The paper titled "Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory" critiques the pessimistic meta-inductive argument against scientific realism by attacking its inductive step rather than its historical premise. It introduces a new challenge, drawing on frequentist statistics, machine learning, and formal epistemology to assess induction through convergence to truth. The authors argue that ordinary enumerative induction can achieve convergence everywhere, whereas meta-induction fails to achieve even almost everywhere convergence, and in contexts where meta-induction applies, no inference method can achieve almost everywhere convergence.
arXiv:2608. 04049v1 Announce Type: cross Abstract: The principle of Occam's razor, which instructs us to prefer simplicity in inductive inference, has attracted much scrutiny both in the philosophy of science and in machine learning.
The paper presents an order-theoretic characterization of consistent inductive inference for arbitrary binary hypothesis classes. It shows that consistency—making only finitely many prediction errors on any infinite sequence labeled by an unknown hypothesis—can be captured by a single linear order on finite realizable traces. This order must satisfy two conditions: conflicting traces select different least subtraces, and the order is well‑founded on traces of each fixed target, enabling a learner whose evidence decreases with each mistake. Conversely, any consistent learner induces such an order via canonical mistake transcripts and the Kleene–Brouwer ordering.
arXiv:2501. 05844v4 Announce Type: replace Abstract: Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality.
The article discusses how predictive benchmarking—evaluating machine learning models by their performance and ranking—serves as a core method in machine learning research. It argues that benchmark scores only reflect performance on specific datasets and learning problems, and that drawing broader scientific conclusions requires explicit assumptions. By adapting concepts from psychological validity theory, the authors propose validity conditions to make these assumptions clear, and demonstrate their application in two case studies (ImageNet and the Fragile Families Challenge) to illustrate how benchmark results can inform inferences about research progress and limits of predictability.
arXiv:2608. 03386v1 Announce Type: new Abstract: Contemporary deep learning methods generalize well even when they fit their training data perfectly, a phenomenon known as benign interpolation.
arXiv:2606. 29346v1 Announce Type: new Abstract: Post-hoc explanation methods are routinely used to interpret scientific machine learning models, with the deliverable understood to be insight into the phenomenon the model has been trained on.
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
arXiv:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.
arXiv:2606. 02632v1 Announce Type: cross Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data.
arXiv:2606. 04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks.
arXiv:2609.01526v1 Announce Type: new Abstract: Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in...
arXiv:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.