1. Something for almost nothing: Advances in sublinear time algorithms Ronitt Rubinfeld MIT and Tel Aviv U.

2. Algorithms for REALLY big data

3. No time What can we hope to do without viewing most of the data?

4. Small world phenomenon The social network is a graph: – “node’’ is a person – “edge’’ between people that know each other “6 degrees of separation’’ Are all pairs of people connected by path of distance at most 6?

5. Vast data Impossible to access all of it Accessible data is too enormous to be viewed by a single individual Once accessed, data can change

6. The Gold Standard Linear time algorithms: – for inputs encoded by n bits/words, allow cn time steps (constant c) Inadequate…

7. What can we hope to do without viewing most of the data? Can’t answer “for all” or “exactly” type statements: are all individuals connected by at most 6 degrees of separation? exactly how many individuals on earth are left-handed? Compromise? is there a large group of individuals connected by at most 6 degrees of separation? approximately how many individuals on earth are left-handed?

8. What types of approximation?

9. Property Testing Does the input object have crucial properties? Example Properties: Clusterability, Small diameter graph, Close to a codeword, Linear or low degree polynomial function, Increasing order Lots and lots more…

10. “In the ballpark” vs. “out of the ballpark” tests Property testing: Distinguish inputs that have specific property from those that are far from having that property Benefits: – Can often answer such questions much faster – May be the natural question to ask When some “noise” always present When data constantly changing Gives fast sanity check to rule out very “bad” inputs (i.e., restaurant bills) Model selection problem in machine learning

11. Examples Can test if a function is a homomorphism in CONSTANT TIME [Blum Luby R.] Can test if the social network has 6 degrees of separation in CONSTANT TIME [Parnas Ron]

12. Find characterization of property that is Efficiently (locally) testable Robust - objects that have the property satisfy characterization, and objects far from having the property are unlikely to PASS Constructing a property tester: Usually the bigger challenge

13. A “bad” testing characterization: ∀x,y f(x)+f(y) = f(x+y) Another bad characterization: For most x f(x)+f(1) = f(x+1) Good characterization: For most x,y f(x)+f(y) = f(x+y) Example: Homomorphism property of functions

14. A “bad” testing characterization: For every node, all other nodes within distance 6. Another bad one: For most nodes, all other nodes within distance 6. Good characterization: For most nodes, there are many other nodes within distance 6. Example: 6 degrees of separation

15. An example in depth

16. Monotonicity of a sequence Given: list y 1 y 2... y n Question: is the list sorted? Clearly requires n steps – must look at each y i

17. Monotonicity of a sequence Given: list y 1 y 2... y n Question: can we quickly test if the list close to sorted?

18. What do we mean by ``quick’’? query complexity measured in terms of list size n Our goal (if possible): Very small compared to n, will go for clog n

19. What do we mean by “close’’? Definition: a list of size n is  -close to sorted if can delete at most.01n values to make it sorted. Otherwise,.99-far. Sorted: 1 2 4 5 7 11 14 19 20 21 23 38 39 45 Close: 1 4 2 5 7 11 14 19 20 39 23 21 38 45 1 4 5 7 11 14 19 20 23 38 45 Far: 45 39 23 1 38 4 5 21 20 19 2 7 11 14 1 4 5 7 11 14 Requirements for algorithm: pass sorted lists if list passes test, can change at most.01 fraction of list to make it sorted

20. Monotonicity of a sequence Given: list y 1 y 2... y n Question: can we quickly test if the list close to sorted? i.e., (1) pass sorted lists and (2) if passes test, can change at most  fraction of list to make it sorted Can test in O(1/  log n) time [Ergun, Kannan, Kumar, Rubinfeld, Viswanathan] best possible [EKKRV] + [Fischer]

21. An attempt: Proposed algorithm : Pick random i and test that y i ≤y i+1 Bad input type: 1,2,3,4,5,…j, 1,2,3,4,5,….j, 1,2,3,4,5,…j, 1,2,3,4,5,…,j Difficult for this algorithm to find “breakpoint” But other algorithms work well i yiyi

22. A second attempt: Proposed algorithm: Pick random i<j and test that y i ≤y j Bad input type: n/m groups of m elements m,m-1,m-2,…,1, 2m,2m-1,2m-2,…m+1, 3m,3m-1,3m-2,…, must pick i,j in same group need at least (n/m) 1/2 choices to do this i yiyi

23. A test that works The test: (for distinct y i ) Test several times: Pick random i Look at value of y i Do binary search for y i Does the binary search find any inconsistencies? If yes, FAIL Do we end up at location i? If not FAIL Pass if never failed Running time: O(log n) time Why does this work? If list is in order, then test always passes If the test passes on choice i and j, then y i and y j are in correct order Since test usually passes, most y i ’s in the right order

24. Many more properties studied! Graphs, functions, point sets, strings, … Amazing characterizations of problems testable in graph and function testing models!

25. Properties of functions Linearity and low total degree polynomials [Blum Luby R.] [Bellare Coppersmith Hastad Kiwi Sudan] [R. Sudan] [Arora Safra] [Arora Lund Motwani Sudan Szegedy] [Arora Sudan]... Functions definable by functional equations – trigonometric, elliptic functions [R.] All locally characterized affine invariant function classes! [Bhattacharyya Fischer Hatami Hatami Lovett] Groups, Fields [Ergun Kannan Kumar R. Viswanathan] Monotonicity [EKKRV] [Goldreich Goldwasser Lehman Ron Samorodnitsky] [Dodis Goldreich Lehman Ron Raskhodnikova Samorodnitsky][Fischer Lehman Newman Raskhodnikova R. Samorodnitsky] [Chakrabarty Seshadri]… Convexity, submodularity [Parnas Ron R.] [Fattal Ron] [Seshadri Vondrak]… Low complexity functions, Juntas [Parnas Ron Samorodnitsky] [Fischer Kindler Ron Safra Samorodnitsky] [Diakonikolas Lee Matulef Onak R. Servedio Wan]…

26. Linear and low degree polynomials, trigonometric functions [Ergun Kumar Rubinfeld] [Kiwi Magniez Santha] [Magniez]… Some properties testable even with approximation errors!

27. Properties of sparse and general graphs Easily testable dense and hyperfinite graph properties are completely characterized! [Alon Fischer Newman Shapira][Newman Sohler] General Sparse graphs: bipartiteness, connectivity, diameter, colorability, rapid mixing, triangle free,… [Goldreich Ron] [Parnas Ron] [Batu Fortnow R. Smith White] [Kaufman Krivelevich Ron] [Alon Kaufman Krivelevich Ron]… Tools: Szemeredi regularity lemma, random walks, local search, simulate greedy, borrow from parallel algorithms

28. Some other combinatorial properties Set properties – equality, distinctness,... String properties – edit distance, compressibility,… Metric properties – metrics, clustering, convex hulls, embeddability... Membership in low complexity languages – regular languages, constant width branching programs, context-free languages, regular tree languages… Codes – BCH and dual-BCH codes, Generalized Reed- Muller,…

29. Yes! e.g., [Buhrman Fortnow Newman Rohrig] [Friedl Ivanyos Santha] [Friedl Santha Magniez Sen] Can quantum property testers do better?

30. May only query labels of points from a sample Useful for model selection problem Active property testing [Balcan Blais Blum Yang]

31. “Traditional” approximation Output number close to value of the optimal solution (not enough time to construct a solution) Some examples: Minimum spanning tree, vertex cover, max cut, positive linear program, edit distance, …

32. Given graph G(V,E), a vertex cover (VC) C is a subset of V such that it “touches” every edge. What is minimum size of a vertex cover? NP-complete Poly time multiplicative 2-approximation based on relationship of VC and maximal matching Example: Vertex Cover

33. Vertex Cover and Maximal Matching

34. “Classical” approximation examples Can get CONSTANT TIME approximation for vertex cover on sparse graphs! Output y which is at most 2 ∙ OPT + ϵn How? Oracle reduction framework [Parnas Ron] Construct “oracle” that tells you if node u in 2-approx vertex cover Use oracle + standard sampling to estimate size of cover But how do you implement the oracle?

35. Implementing the oracle – two approaches: Sequentially simulate computations of a local distributed algorithm [Parnas Ron] Figure out what greedy maximal matching algorithm would do on u [Nguyen Onak]

36. Greedy algorithm for maximal matching

37. Implementing the Oracle via Greedy To decide if edge e in matching: Must know if adjacent edges that come before e in the ordering are in the matching Do not need to know anything about edges coming after Arbitrary edge order can have long dependency chains! Odd or even steps from beginning? 1 2 4 8 25 36 47 88 89 110 111 112 113

38. Breaking long dependency chains [Nguyen Onak] Assign random ordering to edges Greedy works under any ordering Important fact: random order has short dependency chains

39. Better Complexity for VC Always recurse on least ranked edge first Heuristic suggested by [Nguyen Onak] Yields time poly in degree [Yoshida Yamamoto Ito] Additional ideas yield query complexity nearly linear in average degree for general graphs [Onak Ron Rosen R.]

40. Further work More complicated arguments for maximum matching, set cover, positive LP… [Parnas Ron + Kuhn Moscibroda Wattenhofer] [Nguyen Onak] [Yoshida Yamamoto Ito] Even better results for hyperfinite graphs [Hassidim Kelner Nguyen Onak][Newman Sohler] e.g., planar Can dependence be made poly in average degree?

41. No samples What if data only accessible via random samples?

42. Distributions

43. Play the lottery?

44. Is the lottery unfair? From Hitlotto.com: Lottery experts agree, past number histories can be the key to predicting future winners.

45. True Story! Polish lottery Multilotek Choose “uniformly” at random distinct 20 numbers out of 1 to 80. Initial machine biased e.g., probability of 50-59 too small Past results: http://serwis.lotto.pl:8080/archiwum/wyniki_wszystkie.php?id_gra=2

46. Thanks to Krzysztof Onak (pointer) and Eric Price (graph)

47. New Jersey Pick 3,4 Lottery New Jersey Pick k ( =3,4) Lottery. Pick k digits in order. 10 k possible values. Assume lottery draws iid Data: Pick 3 - 8522 results from 5/22/75 to 10/15/00  2 - test gives 42% confidence Pick 4 - 6544 results from 9/1/77 to 10/15/00. fewer results than possible values  2 - test gives no confidence

48. Distributions on BIG domains Given samples of a distribution, need to know, e.g., entropy number of distinct elements “shape” (monotone, bimodal,…) closeness to uniform, Gaussian, Zipfian… No assumptions on shape of distribution i.e., smoothness, monotonicity, Normal distribution,… Considered in statistics, information theory, machine learning, databases, algorithms, physics, biology,…

49. Key Question How many samples do you need in terms of domain size? Do you need to estimate the probabilities of each domain item? Can sample complexity be sublinear in size of the domain? Rules out standard statistical techniques, learning distribution

50. Our Aim: Algorithms with sublinear sample complexity

51. Similarities of distributions Are p and q close or far? p is given via samples q is either known to the tester (e.g. uniform) given via samples

52. Is p uniform? p Test samples Pass/Fail?

53. Upper bound for L 2 distance [Goldreich Ron]

54. Testing uniformity [GR, Batu et. al.] Upper bound: Estimate collision probability and use known relation between between L 1 and L 2 norms Issues: Collision probability of uniform is 1/n Use O(sqrt(n)) samples via recycling Comment: [P] uses different estimator Easy lower bound:  (n ½ ) Can get  (n ½ /  2 ) [P]

55. Back to the lottery… plenty of samples!

56. Is p uniform? Theorem: ([Goldreich Ron][Batu Fortnow R. Smith White] [Paninski] ) Sample complexity of distinguishing p=U from |p-U| 1 >  is  (n 1/2 ) Nearly same complexity to test if p is any known distribution [Batu Fischer Fortnow Kumar R. White]: “Testing identity” p Test samples Pass/Fail?

57. Testing identity via testing uniformity on subdomains: (Relabel domain so that q monotone) Partition domain into O(log n) groups, so that each group almost “flat” -- differ by <(1+  ) multiplicative factor q close to uniform over each group Test: – Test that p close to uniform over each group – Test that p assigns approximately correct total weights to each group q (known)

58. Transactions of 20-30 yr oldsTransactions of 30-40 yr olds Testing closeness of two distributions: trend change?

59. Testing closeness Theorem: ([BFRSW] [P. Valiant] ) Sample complexity of distinguishing p=q from ||p-q|| 1 >  is  (n 2/3 ) p Test Pass/Fail? q ~

60. Why so different? Collision statistics are all that matter Collisions on “heavy” elements can hide collision statistics of rest of the domain Construct pairs of distributions where heavy elements are identical, but “light” elements are either identical or very different

61. Output ||p-q|| 1 ±  need  (n/log n) samples [G. Valiant P. Valiant] Additively estimate distance?

62. Collisions tell all Algorithms: Algorithms use collisions to determine “wrong” behavior E.g., too many collisions implies far from uniform [GR,BFSRW] Use Linear Programming to determine if there is a distribution with the right collision probabilities and the right property [G. Valiant P. Valiant] Lower bounds: For symmetric properties, collision statistics are only relevant information [BFRSW] (see also [Orlitsky Santhanam Zhang] [Orlitsky Santhanam Viswanthan Zhang]) Need new analysis tools since not independent Central limit theorem for generalized multinomial distributions [G. Valiant P. Valiant]

63. Information theoretic quantities Entropy Support size

64. Information in neural spike trails Each application of stimuli gives sample of signal (spike trail) Entropy of (discretized) signal indicates which neurons respond to stimuli Neural signals time [Strong, Koberle, de Ruyter van Steveninck, Bialek ’98]

65. Compressibility of data

66. Can we get multiplicative approximations? In general, no….  0 entropy distributions are hard to distinguish What if entropy is bigger? Can  -multiplicatively approximate the entropy with Õ(n 1/  2 ) samples (when entropy >2  /  ) [Batu Dasgupta R. Kumar] requires  (n 1/  2 ) [Valiant] better bounds when support size is small [Brautbar Samorodnitsky] Similar bounds for estimating support size [Raskhodikova Ron R. Smith] [Raskhodnikova Ron Shpilka Smith]

67. More properties: Independence: [Batu Fischer Fortnow Kumar R. White] Limited Independence: [Alon Andoni Kaufman Matulef R. Xie] [Haviv Langberg] K-flat distributions [Levi Indyk R.] K-modal distributions [Daskalakis Diakonikolas Servedio] Poisson Binomial Distributions [Daskalakis Diakonikolas Servedio] Monotonicity over general posets [Batu Kumar R.] [Bhattacharyya Fischer R. P. Valiant] Properties of multiple distributions [Levi Ron R.]

68. Many other properties to consider! Higher dimensional flat distributions Mixtures of k Gaussians “Junta”-distributions …

69. What about joint properties of many distributions?

70. Some questions (and answers): Are they all equal? Can they be clustered into k groups of similar distributions? Do they all have the same mean? See [Levi Ron R. 2011, Levi Ron R. 2012]

71. Getting past the lower bounds Special distributions e.g, uniform on a subset, monotone Other query models Queries to probabilities of elements Other distance measures [Guha McGregor Venkatasubramanian] Competitive classification/closeness testing -- compare to best symmetric test [Acharya Das Jafarpour Orlitsky Pan Suresh] [Acharya Das Jafarpour Orlitsky Pan]

72. More open directions Other properties? Non-iid samples?

73. For many problems, we need a lot less time and samples than one might think! Many cool ideas and techniques have been developed Lots more to do! Conclusion:

74. Thank you!