arXiv:2408.13235v2 Announce Type: replace-cross Abstract: We consider ordinary least squares estimation and variations on least squares estimation such as penalized (regularized) least squares and spectral shrinkage estimates for problems with p > n and associated problems with prediction of new…
arXiv:2405.18222v3 Announce Type: replace Abstract: Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not been used for Learning to Optimize (L2O). Following these…
arXiv:2502.12717v2 Announce Type: replace Abstract: Machine learning explorations can make significant inroads into solving difficult problems in pure mathematics. One advantage of this approach is that mathematical datasets do not suffer from noise, but a challenge is the amount of…
arXiv:2509.14488v1 Announce Type: new Abstract: Distributed optimization requires nodes to coordinate, yet full synchronization scales poorly. When $n$ nodes collaborate through $m$ pairwise regularizers, standard methods demand $mathcal{O}(m)$ communications per iteration. This paper proposes randomized local coordination: each node independently…
arXiv:2509.14519v1 Announce Type: new Abstract: Malware is becoming increasingly complex and widespread, making it essential to develop more effective and timely detection methods. Traditional static analysis often fails to defend against modern threats that employ code obfuscation, polymorphism, and other…
arXiv:2509.14444v1 Announce Type: new Abstract: Federated Learning (FL) allows distributed model training without sharing raw data, but suffers when client participation is partial. In practice, the distribution of available users (emph{availability distribution} $q$) rarely aligns with the distribution defining the…
arXiv:2509.14472v1 Announce Type: new Abstract: The plethora of space-borne and ground-based observatories has provided astrophysicists with an unprecedented volume of data, which can only be processed at scale using advanced computing algorithms. Consequently, ensuring the quality of data fed into…
arXiv:2509.14427v1 Announce Type: new Abstract: Information retrieval with compact binary embeddings, also referred to as hashing, is crucial for scalable fast search applications, yet state-of-the-art hashing methods require expensive, scenario-specific training. In this work, we introduce Hashing-Baseline, a strong training-free…
arXiv:2509.14391v1 Announce Type: new Abstract: Extending LLM context windows is crucial for long range tasks. RoPE-based position interpolation (PI) methods like linear and frequency-aware scaling extend input lengths without retraining, while post-training quantization (PTQ) enables practical deployment. We show that…
arXiv:2509.14386v1 Announce Type: new Abstract: We prove a fundamental impossibility theorem: neural networks cannot simultaneously learn well-calibrated confidence estimates with meaningful diversity when trained using binary correct/incorrect supervision. Through rigorous mathematical analysis and comprehensive empirical evaluation spanning negative reward training,…
