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PangWei Koh and Percy Liang,

ICML2017 Best paper

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- 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um UNDERSTANDING BLACK-BOX PRED -ICTION VIA INFLUENCE FUNCTIONS 1
- 2. TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) ICML2017 best paper https://youtu.be/0w9fLX_T6tY
- 3. QUESTIONS Terry Taewoong Um (terry.t.um@gmail.com) • How can we explain the predictions of a black-box model? • Why did the system make this prediction? • How can we explain where the model came from? • What would happen if the values of a training point where slightly changed?
- 4. INTERPRETATION OF DL RESULTS Terry Taewoong Um (terry.t.um@gmail.com) • Retrieving images that maximally activate a neuron [Girshick et al. 2014] • Finding the most influential part from the image [Zhou et al. 2016] • Learning a simpler model around a test point [Ribeiro et al. 2016] But, they assumed a fixed model My NN is a function of training inputs
- 5. INFLUENCE OF A TRAINING POINT Terry Taewoong Um (terry.t.um@gmail.com) • What is the influence of a training example for the model (or for the loss of a test example)? Optimal model param. : Model param. by training w/o z : Model param. by upweighting z : without z == (𝜖 = − 1 𝑛 ) • The influence of upweighting z on the parameters 𝜃
- 6. INFLUENCE OF A TRAINING POINT • Influence vs. Euclidean distance
- 7. INFLUENCE OF A TRAINING POINT Terry Taewoong Um (terry.t.um@gmail.com) • The influence of upweighting z on the parameters 𝜃 • The influence of upweighting z on the loss at a test point
- 8. PERTURBING A TRAINING POINT Terry Taewoong Um (terry.t.um@gmail.com) • Move 𝜖 mass from 𝑧 to 𝑧 𝛿 • If x is continuous and 𝛿 is small • The effect of 𝑧 𝑧 𝛿 on the loss at a test point
- 9. SUMMARY Terry Taewoong Um (terry.t.um@gmail.com) • The influence of 𝑧 𝑧 𝛿 on the loss at a test point • The influence of upweighting z on the parameters 𝜃 • The influence of upweighting z on the loss at a test point
- 10. EXAMPLE Terry Taewoong Um (terry.t.um@gmail.com) • The influence of upweighting z • In logistic regression, • Test : 7, Train : 7 (green), 1 (red)
- 11. SEVERAL PROBLEMS Terry Taewoong Um (terry.t.um@gmail.com) • Calculation of Use Hessian-vector products (HVPs) precompute 𝑠𝑡𝑒𝑠𝑡 by optimizing or sampling-based approximation
- 12. SEVERAL PROBLEMS Terry Taewoong Um (terry.t.um@gmail.com) • What if is non-convex, so H < 0 Assuming that is a local minimum point, define a quadratic loss Then calculate using the above empirically working! • Influence function vs. retraining
- 13. SEVERAL PROBLEMS Terry Taewoong Um (terry.t.um@gmail.com) • What if is non-differentiable? e.g.) hinge loss Use a differentiable variation of the hinge loss
- 14. APPLICATIONS Terry Taewoong Um (terry.t.um@gmail.com) • Understanding model behavior
- 15. APPLICATIONS Terry Taewoong Um (terry.t.um@gmail.com) • Adversarial examples c.f.) The effect of 𝑧 𝑧 𝛿 on the loss at a test point
- 16. APPLICATIONS Terry Taewoong Um (terry.t.um@gmail.com) • Debugging domain mismatch
- 17. APPLICATIONS Terry Taewoong Um (terry.t.um@gmail.com) • Fixing mislabeled examples

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