After 8 years building production ML systems (in data quality, entity resolution, diagnostics), I keep running into the same problem: Models with great offline metrics fail in production because they learn correlations, not causal mechanisms. I just started a 5-part series on building causal ML systems on the NeoForge Labs research blog. Part 1 covers: Why correlation fails – The ice cream/drowning example, but with real production failures Pearl’s Ladder of Causation – Association, Intervention, Counterfactuals Practical implications […]

Ver mais

Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal–abnormal and benign–malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several […]

Ver mais

arXiv:2602.19578v1 Announce Type: new Abstract: Active data acquisition is central to many learning and optimization tasks in deep neural networks, yet remains challenging because most approaches rely on predictive uncertainty estimates that are difficult to obtain reliably. To this end, we propose Goal-Oriented Influence- Maximizing Data Acquisition (GOIMDA), an active acquisition algorithm that avoids explicit posterior inference while remaining uncertainty-aware through inverse curvature. GOIMDA selects inputs by maximizing their expected influence on a user-specified goal functional, such as […]

Ver mais

In this paper, we move beyond the classical setting by redefining the Chebyshev functional in the context of q-circles situated within Minkowski space, rather than the standard Euclidean circles in R2. This approach introduces a new theoretical framework suitable for non-Euclidean geometries. We derive sharp estimates for the functional when applied to functions on q-circles that adhere to Hölder-type continuity conditions.

Ver mais

arXiv:2601.15360v1 Announce Type: new Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it is fundamentally vulnerable to “Outlier Smearing” when reliant on Mean Squared Error (MSE) minimization. In this failure mode, the bias from a few extreme observations (“whales”) in the minority group is propagated to the entire majority […]

Ver mais

arXiv:2601.05449v1 Announce Type: new Abstract: The increasing deployment of small Uncrewed Aerial Systems (sUAS) in diverse and often safety-critical environments demands rigorous validation of onboard decision logic under various conditions. In this paper, we present SaFUZZ, a state-aware fuzzing pipeline that validates core behavior associated with state transitions, automated failsafes, and human operator interactions in sUAS applications operating under various timing conditions and environmental disturbances. We create fuzzing specifications to detect behavioral deviations, and then dynamically generate associated […]

Ver mais

arXiv:2602.15070v1 Announce Type: new Abstract: This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized […]

Ver mais