RecKAN introduces a learnable recursive polynomial basis for Kolmogorov–Arnold Networks, replacing fixed bases like B-splines or Chebyshev polynomials. The basis is defined by a second‑order polynomial recurrence whose five coefficients are jointly learned with the network, enabling it to encompass classical families such as Chebyshev, Fibonacci, Pell, and Jacobsthal. Experiments across image, text, biomedical time‑series classification, and forecasting tasks show RecKAN outperforming parameter‑matched KAN baselines and achieving state‑of‑the‑art results on several benchmarks.
By Amirhosein Azarpour
arXiv:2609.24328v1 Announce Type: new
Abstract: Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule u...
By Congye Wang, Yan Lin, Zheyang Shen, Matthew A. Fisher, Chris. J. Oates
arXiv:2610. 21363v1 Announce Type: cross Abstract: Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them.
By Zhi Zhang, Arash A. Amini
arXiv:2607. 19689v1 Announce Type: cross Abstract: We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must output new predictions that are calibrated while incurring small excess error relative to the original forecasts, under a proper loss.
By Lunjia Hu, Kevin Tian, Chutong Yang
arXiv:2511.22435v2 Announce Type: replace
Abstract: Invariant learning on graphs aims to build predictors that rely on causal substructures rather than on environment-specific shortcuts. Current meth...
By Ali Ghasemi, Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri
arXiv:2510.10730v3 Announce Type: replace
Abstract: We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for two mo...
By Jiazheng Sun, Weixin Wang, Pan Xu