Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

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任意

Exploring the?Training Robustness of?Distributional Reinforcement Learning Against Noisy State Obserimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning?(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the con

令人心醉

自由職業(yè)者

Label Shift Quantification with?Robustness Guarantees via?Distribution Feature Matchingframework, distribution feature matching (DFM), that recovers as particular instances various estimators introduced in previous literature. We derive a general performance bound for DFM procedures, improving in several key aspects upon previous bounds derived in particular cases. We then extend this

Peculate

Antioxidant

DualMatch: Robust Semi-supervised Learning with?Dual-Level Interactiong methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning not robust. In this paper, we propose a novel SSL method called DualMatch, in which the clas

使閉塞

Crepitus

Deep Imbalanced Time-Series Forecasting via?Local Discrepancy Densitypite their scarce occurrences in the training set (., data imbalance), abrupt changes incur loss that significantly contributes to the total loss (., heteroscedasticity). Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal state

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