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arXiv:2603.11210v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning …
arXiv:2603.11230v1 Announce Type: new Abstract: We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. …
arXiv:2603.11249v1 Announce Type: new Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure …
arXiv:2603.11269v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection methods perform well on multi-domain benchmarks, yet many practical systems are trained on single-domain data. We show …
arXiv:2603.11273v1 Announce Type: new Abstract: Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, …
arXiv:2603.11296v1 Announce Type: new Abstract: State space models (SSMs) have recently achieved strong performance on long sequence modeling tasks while offering improved memory and computational …
arXiv:2603.11307v1 Announce Type: new Abstract: Federated learning (FL) under data heterogeneity remains challenging: existing methods either ignore client differences (FedAvg), require costly cluster discovery (IFCA), …
arXiv:2603.11308v1 Announce Type: new Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and …
arXiv:2603.11319v1 Announce Type: new Abstract: We consider the robustness of score-based generative modeling to errors in the estimate of the score function. In particular, we …
arXiv:2603.11321v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such …
arXiv:2603.11327v1 Announce Type: new Abstract: This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a …
arXiv:2603.11331v1 Announce Type: new Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can …