Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function.

In Distributional Reinforcement Learning (D-RL) [Bellemare et al., 2023], an agent aims to estimate Sutton and Barto, 2018], where the objective is to predict the expected return only. In Section 3, we answer this methodological question, showing that it is possible to reformulate Policy Evaluation in a distributional setting so that its performance index is explicitly intertwined with the representation of the (state or action) spaces.

We provide the derivation of the upper implicit gradient in eq. We summarize the whole DTG-AuxL algorithm in Algorithm 1, where the lower and upper optimization updates are conducted alternatingly. We use the batch stochastic gradient optimization for both the lower and upper update. STL: It is a natural baseline where we only train on the primary task. Equal: It is a multi-task learning method, where we assign an equal weight of 1.0 to the loss of each MAXL can be only applied to the classification problem.

In linear regression, interpolating the square loss is equivalent to interpolating many other losses (such as the absolute loss) on the training set. Similarly, in the context of linear classification, many works (Soudry et al. 2018; Ji and Telgarsky 2019; Muthukumar et al. 2021) have shown that optimizing

Meta learning is a promising paradigm to enable skill transfer across tasks.

Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the "sign flipping probability" of random projections.

Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm.

To meaningfully regularize the model's decision boundary, CODA introduces virtual unknown classes