TILT: Target-induced loss tilting under covariate shift
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:TILT: Target-induced loss tilting under covariate shift
Abstract:We introduce and analyze Target-Induced Loss Tilting (TILT) for unsupervised domain adaptation under covariate shift. It is based on a novel objective function that decomposes the source predictor as $f+b$, fits $f+b$ on labeled source data while simultaneously penalizing the auxiliary component $b$ on unlabeled target inputs. The resulting fit $f$ is deployed as the final target predictor. At the population level, we show that this target-side penalty implicitly induces relative importance weighting at the population level, but in terms of an estimand $b^*_f$ that is self-localized to the current error, and remains uniformly bounded for any source-target pair (even those with disjoint supports). We prove a general finite-sample oracle inequality on the excess risk, and use it to give an end-to-end guarantee for training with sparse ReLU networks. Experiments on controlled regression problems and shifted CIFAR-100 distillation show that TILT improves target-domain performance over source-only training, exact importance weighting, and relative density-ratio baselines, with a stable dependence on the regularization parameter.
| Comments: | 32 pages, 17 figures. Submitted to NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| MSC classes: | 62G08, 62G20, 68T07 |
| Cite as: | arXiv:2605.14280 [cs.LG] |
| (or arXiv:2605.14280v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14280
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations
May 15
-
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
May 15
-
Rethinking Molecular OOD Generalization via Target-Aware Source Selection
May 15
-
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
May 15
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.