1. Yi Wu (CMU) Joint work with Vitaly Feldman (IBM) Venkat Guruswami (CMU) Prasad Ragvenhdra (MSR) TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A

3. 10 MillionLotteryCheapPharmacyJunkIs Spam YES NOYESNOSPAM NOYES NOYESNOT SPAM YES SPAM NO YESNOT SPAM YESNOYESNOYESNOT SPAM YES NOYESNOSPAM “10 Millon= yes” and “Lottery=yes” and “Pharmacy=yes” The Spam Problem

4. 10 MillionLotteryCheapPharmacyJunkIs Spam YES NOYESNOSPAM NOYES NOYESNOT SPAM YES SPAM NO YES NOT SPAM YESNOYESNOYESNOT SPAM YES NO SPAM If “10 Millon= NO” then Not SPAM Else If “Lottery = No” then Not Spam Else If “Pharmacy = No” then Not Spam Else SPAM The Spam Problem

5. 10 MillionLotteryCheapPharmacyJunkIs Spam YES NOYESNOSPAM NOYES NOYESNOT SPAM YES SPAM NO YES NOT SPAM YESNOYESNOYESNOT SPAM YES NO SPAM “Million= YES” + 2 “Lottery=YES”+ “Pharmacy = YES” ≥ 4 The Spam Problem

6. Halfspaces Conjunctions Decision List

7. Unknown distribution D over R n, examples labeled by an unknown function f. + - - - - + + + - - - - + + + + - After receiving examples, algorithm does its computation and outputs hypothesis h. + Accuracy of hypothesis is f h

8. Unknown distribution D over{0,1} n examples labeled by an unknown conjunctions. + - - - - + + + - - - - + + + + - + is easy! Since conjunctions is a special halfspaces, we can use poly-time linear programming to find a halfspace hypothesis consistent with all examples: Well-known theory (VC dimension)  for any D random sample of many examples yields -accurate hypothesis w.h.p.

9. Real-world data probably doesn’t come with guarantee that examples are labeled perfectly according to a conjunction. Linear programming is brittle: noisy examples can easily result in no consistent hypothesis. Motivates study of noisy variants of PAC learning for conjunctions. is easy!…but not very realistic… perfectly labeled ^ + - - - - + + + - - - - + + + + - + - + + -

10. Unknown distribution D over {0,1} n examples labeled by an unknown conjunction function f. All the random examples given to learner: –1- ε fraction of the example is perfectly labeled, i.e.x~D, y = f(x). –ε fraction of the example mislabeled. Goal: To find a good hypothesis that has good accuracy (close to 1- ε? Or just better than 50%?)

11.  No Noise: [Val84, Lit88, Hau88]: PAC Learnable  Random Noise: [Kea98]: PAC Learnable under random noise model.

12.  For any ε,δ > 0, NP-hard to tell whether ◦ Some conjunction consistent with 1- ε fraction of the data, ◦ No conjunction is ½ + δ consistent with the data. [FGKP06] It is NP-hard to find a 51%-accuracy conjunction even if knowing some conjunction is consistent with 99% of the data.

13.  Proper: Given f is in function class C (e.g. conjunctions), learner output a function in class C.  Non-Proper: Given f is in class C (e.g. conjunctions), learner can output function in the class D (e.g. halfspaces).

14.  We might still be able to learn conjunctions by outputing larger class of functions (say by linear programming?). ◦ E.g. [Lit88] use the winnow algorithm which output halfspace function.

15.  For any ε,δ > 0, NP-hard to tell whether ◦ Some halfspace consistent with 1- ε fraction of the data, ◦ No halfspace is ½ + δ consistent with the data. [FGKP, GR]. It is NP-hard to find a 51%-accuracy halfspaces even if knowing some halfspaces is consistent with 99% of the data.

16.  For any ε,δ > 0, NP-hard to tell whether ◦ Some conjunction consistent with 1- ε fraction of the data, ◦ No function in any hypothesis class is ½ + δ consistent with the data.

17.  [ABX08]: Showing NP-hardness using black- box reductions for unrestricted-class of improper learning is hard. ◦ It will otherwise break some long-standing cryptographic assumptions: (transformation from any average-case hard problem in NP to a one-way function)

19.  For any ε,δ > 0, NP-hard to tell whether ◦ Some conjunction consistent with 1- ε fraction of the data, ◦ No halfspaces is ½ + δ consistent with the data. It is NP-hard to find a 51%-accurate halfspaces even if knowing some conjunction is consistent with 99% of the data.

20. In practice, halfspace are at the heart of many learning algorithms:  Perceptron  Winnow  SVM  Logistic Regression  Linear Discriminant Analysis Learning Theory Computational We can not agnostically learn conjunctions using any of the above mentioned algorithm!

21. Halfspaces Conjunctions Decision List Weakly Agnostic learning Conjunctions/Decision Lists/Halfspaces by Halfspaces is hard!

24. ◦ “Dictator” (halfspaces depending on very few variables  e.g. f(x) = sgn(x 1 )) ◦ “Majority”(no variables has too much weight,  e.g. f(x) = sgn(x 1 +x 2 +x 3 +…+x n ). 24

25. chooses: x 2 {0,1} n, b 2 {0,1} from some distribution. x f(x) Completeness ¸ c $ all (Monomials) f(x) = x i accepted w. prob. ¸ c Soundness · s $ “Majority like function” accepted “w. prob. · s With such a test, we can show NP-hard to tell i) some monomial satisfies c fraction of the data; ii) no halfspaces satisfies more than s fraction of the data. Accept if f(x) = b Tester

26. 1) Generate z by setting each z i independently to be random bits. 1) Generate y by resetting each z i to be 0 with probability 0.99. 1) Generating a random bit b and setting x i to be y i + b/2 n. 2) Output (x,b) (Accept if f(x) = sgn(b)).

27. z = 0 0 y= 0 0 x = b/2 n random bit b

28.  f(x)= x i ◦ Then  Pr(f(x) =x i =b) > Pr(y i = 0) =0.99  f(x) = sgn ( ) ◦ Then  Pr( f(x) = b) = Pr(sgn (N(0, 0.1) + b /2 n ) =b)< 0.51

29.  We prove that even weakly agnostic learning Conjunctions by Halfspace is NP-hard.  To propose a efficient halfspace learning algorithm for conjunctions/decision lists/halfspaces, we need either modeling the distribution of example or the noise.

30.  Prove: For any ε,δ > 0, given a set of training examples such that there is a conjunction consistent with 1- ε fraction of the data, it is NP-hard to find a degree d polynomial threshold function that is ½ + δ consistent with the data. Why low degree ptf? Because such a hypothesis can agnostically learn conjunctions/halfspaces under uniform distribution.