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:2505.14733v2 Announce Type: replace Abstract: Scaling large language models (LLMs) has driven significant advancements, yet it faces diminishing returns and escalating energy demands. This work explores how test-time compute (TTC) can serve as an energy-efficient complement to conventional scaling strategies…
arXiv:2511.05628v1 Announce Type: new Abstract: Deep neural networks, particularly those employing Rectified Linear Units (ReLU), are often perceived as complex, high-dimensional, non-linear systems. This complexity poses a significant challenge to understanding their internal learning mechanisms. In this work, we propose…
arXiv:2506.22655v2 Announce Type: replace Abstract: The physical sciences are replete with dynamical systems that require the resolution of a wide range of length and time scales. This presents significant computational challenges since direct numerical simulation requires discretization at the finest…
