlss2020