arXiv:2602.17577v1 Announce Type: cross Abstract: Omniprediction is a learning problem that requires suboptimality bounds for each of a family of losses $mathcal{L}$ against a family of comparator predictors $mathcal{C}$. We initiate the study of omniprediction in a multiclass setting, where…
arXiv:2602.16839v1 Announce Type: new Abstract: Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires long rollouts for outcome-based rewards, where autoregressive decoding dominates time and memory usage. While sliding-window…
arXiv:2403.11332v3 Announce Type: replace Abstract: We address the challenge of inferring causal effects in social network data. This results in challenges due to interference — where a unit’s outcome is affected by neighbors’ treatments — and network-induced confounding factors. While…
arXiv:2602.16842v1 Announce Type: new Abstract: We study the offline data-driven newsvendor problem with censored demand data. In contrast to prior works where demand is fully observed, we consider the setting where demand is censored at the inventory level and only…
arXiv:2602.16849v1 Announce Type: new Abstract: We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task. Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its…
arXiv:2506.16404v3 Announce Type: replace Abstract: Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Generating such graphs enables tasks such as simulation, data augmentation and novel instance discovery; however,…
arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it…
arXiv:2509.26522v2 Announce Type: replace Abstract: Reasoning LLMs show improved performance with longer chains of thought. However, recent work has highlighted their tendency to overthink, continuing to revise answers even after reaching the correct solution. We quantitatively confirm this inefficiency from…
arXiv:2602.16876v1 Announce Type: new Abstract: Modern datasets often contain ballast as redundant or low-utility information that increases dimensionality, storage requirements, and computational cost without contributing meaningful analytical value. This study introduces a generalized, multimodal framework for ballast detection and reduction…
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