Research map / Learning theory and algorithms / Combinatorial and search optimization
Multi-Objective Self-Paced Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize loss and self-paced regularizer as two objectives with MOEA/D (MOSPL) instead of greedy SPL with increasing pace | self-paced learning · Hollywood2 | MAP (%): 63.720 (SPL optimal) / 60.595 (SPL stable) -> 64.662 | Mixed |
| use MOSPL instead of standard batch training | kernel SVM · Hollywood2 | MAP (%): 58.164 -> 64.662 | Helped |
| MOSPL vs SPL on stable (final-iteration) result in matrix factorization | SPL with LS loss · Synthetic MF, noise [-20,20] and (0,0.01) | RMSE: 8.1222 -> 1.6170 | Helped |
| MOSPL vs SPL tuned to optimal pace in matrix factorization | SPL with LS loss · Synthetic MF, noise [-20,20] and (0,0.01) | RMSE: 1.7837 -> 1.6170 | No clear effect |
| MOSPL vs robust MF baselines RegL1ALM, CWM, MoG | matrix factorization · Synthetic MF, noise [-20,20] and (0,0.01) | RMSE: 2.7574 (CWM) -> 1.6170 | Helped |
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