arXiv:2609.36655v1 Announce Type: new
Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods o...
By Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong
arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.
By Jiguang Li, Hengrui Luo
arXiv:2609.37687v1 Announce Type: new
Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively queryin...
By Muhammad Huzaifa, Lea Sch\"onherr, Thorsten Eisenhofer
arXiv:2607. 00259v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data.
By Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny, Arash Mohammadi, Damien Teney, Ehsan Abbasnejad
The paper studies how test‑time training (TTT) improves in‑context learning (ICL) for nonlinear models, focusing on single‑index models where features lie in a hidden low‑dimensional subspace. By applying TTT to single‑layer transformers trained with gradient‑based methods, the authors derive an upper bound on prediction risk and show that TTT allows the model to adapt to both feature vectors and link functions that vary across tasks—something ICL alone struggles to achieve. They also provide a convergence rate indicating that predictive error can approach the noise level as context size and network width increase.
By Kento Kuwataka, Taiji Suzuki
The paper introduces a statistical framework for post‑training hyperparameter selection, emphasizing the learn‑then‑test (LTT) paradigm. It treats hyperparameter tuning as a multiple hypothesis testing problem over a candidate set, enabling the selection of hyperparameters that meet specified reliability constraints such as risk bounds or information‑theoretic limits. The framework provides finite‑sample control of error probabilities using p‑values, e‑values, and concentration inequalities derived from first principles.
By Amirmohammad Farzaneh, Osvaldo Simeone