A Machine Learning Framework for Programming by Example by Aditya Menon, UCSD/NICTA Santosh Vempala, Georgia Tech Omer Tamuz, Weizmann Sumit Gulwani, MSR Butler Lampson, MSR Adam Tauman Kalai, MSR The computer learns π from a few examples! Lawrence Carin (5) John D. Lafferty (4) Michael I. Jordan (4) Zoubin Ghahramani (4) Huan Xu (3) Ivor W. Tsang (3) Ambuj Tewari (3) Csaba Szepesvári (3) Masashi Sugiyama (3) Nathan Srebro (3) Bernhard Schölkopf (3) Mark D. Reid (3) Shie Mannor (3) Rong Jin (3) Ali Jalali (3) Hal Daumé III (3) Steven C. H. Hoi (3) Geoffrey E. Hinton (3) Arthur Gretton (3) David B. Dunson (3) David M. Blei (3) Yoshua Bengio (3) Peilin Zhao (2) Yaoliang Yu (2) Tianbao Yang (2) Zhixiang Eddie Xu (2) Min Xu (2) Eric P. Xing (2) Jialei Wang (2) Pascal Vincent (2) Prior work EBE [Nix85] Tourmaline [Mye93] TELS [WM93] Eager [Cyp93] Cima [Mau94] DEED [Fuj98] SmartEDIT [LWDW01] LAPIS [Miller02] FlashFill [Gulwani2011] [Liang-Jordan-Klein10] Sidestep the NP-hard search problem Sequential Transformations by Example Programming System STEPS: Each step defined by example inputβoutput Dong Yu, Frank Seide, Gang Li: Conversationa Nathan Parrish, Maya R. Gupta: Dimensionalit (Step 1) Dong Yu, Frank Seide, Gang Li Nathan Parrish, Maya R. Gupta STEPS: Each step defined by example inputβoutput Dong Yu, Frank Seide, Gang Li: Conversationa Dong Yu, Frank Seide, Gang Li Dong Yu (Step 2) (Step 1) Nathan Parrish, Maya R. Gupta: Dimensionalit Nathan Parrish, Maya R. Gupta Frank Seide Gang Li Nathan Parrish Maya R. Gupta x.Replace(/:.*$/gm,"") nputβoutput 1) Dong Yu, Frank Seide, Gang Li Nathan Parrish, Maya R. Gupta :.*$/gm,"") (Step 2) Dong Yu Frank Seide Gang Li Nathan Parrish Maya R. Gupta x.Replace(/, /gm,"\n") 2) Dong Yu Frank Seide Gang Li Nathan Parrish Maya R. Gupta /, /gm,"\n") (Step 3) Dong Yu (1) Frank Seide (1) Gang Li (1) Nathan Parrish (1) Maya R. Gupta (1) Count or append β (1)β? . Mock example 2) Dong Yu Frank Seide Gang Li Nathan Parrish Maya R. Gupta /, /gm,"\n") adam adam john nina nina adam (Step 3) adam (3) john (1) nina (2) (Step 4) Join("\n", ListCat(Dedup(Split(π₯, "\n")), " (", Dedup(Count(Split(π₯, "\n"), Split(π₯, "\n"))), ")")) adam (3) nina (2) john (1) Learning to Search for Programming by example Given strings π₯, π¦ β π, find βgoodβ π: π β π such that π π₯ = π¦ (Dynamic programming & genetic algorithms wonβt work) π₯ π¦ Peaches Bananas Pears Apples Apples Pears Bananas Peaches PCFG . .12 .06 .01 .01 .20 .10 .22 .12 .08 .04 π π π π β π₯ β Join(π·ππππ, ππΏππ π‘) β βPeachesβ β βBananasβ ... ππΏππ π‘ β Sort(ππΏππ π‘, πΆπππ) ππΏππ π‘ β Reverse(ππΏππ π‘) ππΏππ π‘ β Split(π, π·ππππ) ... π·ππππ β β\nβ π·ππππ β β β π·ππππ β π ... Join β\nβ Reverse Split π₯ β\nβ Learning to Search for Programming by example Given strings π₯, π¦ β π, find βgoodβ π: π β π such that π π₯ = π¦ Enumerate PCFG programs in order of likelihood. π₯ π¦ Peaches Bananas Pears Apples Apples Pears Bananas Peaches Trained on corpus of tasks from help forums PCFG . .12 .06 .01 .01 .20 .10 .22 .12 .08 .04 π π π π β π₯ β Join(π·ππππ, ππΏππ π‘) β βPeachesβ β βBananasβ ... ππΏππ π‘ β Sort(ππΏππ π‘, πΆπππ) ππΏππ π‘ β Reverse(ππΏππ π‘) ππΏππ π‘ β Split(π, π·ππππ) ... π·ππππ β β\nβ π·ππππ β β β π·ππππ β π ... Join β\nβ Reverse Split π₯ β\nβ The abstract MLE problem: Given dist. π over (π₯, π¦, data, π), find argmax Pr π|π₯, π¦, data π π The wrong MLE problem: Given π₯, π¦ β π, dist. π over π: π β π, find argmax Pr π ? π:π π₯ =π¦ π Which program is more likely under π β Remove from : to end of line Truncate each line to 29 characters Dong Yu, Frank Seide, Gang Li: Conversationa Nathan Parrish, Maya R. Gupta: Dimensionalit Dong Yu, Frank Seide, Gang Li Nathan Parrish, Maya R. Gupta The wrong MLE problem: Given π₯, π¦ β π, dist. π over π: π β π, find argmax Pr π ? π:π π₯ =π¦ π Which program is more likely under π Remove from : to end of line β Truncate each line to 29 characters /a-z/g /^$/ 24.2 18.5 Tr8 SP :-) :( 100% 0% /a-z/g /^$/ 24.2 18.5 Tr8 SP The abstract MLE problem: Given dist. ππ over (π₯, π¦, data, π), find argmax Pr π|π₯, π¦, data π Estimating system parameters π: π π (π) Given training corpus π₯ , π¦ , data , π Choose π to minimize: β π log Pr π π |π₯ π , π¦ π , data(π) + π π ππ using convex optimization [Vempala]. ππ π π π=1 2 Experimental results Baseline = equal weights (MDL) *Everything is in Javascript Conclusions β’ Programming by Example involves hard search problem β’ Search space generated by clues (features->CFG rules) β’ Learn weights on heuristic clues Future work β’ Learned shared structure (like [Liang-Jordan-Klein10]) β’ Generate more clues on-the-fly β’F
© Copyright 2024