Online Learning

1. And Other Cool Stuff Online Learning Your guide: Avrim Blum Carnegie Mellon University [Machine Learning Summer School 2012]

2. Itinerary • Stop 1: Minimizing regret and combining advice. • Randomized Wtd Majority / Multiplicative Weights alg • Connections to game theory • Stop 2: Extensions • Online learning from limited feedback (bandit algs) • Algorithms for large action spaces, sleeping experts • Stop 3: Powerful online LTF algorithms • Winnow, Perceptron • Stop 4: Powerful tools for using these algorithms • Kernels and Similarity functions • Stop 5: Something completely different • Distributed machine learning

3. Powerful tools for learning:Kernels and Similarity Functions

4. Powerful tools for learning:Kernels and Similarity Functions

5. 2-minute version • Suppose we are given a set of images , and want to learn a rule to distinguish men from women. Problem: pixel representation not so good. • A powerful technique for such settings is to use akernel: a special kind of pairwise function K( , ). • Can think about & analyze kernels in terms of implicit mappings, building on margin analysis we just did for Perceptron (and similar for SVMs). • Can also directly analyze directly as similarity functions, building on analysis we just did for Winnow. [Balcan-B’06] [Balcan-B-Srebro’08]

6. + + + - + - - - - - + - - + - + + - - - Kernel functions and Learning • Back to our generic classification problem. E.g., given a set of images , labeled by gender, learn a rule to distinguish men from women. [Goal: do well on new data] • Problem: our best algorithms learn linear separators, but might not be good for data in its natural representation. • Old approach: use a more complex class of functions. • More recentapproach: use a kernel.

7. What’s a kernel? • A kernel K is a legal def of dot-product: fns.t. there exists an implicit mapping K such that K( , )=K( )¢K( ). • E.g., K(x,y) = (x ¢y+ 1)d. • K:(n-diml space) ! (nd-diml space). • Point is: many learning algs can be written so only interact with data via dot-products. • E.g., Perceptron: w = x(1) + x(2) – x(5) + x(9). w ¢ x = (x(1) + x(2) – x(5) + x(9)) ¢ x. • If replace x¢ywith K(x,y), it acts implicitly as if data was in higher-dimensional -space. Kernel should be pos. semi-definite (PSD)

8. z2 x2 X X X X X X X X X X X X O X X O O O X X O X x1 O O X O O z1 O O O O O X X O O X X X X X O X X X X z3 X X X X X X X Example • E.g., for the case of n=2, d=2, the kernel K(x,y) = (1 + x¢y)dcorresponds to the mapping:

9. + +  +  + + - + - - - - Moreover, generalize well if good margin • If data is lin. separable by margin  in F-space, then need sample size only Õ(1/2) to get confidence in generalization. • E.g., follows directly from mistake bound we proved for Perceptron. • Kernels found to be useful in practice for dealing with many, many different kinds of data. Assume |F(x)|· 1.

10. x y Moreover, generalize well if good margin But there is a little bit of a disconnect... • In practice, kernels constructed by viewing as a measure of similarity: K(x,y) 2 [-1,1], with some extra reqts. • But Theory talks about margins in implicit high-dimensional F-space. K(x,y) = F(x)¢F(y). • Can we give an explanation for desirable properties of a similarity function that doesn’t use implicit spaces? • And even remove the PSD requirement?

11. Goal: notion of “good similarity function” for a learning problem that… • Talks in terms of more intuitive properties (no implicit high-diml spaces, no requirement of positive-semidefiniteness, etc) • If K satisfies these properties for our given problem, then has implications to learning • Includes usual notion of “good kernel” (one that induces a large margin separator in F-space).

12. Defn satisfying (1) and (2): • Say have a learning problem P (distribution D over examples labeled by unknown target f). • Simfn K:(x,y)![-1,1] is (,)-good for P if at least a 1- fraction of examples x satisfy: Ey~D[K(x,y)|l(y)=l(x)] ¸Ey~D[K(x,y)|l(y)l(x)]+ Average similarity to points of the same label Average similarity to points of opposite label gap “most x are on average more similar to points y of their own type than to points y of the other type”

13. Defn satisfying (1) and (2): • Say have a learning problem P (distribution D over examples labeled by unknown target f). • Sim fn K:(x,y)![-1,1] is (,)-good for P if at least a 1- fraction of examples x satisfy: Ey~D[K(x,y)|l(y)=l(x)] ¸ Ey~D[K(x,y)|l(y)l(x)]+ Average similarity to points of the same label Average similarity to points of opposite label gap Note: it’s possible to satisfy this and not be PSD.

14. Defn satisfying (1) and (2): • Say have a learning problem P (distribution D over examples labeled by unknown target f). • Sim fn K:(x,y)![-1,1] is (,)-good for P if at least a 1- fraction of examples x satisfy: Ey~D[K(x,y)|l(y)=l(x)] ¸ Ey~D[K(x,y)|l(y)l(x)]+ Average similarity to points of the same label Average similarity to points of opposite label gap How can we use it?

15. How to use it At least a 1- prob mass of x satisfy: Ey~D[K(x,y)|l(y)=l(x)] ¸ Ey~D[K(x,y)|l(y)l(x)]+

16. How to use it At least a 1- prob mass of x satisfy: Ey~D[K(x,y)|l(y)=l(x)] ¸ Ey~D[K(x,y)|l(y)l(x)]+ • Proof: • For any given “good x”, prob of error over draw of S+,S- at most 2. • So, at most  chance our draw is bad on more than  fraction of “good x”. • With prob¸ 1-, error rate · + .

17. + + _ But not broad enough Avg simil to negs is ½, but to pos is only ½¢1+½¢(-½) = ¼. • K(x,y)=x¢y has good separator but doesn’t satisfy defn. (half of positives are more similar to negs that to typical pos)

18. + + _ But not broad enough • Idea: would work if we didn’t pick y’s from top-left. • Broaden to say: OK if 9 large region R s.t. most x are on average more similar to y2R of same label than to y2R of other label. (even if don’t know R in advance)

19. Broader defn… • Ask that exists a set R of “reasonable” y(allowprobabilistic) s.t.almost allxsatisfy Ey[K(x,y)|l(y)=l(x),R(y)]¸Ey[K(x,y)|l(y)l(x), R(y)]+ • Formally, say K is (,,)-good if have hinge-loss , and Pr(R+), Pr(R-) ¸. • Claim 1: this is a legitimate way to think about good (large margin) kernels: • If -good kernel then (t,2,t)-good here. • If -good here and PSD then -good kernel

20. Broader defn… • Ask that exists a set R of “reasonable” y(allowprobabilistic) s.t.almost allxsatisfy Ey[K(x,y)|l(y)=l(x),R(y)]¸Ey[K(x,y)|l(y)l(x), R(y)]+ • Formally, say K is (,,)-good if have hinge-loss , and Pr(R+), Pr(R-) ¸. • Claim 2: even if not PSD, can still use for learning. • So, don’t need to have implicit-space interpretation to be useful for learning. • But, maybe not with SVM/Perceptron directly…

21. How to use such a sim fn? • Ask that exists a set R of “reasonable” y(allowprobabilistic) s.t.almost allxsatisfy Ey[K(x,y)|l(y)=l(x),R(y)]¸Ey[K(x,y)|l(y)l(x), R(y)]+ • Draw S = {y1,…,yn}, n¼1/(2t). • View as “landmarks”, use to map new data: • F(x) = [K(x,y1), …,K(x,yn)]. • Whp, exists separator of good L1 margin in this space: w*=[0,0,1/n+,1/n+,0,0,0,-1/n-,0,0] • So, take new set of examples, project to this space, and run good L1alg (e.g., Winnow)! could be unlabeled

22. How to use such a sim fn? If K is (,,)-good, then can learn to error ’ = O() with O((1/(’2)) log(n)) labeled examples. • Whp, exists separator of good L1 margin in this space: w*=[0,0,1/n+,1/n+,0,0,0,-1/n-,0,0] • So, take new set of examples, project to this space, and run good L1alg (e.g., Winnow)!

23. Learning with Multiple Similarity Functions • Let K1, …, Kr be similarity functions s. t. some (unknown) convex combination of them is (,)-good. Algorithm • Draw S={y1, , yn} set of landmarks. Concatenate features. F(x) = [K1(x,y1), …,Kr(x,y1), …, K1(x,yn),…,Kr(x,yn)]. • Run same L1 optimization algorithm as before (or Winnow) in this new feature space.

24. Learning with Multiple Similarity Functions • Let K1, …, Kr be similarity functions s. t. some (unknown) convex combination of them is (,)-good. Algorithm • Draw S={y1, , yn} set of landmarks. Concatenate features. F(x) = [K1(x,y1), …,Kr(x,y1), …, K1(x,yn),…,Kr(x,yn)]. Guarantee:Whp the induced distribution F(P) in Rnrhas a separator of error · +  at L1 margin at least Sample complexity is roughly: O((1/(2)) log(nr)) Only increases by log(r) factor!

25. Learning with Multiple Similarity Functions • Interesting fact: because property defined in terms of L1, no change in margin. • Only log(r) penalty for concatenating feature spaces. • If L2, margin would drop by factor r1/2, giving O(r) penalty in sample complexity. • Algorithm is also very simple (just concatenate).

26. Applications/extensions • Bellet, A.; Habrard, A.; Sebban, M. ICTAI 2011:notion fits well with string edit similarities. • If use directly this way rather than converting to PSD kernel, comparable performance and models much sparser. (They use L1-normalized SVM). • Bellet, A.; Habrard, A.; Sebban, M. MLJ 2012, ICML 2012: efficient algorithms for learning (,,)-good similarity functions in different contexts.

27. Summary • Kernels and similarity functions are powerful tools for learning. • Can analyze kernels using theory of L2 margins, plug in to Perceptron or SVM • Can also analyze more general similarity fns(not nec. PSD) without implicit spaces, connectingwith L1 margins and Winnow, L1-SVM. • Second notion includes 1st notion as well (modulo some loss in parameters). • Potentially other interesting suffic. conditions too. E.g., [WangYangFeng07] motivated by boosting.

28. Itinerary • Stop 1: Minimizing regret and combining advice. • Randomized Wtd Majority / Multiplicative Weights alg • Connections to game theory • Stop 2: Extensions • Online learning from limited feedback (bandit algs) • Algorithms for large action spaces, sleeping experts • Stop 3: Powerful online LTF algorithms • Winnow, Perceptron • Stop 4: Powerful tools for using these algorithms • Kernels and Similarity functions • Stop 5: Something completely different • Distributed machine learning

29. Distributed PAC Learning Maria-Florina Balcan Avrim Blum Shai Fine Yishay Mansour Georgia Tech CMU IBM Tel-Aviv [In COLT 2012]

30. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations.

31. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • Click data

32. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • Customer data

33. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • Scientific data

34. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • Each has only a piece of the overall data pie

35. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • In order to learn over the combined D, holders will need to communicate.

36. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • Classic ML question: how much data is needed to learn a given class of functions well?

37. Distributed Learning Many ML problems today involve massive amounts of data distributed across multiple locations. • These settings bring up a new question: how much communication? • Plus issues like privacy, etc.

38. The distributed PAC learning model • Goal is to learn unknown function f 2 C given labeled data from some distribution D. • However, D is arbitrarily partitioned among k entities (players) 1,2,…,k. [k=2 is interesting] + + + - + - - -

39. The distributed PAC learning model • Goal is to learn unknown function f 2 C given labeled data from some distribution D. • However, D is arbitrarily partitioned among k entities (players) 1,2,…,k. [k=2 is interesting] • Players can sample (x,f(x)) from their own Di. D = (D1 + D2 + … + Dk)/k 1 2 … k D1 D2 … Dk

40. The distributed PAC learning model • Goal is to learn unknown function f 2 C given labeled data from some distribution D. • However, D is arbitrarily partitioned among k entities (players) 1,2,…,k. [k=2 is interesting] • Players can sample (x,f(x)) from their own Di. Goal: learn good h over D, using as little communication as possible. 1 2 … k D1 D2 … Dk

41. The distributed PAC learning model Interesting special case to think about: • K=2. • One has the positives and one has the negatives. • How much communication to learn, e.g., a good linear separator? 1 2 + + + + + + + + + + + + - - - - + + + + - - - - - - - - - - - -

42. The distributed PAC learning model Assume learning a class C of VC-dimension d. Some simple baselines. [viewing k << d] • Baseline #1: based on fact that can learn any class of VC-dim d to error ² from O(d/² log 1/²) samples • Each player sends 1/k fraction to player 1. • Player 1 finds consistent h 2 C, whp has error ·² with respect to D. Sends h to others. • Total: 1 round, O(d/² log 1/²) examples communic. D1 D2 … Dk

43. The distributed PAC learning model • Baseline #2: • Suppose C is learnable by an online algorithm A with mistake-bound M. • Player 1 runs A, broadcasts current hypothesis. • If any player has a counterexample, sends to player 1. Player 1 updates, re-broadcasts. • At most M examples and hypotheses communicated. D1 D2 … Dk

44. Dependence on 1/² Had linear dependence in d and 1/², or M and no dependence on 1/². • Can you get O(d log 1/²) examples of communication? • Yes! Distributed boosting. D1 D2 … Dk

45. Recap of Adaboost + + • Weak learning algorithm A. + + + + • For t=1,2, … ,T + - + • Construct on {, …, } - - - - - • Run A on producing - - • uniform on {, …, } • increases weight onif makes a mistake on ; decreases it on correct. Key points: • dependsonand normalization factor that can be communicated efficiently. • To achieve weak learning it suffices to use O(d) examples.

46. Distributed Adaboost Si Sj • Each player ihas a samplefrom • For t=1,2, … ,T wi,t wj,t Si Sj • Each player sends player 1, enough data to produce hypothesis of error ¼. [For t=1, O(d/k) examples each.] ht ht + ht + • Player 1 broadcasts to all other players. + + + • Each player i reweights its own distribution on using and sends the sum of its weightsto player 1. + + + - + - + - - - - - - (ht may do better on some than others) - - - - • Player 1 determines the #of samples to request next from each i[samples O(d) times from the multinomial given by /].

47. Distributed Adaboost Final result: • O(d) examples of communication per round + O(k log d) extra bits to send weights & request + 1 hypothesis sent per round • O(log 1/²) rounds of communication. • So, O(d log 1/²) examples of communication in total plus low order extra info.

48. Agnostic learning Recent result of [Balcan-Hanneke] gives robust halving alg that can be implemented in distributed setting. • Get error 2 OPT(C) + ² using total of only O(k log|C| log(1/²)) examples. • Not computationally efficient in general, but says O(log(1/²)) possible in principle.

49. Can we do better for specific classes of interest? E.g., conjunctions over {0,1}d. f(x) = x2x5x9x15. • These generic methods give O(d) examples, or O(d2) bits total. Can you do better? • Again, thinking of k << d.

50. Can we do better for specific classes of interest? E.g., conjunctions over {0,1}d. f(x) = x2x5x9x15. • These generic methods give O(d) examples, or O(d2) bits total. Can you do better? • Sure: each entity intersects its positives. Sends to player 1. 1101111011010111 1111110111001110 1100110011001111 1100110011000110 • Player 1 intersects & broadcasts.