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CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling for HCI

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Automated Nonlinear Regression Modeling for HCI

CHI 2014 talk by Antti Oulasvirta

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CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling for HCI

1. 1. Automated Nonlinear Regression Modeling for HCI Antti Oulasvirta Max Planck Institute for Informatics and Saarland University Saarbrücken Germany
2. 2. I have data!
3. 3. I have data! I need a model! This Note contributes a method that supports model acquisition in HCI
8. 8. Current tools offer poor support Equation Evaluation
9. 9. Exploration is inefﬁcient and laborious The set of all possible models deﬁned by your task Unexplored model space
10. 10. Exploration is inefﬁcient and laborious The set of all possible models deﬁned by your task Unexplored model space
11. 11. We propose automated model search Best models Automated model search Dataset It builds on work in symbolic programming [6,15] Constraints
12. 12. We propose automated model search Best models Automated model search Dataset It builds on work in symbolic programming [6,15] Generate Test Constraints
13. 13. Iterative search in a model space y, X = {x1, ..., xm} Dependent variable Predictor variables y = β1f1(X) + ... + βnfn(X) Winner
14. 14. Iterative search in a model space y, X = {x1, ..., xm} Dependent variable Predictor variables y = β1x1 + ... + βnxm Start y = β1f1(X) + ... + βnfn(X) Winner
15. 15. Iterative search in a model space y, X = {x1, ..., xm} Dependent variable Predictor variables y = β1x1 + ... + βnxm Start y = β1f1(X) + ... + βnfn(X) Winner y = β1(xl ¤ xk) + ... + βnxm Transform/ Drop Iterate Fitness function
16. 16. Iterative search in a model space y, X = {x1, ..., xm} Dependent variable Predictor variables y = β1x1 + ... + βnxm Start y = β1f1(X) + ... + βnfn(X) Winner y = β1(xl ¤ xk) + ... + βnxm Transform/ Drop Iterate Fitness function Algebraic Exponential Logarithmic Trigonometric Presently 16 transformations
17. 17. Stochastic search method The set of all possible models deﬁned by your task
18. 18. Stochastic search method The set of all possible models deﬁned by your task
19. 19. Command line operation
20. 20. Command line operation Dataset
21. 21. Command line operation Dataset
22. 22. Command line operation Dataset
23. 23. Multiple controls offered Your model space
24. 24. Multiple controls offered Max. number of free parameters Transformations:Types, Number per term Seed equation Constraints to the model space Your model space
25. 25. Multiple controls offered Max. number of free parameters Transformations:Types, Number per term Seed equation Constraints to the model space Stochasticity Fitness function (e.g., R2,AIC, BIC) Search process Local search depth Your model space
26. 26. Does it work??
27. 27. Case 1. Comparison with 11 existing models in literature
28. 28. Case 1. Comparison with 11 existing models in literature Mouse pointing Two-thumb tapping ... Menu selection D,W ID, Telapsed B,I,D,W,Fr
29. 29. Case 1. Comparison with 11 existing models in literature Mouse pointing Two-thumb tapping ... Menu selection D,W ID, Telapsed B,I,D,W,Fr More predictors, observations, model terms
30. 30. Case 1. Comparison with 11 existing models in literature Mouse pointing Two-thumb tapping ... Menu selection D,W ID, Telapsed B,I,D,W,Fr Improvements to ﬁtness found in 7 out of 11 cases. Comparable model ﬁtness in others. More predictors, observations, model terms
31. 31. Baseline This paper # Dataset Predictors⇤ n k Model provided in paper R2 ⇤⇤ Best model found⇤⇤⇤ R2 1 Stylus tapping (1 oz)[8] A,W 16 2 a + b log2(2A/W) .966 a + b log2(A/W) .966 2 Reanalyzed data [8] A,We a + b log2(A/We + 1) .987 a + b(log2(log2 A) We) .981 3 Mouse pointing [8] A,W 16 2 a + b log2(A/W + 1) .984 a + b log2(A/W) .973 4 A,We a + b log2(A/We + 1) .980 a + b log10(A/We) .978 5 Trackball dragging [8] A,W 16 2 a + b log2(A/W + 1) .965 a + b log2(A (W3)4) .981 6 A,We a + b log2(A/We + 1) .817 a + b(A/(1 elog10 We )) .941 7 Magic lens pointing [13] A,W, S 16 3 a + b log2(D/S + 1) + c log2(S/2/A) .88 a + b(1 1/A) + cW9 .947 8 Tactile guidance [7] N,I,D 16 3 Eq. 8-9, nonlinear .91, .95 Nonlinear (k = 3) .980 9 Pointing, angular [3] Exp. 2 W, H, ↵, A 310 4 Eq. 33, IDpr, nonlinear .953 Nonlinear (k = 4) .962 10 Two thumb tapping[11] ID,Telapsed 20 6 Eq. 5-6, quadratic .79 a + b(T2 elapsed/ID) .929 11 Menu selection[2] B,I,D,W,Fr 10 6 Eq. 1-7, nonlinear .99,.52 Nonlinear (k = 6) .990 Table 1. Benchmarking automatic modeling against previously published models of response time in HCI. Notes: n = Number of observations (data rows); k = Number of free parameters; * All variable names from the original papers, except I is interface type (dummy coded); ** = As reported in the paper; *** = Some equations omitted due to space restrictions to ﬁxed terms. A second is deciding on a meaningful ﬁt- ness score – we currently use R2 , but this can be changed to cross-validation metrics. A third is model diagnostics. For instance, the use of OLS assumes collinearity and homoge- nous error variance [9]. The latter is probably an unrealistic assumption in many HCI datasets. Analytics are needed to examine the consequences. Fourthly, the equations are not 1. Pointing datasets 1–6 provide the least room to improve, since the R2 s are high to begin with. 2. The method is more successful when there are more predic- tors. The improvements obtained for datasets 7–11 range from small (8, 9, and 11) to medium (7) to large (10). Constraining of model exploration See the full table in the paper
32. 32. Baseline This paper # Dataset Predictors⇤ n k Model provided in paper R2 ⇤⇤ Best model found⇤⇤⇤ R2 1 Stylus tapping (1 oz)[8] A,W 16 2 a + b log2(2A/W) .966 a + b log2(A/W) .966 2 Reanalyzed data [8] A,We a + b log2(A/We + 1) .987 a + b(log2(log2 A) We) .981 3 Mouse pointing [8] A,W 16 2 a + b log2(A/W + 1) .984 a + b log2(A/W) .973 4 A,We a + b log2(A/We + 1) .980 a + b log10(A/We) .978 5 Trackball dragging [8] A,W 16 2 a + b log2(A/W + 1) .965 a + b log2(A (W3)4) .981 6 A,We a + b log2(A/We + 1) .817 a + b(A/(1 elog10 We )) .941 7 Magic lens pointing [13] A,W, S 16 3 a + b log2(D/S + 1) + c log2(S/2/A) .88 a + b(1 1/A) + cW9 .947 8 Tactile guidance [7] N,I,D 16 3 Eq. 8-9, nonlinear .91, .95 Nonlinear (k = 3) .980 9 Pointing, angular [3] Exp. 2 W, H, ↵, A 310 4 Eq. 33, IDpr, nonlinear .953 Nonlinear (k = 4) .962 10 Two thumb tapping[11] ID,Telapsed 20 6 Eq. 5-6, quadratic .79 a + b(T2 elapsed/ID) .929 11 Menu selection[2] B,I,D,W,Fr 10 6 Eq. 1-7, nonlinear .99,.52 Nonlinear (k = 6) .990 Table 1. Benchmarking automatic modeling against previously published models of response time in HCI. Notes: n = Number of observations (data rows); k = Number of free parameters; * All variable names from the original papers, except I is interface type (dummy coded); ** = As reported in the paper; *** = Some equations omitted due to space restrictions to ﬁxed terms. A second is deciding on a meaningful ﬁt- ness score – we currently use R2 , but this can be changed to cross-validation metrics. A third is model diagnostics. For instance, the use of OLS assumes collinearity and homoge- nous error variance [9]. The latter is probably an unrealistic assumption in many HCI datasets. Analytics are needed to examine the consequences. Fourthly, the equations are not 1. Pointing datasets 1–6 provide the least room to improve, since the R2 s are high to begin with. 2. The method is more successful when there are more predic- tors. The improvements obtained for datasets 7–11 range from small (8, 9, and 11) to medium (7) to large (10). Constraining of model exploration See the full table in the paper Baseline This paper in paper R2 ⇤⇤ Best model found⇤⇤⇤ W) .966 a + b log2(A/W) . We + 1) .987 a + b(log2(log2 A) We) . W + 1) .984 a + b log2(A/W) . We + 1) .980 a + b log10(A/We) . W + 1) .965 a + b log2(A (W3)4) . We + 1) .817 a + b(A/(1 elog10 We )) . + 1) + c log2(S/2/A) .88 a + b(1 1/A) + cW9 . ar .91, .95 Nonlinear (k = 3) . onlinear .953 Nonlinear (k = 4) . c .79 a + b(T2 elapsed/ID) . ar .99,.52 Nonlinear (k = 6) . ls of response time in HCI. Notes: n = Number of observations (dat
33. 33. But my data is more complex!
34. 34. Case 2: Complex dataset Multitouch-rotation data n of parameters: Angle, shown in figure below). display with tablet in Po- Dependent variable: MT Predictors: Angle, Diameter, X position,Y position, Direction [Hoggan et al. Proc. CHI’13]
35. 35. Case 2: Complex dataset Multitouch-rotation data n of parameters: Angle, shown in figure below). display with tablet in Po- Dependent variable: MT Predictors: Angle, Diameter, X position,Y position, Direction [Hoggan et al. Proc. CHI’13] and R2 = 0.835. However, the method also foun with seven free parameters and R2 = 0.827. Also, model, with four parameters and R2 = 0.805, was a + bx1 + c cos x3 2 e cos 1 x2 0 log10(x1⇥x3) + d tan x3 Here, variables x0, ..., x3 refer to x-position, y-po gle, and diameter, respectively. Further analysis i R2=0.805
36. 36. But the models don’t make sense!
37. 37. Case 3:Theoretically motivated operations Dataset Model
38. 38. Case 3:Theoretically motivated operations Dataset Model type (dummy coded); ** = As reported in provide the least room to improve, to begin with. ccessful when there are more predic- ts obtained for datasets 7–11 range 1) to medium (7) to large (10). exploration ted modeling, we took Dataset 11 mations (1/x, log2(x), ⇤, /, +, ) to e original paper. Many models were ee parameters and R2 = 0.90. tasets with a single model eling multiple datasets with a single pointing papers, the model terms are ameters ﬁtted per dataset. We tested covering three datasets (1, 3, and 5) Theoretically motivated operations
39. 39. But I need a model for MANY datasets!
40. 40. Case 4: Multiple datasets, one model Dataset 3 Dataset 1 Dataset 2 Model
41. 41. Conclusion & Discussion • Proof-of-concept • Model identiﬁcation by deﬁning constraints • Supports different modeling tasks in HCI • Promising results • Limitations and open questions • E.g., assumptions of nonlinear modeling (see paper) • “Brute force” approach • Warning against “ﬁshing”! • Future work: performance and expressive controls
42. 42. Project homepage (code forthcoming!) http://www.mpi-inf.mpg.de/~oantti/nonlinearmodeling/ Acknowledgements: This research was funded by the Max Planck Centre for Visual Computing and Communication and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University. antti.oulasvirta@aalto.ﬁ Automated Nonlinear Regression Modeling for HCI Take-away: •Model identiﬁcation by constraint deﬁnition