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arXiv:2603.20538v1 Announce Type: new Abstract: Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and …
arXiv:2603.20572v1 Announce Type: new Abstract: The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth …
arXiv:2603.20585v1 Announce Type: new Abstract: Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and …
arXiv:2603.20586v1 Announce Type: new Abstract: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, …
arXiv:2603.20587v1 Announce Type: new Abstract: When training a neural network for classification, the feature vectors of the training set are known to collapse to the …
arXiv:2603.20589v1 Announce Type: new Abstract: Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. …
arXiv:2603.20604v1 Announce Type: new Abstract: We study Bayesian learning in episodic, finite-horizon zero-sum Markov games with unknown transition and reward models. We investigate a posterior …
arXiv:2603.20616v1 Announce Type: new Abstract: Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting …
arXiv:2603.20632v1 Announce Type: new Abstract: Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. …
arXiv:2603.20634v1 Announce Type: new Abstract: Accurately characterizing non-linear functional manifolds with singularities is a fundamental challenge in scientific computing. While Multi-Layer Perceptrons (MLPs) dominate, their …
arXiv:2603.20645v1 Announce Type: new Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated …
arXiv:2603.20655v1 Announce Type: new Abstract: We introduce Exponential Family Discriminant Analysis (EFDA), a unified generative framework that extends classical Linear Discriminant Analysis (LDA) beyond the …
