Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased. While previous work tackles this issue with domain-specific knowledge or explicit supervision on the spuriously correlated attributes, we instead tackle a more challenging setting where such information is unavailable. To this end, we first observe that neural networks learn to rely on the spurious correlation only when it is "easier" to learn than the desired knowledge, and such reliance is most prominent during the early phase of training. Based on the observations, we propose a failure-based debiasing scheme by training a pair of neural networks simultaneously. Our main idea is twofold; (a) we intentionally train the first network to be biased by repeatedly amplifying its "prejudice", and (b) we debias the training of the second network by focusing on samples that go against the prejudice of the biased network in (a). Extensive experiments demonstrate that our method significantly improves the training of network against various types of biases in both synthetic and real-world datasets. Surprisingly, our framework even occasionally outperforms the debiasing methods requiring explicit supervision of the spuriously correlated attributes.Preprint. Under review.
We study contextual multi-armed bandit problems under linear realizability on rewards and uncertainty (or noise) on features. For the case of identical noise on features across actions, we propose an algorithm, coined NLinRel, having O T 7 8 log (dT ) + K √ d regret bound for T rounds, K actions, and d-dimensional feature vectors. Next, for the case of non-identical noise, we observe that popular linear hypotheses including NLinRel are impossible to achieve such sub-linear regret. Instead, under assumption of Gaussian feature vectors, we prove that a greedy algorithm has O T 2 3 √ log d regret bound with respect to the optimal linear hypothesis. Utilizing our theoretical understanding on the Gaussian case, we also design a practical variant of NLinRel, coined Universal-NLinRel, for arbitrary feature distributions. It first runs NLinRel for finding the 'true' coefficient vector using feature uncertainties and then adjust it to minimize its regret using the statistical feature information. We justify the performance of Universal-NLinRel on both synthetic and real-world datasets.
2.5 ) and 0.9 (IQR 0.7-1.0), respectively, proving a statistically significant reduction compared to baseline values (p[0.048 and 0.0051, respectively). Median DSUVmax -22% and DSUVratio resulted-30%. There was a statistically significant correlation of DSUVmax and PSA response at 12 months post-HIFU (p[0.021) (Figure).CONCLUSIONS: PSMA-PET may be a useful tool for monitoring response in PCa patients undergoing HIFU FT. Moreover, the SUV variation can predict biochemical response at 12 months posttreatment completion.
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