arXiv:2505.21626v2 Announce Type: replace-cross Abstract: In scientific machine learning, models are routinely deployed with parameter values or boundary conditions far from those used in training. This paper studies the learning-where-to-learn problem of designing a training data distribution that minimizes average…
arXiv:2510.06935v1 Announce Type: new Abstract: Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a “policy”, that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential…
arXiv:2510.06995v1 Announce Type: new Abstract: We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several “root cause” nodes. We show that it is possible to identify a short list…
arXiv:2510.06919v1 Announce Type: new Abstract: We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a…
arXiv:2510.06685v1 Announce Type: new Abstract: Self-attention layers have become fundamental building blocks of modern deep neural networks, yet their theoretical understanding remains limited, particularly from the perspective of random matrix theory. In this work, we provide a rigorous analysis of…
arXiv:2510.06647v1 Announce Type: new Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minimax worst-case regret, but their gap-dependent bounds remain coarse and fail to fully capture the…
arXiv:2510.07099v1 Announce Type: new Abstract: In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with…
arXiv:2502.00336v2 Announce Type: replace-cross Abstract: We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity and the size of the training dataset. In our experiments, we further…
arXiv:2510.07185v1 Announce Type: new Abstract: Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong performance guarantees with minimal computational costs.…
arXiv:2507.15112v3 Announce Type: replace-cross Abstract: Machine learning systems increasingly face requirements to remove entire domains of information — such as toxic language or biases — rather than individual user data. This task presents a dilemma: full removal of the unwanted…
