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Feature Learning for Interpretable, Performant Decision Trees
Points were sampled uniformly in the bands denoted by dashed lines. We posit that these barriers are due, at least in part, to the sensitivity of decision trees to transformations of the input resulting from greedy construction and simple decision rules. Of these, key limitation is the latter; even if we replace greedy construction with a perfect tree learner, simple distributions can nonetheless require an arbitrarily large axis-aligned tree to fit.
Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
We further provide numerical simulations to corroborate our theoretical findings.
Differentiable sorting for censored time-to-event data
This bias becomes particularly noticeable when the study's endpoint can be inferred from the
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development
Despite diverse modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations.
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess.
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to make real-time predictions on streaming data in non-stationary environments.
