Our analysis exposes significant gaps between current Deep MIL algorithms, highlighting thetheoretical conditions that must besatisfied by MIL algorithms to ensure instance-level learnability.

However, when merging a parameter that is influential for one model but redundant (i.e.

Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.

Eq. (4) gives the causal operator.1.2 The set C represents the smallest set for which Eq. (2) holds. The conditions in Theorem 3.1 allow for full transfer across all intervention functions in This is equivalent to sampling from the mutilated graph. We compute the integrals in Eqs. Finally, we fix the variance in the likelihood of Eq.

The model proposed here enables proper uncertainty quantification of the causal effects thus allowing the definition of optimal experimental design strategies.

LLMs are computationally expensive to pre-train due to their large scale.

One the one hand, the classification performance will be promoted. F ollow the settings of Proof A.2 and Eq.6, we can get the following relationship of the Hence we can generalize Eq.A.9 to: y According to Eq.A.11, it's easy to find a derivative zero point in range [1,C]. F ollow the Basic Setting of Proof.1, suppose It has limited contribution for the tail class' feature LT scenarios, the likelihood is consistent in the train and test set, but the prior is different. Hence, the actual optimization direction is not described as Eq.B.2 because the bias incurred by According to LemmaB.1, we immediately deduce that Bayias-compensated cross-entropy loss ensures All above re-weight methods are proven effective empirically, more or less. They propose the LDAM loss to encourage the tail classes to enjoy larger margins.