arXiv:2311.01537v2 Announce Type: replace-cross Abstract: We study two-sample variable selection: identifying variables that discriminate between the distributions of two sets of data vectors. Such variables help scientists understand the mechanisms behind dataset discrepancies. Although domain-specific methods exist (e.g., in medical…
arXiv:2511.03046v1 Announce Type: new Abstract: Recent work in financial machine learning has shown the virtue of complexity: the phenomenon by which deep learning methods capable of learning highly nonlinear relationships outperform simpler approaches in financial forecasting. While transformer architectures like…
arXiv:2511.03047v1 Announce Type: new Abstract: Large language models (LLMs) have seen increasing popularity in enterprise applications where AI agents and humans engage in objective-driven interactions. However, these systems are difficult to evaluate: data may be complex and unlabeled; human annotation…
arXiv:2510.07978v2 Announce Type: replace-cross Abstract: Large-scale Speech Language Models (SpeechLMs) have enabled voice assistants capable of understanding natural spoken queries and performing complex tasks. However, existing speech benchmarks primarily focus on isolated capabilities such as transcription, or question-answering, and do…
arXiv:2511.03060v1 Announce Type: new Abstract: We present a geometric framework for understanding Transformer-based language models, drawing an explicit analogy to General Relativity. Queries and keys induce an effective metric on representation space, and attention acts as a discrete connection that…
arXiv:2511.03653v1 Announce Type: cross Abstract: Given a small random sample of $n$-bit strings labeled by an unknown Boolean function, which properties of this function can be tested computationally efficiently? We show an equivalence between properties that are efficiently testable from…
arXiv:2511.03068v1 Announce Type: new Abstract: For far too long, expressivity of graph neural networks has been measured emph{only} in terms of combinatorial properties. In this work we stray away from this tradition and provide a principled way to measure similarity…
arXiv:2405.18187v2 Announce Type: replace Abstract: Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear how to recover the implicit policy from the…
arXiv:2511.03074v1 Announce Type: new Abstract: Online learning to rank (OLTR) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly modeled as cascading bandits,…
arXiv:2501.15971v2 Announce Type: replace Abstract: Chemical language models, combined with reinforcement learning (RL), have shown significant promise to efficiently traverse large chemical spaces for drug discovery. However, the performance of various RL algorithms and their best practices for practical drug…
