The theoretical results highlight atrade-offbetween localization ofthe statistical risk and convergence speed to the long-term risk target.

Machine learning has seen many successful achievements in recent years.

Adversarial attacks ofteninvolverandom perturbations oftheinputsdrawnfrom uniform or Gaussian distributions, e.g., to initialize optimization-based whitebox attacks or generate update directions in black-box attacks. These simple perturbations, however, could be sub-optimal as they are agnostic to the model being attacked.

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks.

The internet age relies on lossy compression algorithms that transmit information at low bitrates. These algorithms are typically analysed through the rate-distortion trade-off, originally posited by Shannon[33].

Value factorisation is a useful technique for multi-agent reinforcement learning (MARL) in global reward game, however, its underlying mechanism is not yet fully understood.

Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e.