Case Study 1: Estimating Click Probabilities. Kakade Announcements: Project Proposals: due this Friday!

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1 Case Study 1: Estimating Click Probabilities Intro Logistic Regression Gradient Descent + SGD Machine Learning for Big Data CSE547/STAT548, University of Washington Sham Kakade April 4, Announcements: Project Proposals: due this Friday! One page HW1 posted today. (starting NEXT week) TA office hours Readings: please do them. Today: Review: logistic regression, GD, SGD Hashing and Sketching Kakade 017 1

2 Machine Learning for Big Data (CSE 547 / STAT 548) ( what is big data anyways?) Ad Placement Strategies Companies bid on ad prices Which ad wins? (many simplifications here) Naively: But: Instead: 4

3 Learning Problem for Click Prediction Prediction task: Features: Data: Batch: Online: Many approaches (e.g., logistic regression, SVMs, naïve Bayes, decision trees, boosting, ) Focus on logistic regression; captures main concepts, ideas generalize to other approaches 5 Logistic Regression n Learn P(Y X) directly Assume a particular functional form Sigmoid applied to a linear function of the data: Logistic function (or Sigmoid): Z Features can be discrete or continuous! 6 3

4 Maximizing Conditional Log Likelihood = X j! dx dx y j (w 0 + w i x j i ) ln 1+exp(w 0 + w i x j i ) i=1 i=1 Good news: l(w) is concave function of w, no local optima problems Bad news: no closed-form solution to maximize l(w) Good news: concave functions easy to optimize 7 Gradient Ascent for LR Gradient ascent algorithm: iterate until change < e (t) For i = 1,,d, (t) repeat 8 4

5 Regularized Conditional Log Likelihood If data are linearly separable, weights go to infinity Leads to overfitting à Penalize large weights Add regularization penalty, e.g., L : `(w) =ln Y j P (y j x j, w)) w Practical note about w 0 : 9 Standard v. Regularized Updates Maximum conditional likelihood estimate (t) Regularized maximum conditional likelihood estimate 3 w = arg max ln 4 Y X P (y j x j, w)) 5 wi w j i>0 (t) 10 5

6 Stopping criterion `(w) =ln Y j P (y j x j, w)) w Regularized logistic regression is strongly concave Negative second derivative bounded away from zero: Strong concavity (convexity) is super helpful!! For example, for strongly concave l(w): `(w ) `(w) apple 1 r`(w) 11 Convergence rates for gradient descent/ascent Number of iterations to get to accuracy `(w ) If func l(w) Lipschitz: O(1/ϵ ) `(w) apple If gradient of func Lipschitz: O(1/ϵ) If func is strongly convex: O(ln(1/ϵ)) 1 6

7 Challenge 1: Complexity of computing gradients What s the cost of a gradient update step for LR??? (t) 13 Challenge : Data is streaming Assumption thus far: Batch data But, click prediction is a streaming data task: User enters query, and ad must be selected: Observe x j, and must predict y j User either clicks or doesn t click on ad: Label y j is revealed afterwards Google gets a reward if user clicks on ad Weights must be updated for next time: 14 7

8 Learning Problems as Expectations Minimizing loss in training data: Given dataset: Sampled iid from some distribution p(x) on features: Loss function, e.g., hinge loss, logistic loss, We often minimize loss in training data: `D(w) = 1 NX `(w, x j ) N j=1 However, we should really minimize expected loss on all data: Z `(w) =E x [`(w, x)] = p(x)`(w, x)dx So, we are approximating the integral by the average on the training data 15 Gradient Ascent in Terms of Expectations True objective function: `(w) =E x [`(w, x)] = Taking the gradient: Z p(x)`(w, x)dx True gradient ascent rule: How do we estimate expected gradient? 16 8

9 SGD: Stochastic Gradient Ascent (or Descent) True gradient: r`(w) =E x [r`(w, x)] Sample based approximation: What if we estimate gradient with just one sample??? Unbiased estimate of gradient Very noisy! Called stochastic gradient ascent (or descent) Among many other names VERY useful in practice!!! 17 Stochastic Gradient Ascent: General Case Given a stochastic function of parameters: Want to find maximum Start from w (0) Repeat until convergence: Get a sample data point x t Update parameters: Works in the online learning setting! Complexity of each gradient step is constant in number of examples! In general, step size changes with iterations 18 9

10 Stochastic Gradient Ascent for Logistic Regression Logistic loss as a stochastic function: E x [`(w, x)] = E x ln P (y x, w) Batch gradient ascent updates: 8 < w (t+1) i w (t) i + : w(t) i + 1 NX N Stochastic gradient ascent updates: Online setting: n w (t+1) i w (t) i + t w (t) i j=1 9 = i [y (j) P (Y =1 x (j), w (t) )] ; x (j) w o + x (t) i [y (t) P (Y =1 x (t), w (t) )] 19 Theorem: Convergence Rate of SGD (see CSE546 notes and readings) Let f be a strongly convex stochastic function Assume gradient of f is Lipschitz continuous Then, for step sizes: The expected loss decreases as O(1/t^0.5): 0 10

11 Convergence Rates for Gradient Descent/Ascent vs. SGD Number of Iterations to get to accuracy Gradient descent: If func is strongly convex: O(ln(1/ϵ)) iterations Stochastic gradient descent: If func is strongly convex: O(1/ϵ) iterations Seems exponentially worse, but much more subtle: Total running time, e.g., for logistic regression: Gradient descent: SGD: `(w ) `(w) apple SGD can win when we have a lot of data See readings for more details 1 What you should know about Logistic Regression (LR) and Click Prediction Click prediction problem: Estimate probability of clicking Can be modeled as logistic regression Logistic regression model: Linear model Gradient ascent to optimize conditional likelihood Overfitting + regularization Regularized optimization Convergence rates and stopping criterion Stochastic gradient ascent for large/streaming data Convergence rates of SGD 11