arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 23925v1 Announce Type: new Abstract: Gradient descent scales well to large models, but becomes unstable over long time horizons.
Gradient descent scales well to large models, but becomes unstable over long time horizons. Gradient-free optimizers can scale to arbitrary timespans, but are hobbled by high dimensions.
arXiv:2609. 22833v1 Announce Type: new Abstract: We study personalized federated reinforcement learning, in which $n$ agents, each acting in its own Markov decision process, collaborate through a server to learn a shared MAML-style policy initialization that becomes effective for an individual agent once that agent adapts it with a single local policy-gradient step.
arXiv:2606. 15832v1 Announce Type: new Abstract: Empirical risk minimization on massive datasets naturally exhibits a nested double finite-sum structure, where $N=nm$ total samples are logically or physically partitioned into $n$ blocks of size $m$ (e.
The paper introduces ZFO, a lightweight framework that separates direction selection from step-size determination in large‑scale neural network optimization. ZFO uses a trusted first‑order optimizer to pick a search direction and then performs only two additional objective evaluations to build a local curvature‑aware model, selecting an adaptive step within a bounded interval. The authors provide theoretical guarantees for reliable curvature estimation, near‑optimal step selection, and convergence to a stationary point, and demonstrate that ZFO improves optimization and final performance over fixed‑step first‑order baselines on language‑model fine‑tuning tasks.
arXiv:2501.14271v4 Announce Type: replace Abstract: Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially...