arXiv:2511.05614v1 Announce Type: new Abstract: Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and…
arXiv:2511.07417v1 Announce Type: cross Abstract: We study language generation in the limit, where an algorithm observes an adversarial enumeration of strings from an unknown target language $K$ and must eventually generate new, unseen strings from $K$. Kleinberg and Mullainathan [KM24]…
arXiv:2511.05615v1 Announce Type: new Abstract: As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have…
arXiv:2409.06525v5 Announce Type: replace Abstract: Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing.…
arXiv:2511.05619v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a “BERT moment” for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle…
arXiv:2502.01717v2 Announce Type: replace Abstract: The adoption of Foundation Models in resource-constrained environments remains challenging due to their large size and inference costs. A promising way to overcome these limitations is post-training compression, which aims to balance reduced model size…
arXiv:2511.05620v1 Announce Type: new Abstract: We study the problem of worst case regret in piecewise stationary multi armed bandits. While the minimax theory for stationary bandits is well established, understanding analogous limits in time-varying settings is challenging. Existing lower bounds…
arXiv:2510.17486v2 Announce Type: replace Abstract: We introduce a methodology for analyzing neural networks through the lens of layer-wise Hessian matrices. The local Hessian of each functional block (layer) is defined as the matrix of second derivatives of a scalar function…
arXiv:2407.10807v2 Announce Type: replace-cross Abstract: Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it…
arXiv:2504.00467v2 Announce Type: replace Abstract: This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial…
