Josias Moukpe | Searcharxiv

arXiv2026

DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories

Autonomous agents for machine learning experimentation must navigate heterogeneous repositories, repair training pipelines, and evaluate candidate improvements under realistic compute constraints. Existing benchmarks only partially capture these conditions. We introduce DeltaML-Bench, a benchmark comprising 48 tasks sourced from research papers that require agents to improve published baselines within imperfect, open-source repositories. We evaluate GPT-5 and Claude Sonnet 4 with a standard Modular agent and a search-based ARG scaffolding. In the 4 x 6h allocation, ARG raises GPT-5's per-run success rate from 9.4% to 33.9%; in the 2 x 12h allocation, GPT-5 ARG reaches 49.0%. Modular configurations exhibit specification gaming rates as high as 47.9%, while no gaming is observed in the evaluated ARG configurations. These results indicate that scaffolding design and integrity checks are important considerations when deploying agents for autonomous ML experimentation.

cs.LG↗

arXiv2023

Survey on Imbalanced Data, Representation Learning and SEP Forecasting

Deep Learning methods have significantly advanced various data-driven tasks such as regression, classification, and forecasting. However, much of this progress has been predicated on the strong but often unrealistic assumption that training datasets are balanced with respect to the targets they contain. This misalignment with real-world conditions, where data is frequently imbalanced, hampers the effectiveness of such models in practical applications. Methods that reconsider that assumption and tackle real-world imbalances have begun to emerge and explore avenues to address this challenge. One such promising avenue is representation learning, which enables models to capture complex data characteristics and generalize better to minority classes. By focusing on a richer representation of the feature space, these techniques hold the potential to mitigate the impact of data imbalance. In this survey, we present deep learning works that step away from the balanced-data assumption, employing strategies like representation learning to better approximate real-world imbalances. We also highlight a critical application in SEP forecasting where addressing data imbalance is paramount for success.

cs.LG↗