Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2506.23033v4 Announce Type: replace-cross Abstract: Fairness audits are a key component of responsible machine-learning deployment. Yet, audit-recommendation reliability under incomplete protected-label access is still poorly understood.…
arXiv:2606.25494v1 Announce Type: cross Abstract: Building on the large-sample analysis of infinitesimal gradient boosting (Dombry and Duchamps, 2024b), we study the fluctuations of the process around…
arXiv:2407.07239v3 Announce Type: replace-cross Abstract: Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on…
arXiv:2606.25268v1 Announce Type: cross Abstract: Textual data are often collected alongside structured response variables, but prediction and interpretation are commonly treated as separate tasks. This paper…
arXiv:2606.13984v2 Announce Type: replace Abstract: Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of impurity measures…
arXiv:2606.25197v1 Announce Type: cross Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be…
arXiv:2603.22050v2 Announce Type: replace Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data. Supervised machine learning methods have demonstrated…
arXiv:2606.25086v1 Announce Type: cross Abstract: Many modern Language Model (LM) pipelines return an averaged model, such as an exponential moving average of the training iterates, rather…
arXiv:2603.13826v2 Announce Type: replace-cross Abstract: Classical sparse recovery treats all nonzero entries equally, though numerical noise often creates long tails of negligible coefficients. This paper develops…
arXiv:2606.25062v1 Announce Type: cross Abstract: Rank aggregation combines information from ordered lists ranking items by preference. Classical parametric models for such data, including the Mallows and…
arXiv:2602.15438v3 Announce Type: replace-cross Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the…
arXiv:2606.25601v1 Announce Type: new Abstract: Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of…
arXiv:2606.25745v1 Announce Type: new Abstract: Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show…
arXiv:2606.26037v1 Announce Type: new Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client…
arXiv:2606.24981v1 Announce Type: cross Abstract: We study linear TD(0) under Markovian sampling, where data are generated along a single trajectory. We provide high-probability guarantees for a…
arXiv:2606.26053v1 Announce Type: new Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This…
arXiv:2606.25042v1 Announce Type: cross Abstract: We prove a single algebraic mixed coincidence identity that unifies a broad swath of information-theoretic variational results. For any family of…
arXiv:2606.24982v1 Announce Type: cross Abstract: Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical…
arXiv:2604.00316v2 Announce Type: replace Abstract: Grokking occurs when a model achieves high training accuracy but generalization to unseen test points happens long after that. This phenomenon…
arXiv:2606.25269v1 Announce Type: new Abstract: As black-box models become foundational to modern research, ensuring their stability is paramount for the realization of trustworthy artificial intelligence. The…
arXiv:2502.18959v4 Announce Type: replace-cross Abstract: The architecture of a neural network and the choice of its activation function are both fundamental to its performance. Equally important…
arXiv:2601.02193v2 Announce Type: replace-cross Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone…
arXiv:2507.20068v3 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown…
arXiv:2602.05704v2 Announce Type: replace-cross Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. To…