Imitation learning uses data for training policies to solve complex tasks.

Datasets often suffer severe selection bias; clinical labels are only available on patients for whom doctors ordered medical exams. To assess model performance outside the support of available data, we present a computational framework for adaptive labeling, providing cost-efficient model evaluations under severe distribution shifts. We formulate the problem as a Markov Decision Process over states defined by posterior beliefs on model performance. Each batch of new labels incurs a "state transition" to sharper beliefs, and we choose batches to minimize uncertainty on model performance at the end of the label collection process. Instead of relying on high-variance REINFORCE policy gradient estimators that do not scale, our adaptive labeling policy is optimized using path-wise policy gradients computed by auto-differentiating through simulated roll-outs. Our framework is agnostic to different uncertainty quantification approaches and highlights the virtue of planning in adaptive labeling. On synthetic and real datasets, we empirically demonstrate even a one-step lookahead policy substantially outperforms active learning-inspired heuristics.

Dating expert names the seemingly innocent'five word phrase' that's a huge red flag at ANY relationship stage If you've ever been on the receiving end of the line, 'It just wasn't meant to be,' you may have chalked it up to bad timing or a relationship mismatch. But according to one of Australia's leading dating experts, those five innocent words can reveal far more about someone's mindset than you may have thought and are a sure sign of a relationship that was never built to last. Perth-based matchmaker Louanne Ward, who has worked in the industry for more than 30 years, said she has noticed a striking pattern among clients who remain single the longest, date extensively and struggle to sustain long-term partnerships. 'The clients who stayed single longest, dated the most people and struggled to make anything last, described their past relationships the same way,' she explained. It wasn't meant to be.'

Their algorithm has a time and space complexity of O ( dk) .

We study how tail properties of the base distribution of a NEF impose limits on the NEF: if the base distribution is subexponential (subgaussian), we show that the NEF is self-concordant with a stretch factor that grows inverse quadratically (respectively, linearly) 2.

SDNist are intended as tools to encourage investigation and discussion of deiden-tification algorithms, and they are not intended or suitable for product evaluation. The National Institute of Standards and Technology does not endorse any algorithm included in these resources.