arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pre...
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black--box models, even though such predictions are increasingly available in settings where labels remain expensive.
arXiv:2609.14065v1 Announce Type: new Abstract: When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop be...
arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
The paper introduces OSR, a method for removing labels from classification models by redistributing output space statistics to approximate retrained confidence vectors. It functions as a modular output filter, avoiding feature‑space adjustments and heavy retraining. Experiments show it matches full retraining performance while improving computational efficiency and privacy.
arXiv:2606. 14506v1 Announce Type: cross Abstract: Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making.
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
The paper investigates the impact of label‑flipping attacks on distributed machine learning, where an adversary can only flip a limited number of training labels. It formalizes the attack as a per‑round constrained optimization problem, derives a greedy label‑selection rule for logistic regression, and shows that this rule is provably optimal under mean aggregation. Experiments demonstrate that optimized label flipping can significantly degrade model accuracy, outperforming random flips, and that the attack transfers to other robust aggregators such as coordinate‑wise median and trimmed mean.
arXiv:2606. 07890v1 Announce Type: new Abstract: Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains.
The paper examines the common practice of aggregating multiple labels per instance into a single ‘true’ label for supervised learning. By creating a theoretical model, the authors show that using the full, non‑aggregated label information can make it easier to train well‑calibrated models, though the benefits depend on the specific problem. They predict when non‑aggregated labels will improve learning and validate these predictions on real datasets.