Hugging Face Trending Papers
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
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arXiv Machine Learning
Sep 23
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
arXiv:2609.25152v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to p...
arXiv AI
Sep 17
Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
The paper introduces MuViS-C, a multi‑domain benchmark that evaluates the robustness of learning‑based virtual sensing models against ten common sensor failure modes, ranging from subtle drifts to catastrophic dropouts. It assesses models using average error, relative degradation, and worst‑case fragility across nine datasets from six domains, comparing six architectures (gradient‑boosted trees, convolution, recurrence, attention, and MLP‑mixing). The study finds that all models degrade under corruption, gradient‑boosted trees are most robust, and targeted robustification can improve attention models at the cost of nominal performance.
Hugging Face Trending Papers
Sep 16
Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail...
arXiv Machine Learning
Jul 10
PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems
arXiv:2607. 08202v1 Announce Type: new Abstract: Estimating original-space conditional expectations is central to value-driven recommender systems, including dwell time, GMV, and LTV forecasting.
arXiv Machine Learning
Sep 7
Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression
The paper introduces DUO, a framework for Deep Imbalanced Regression that models each prediction as a conditional Gaussian to capture instance‑level uncertainty. By decoupling mean and variance optimization, DUO enhances learning signals for tail samples and mitigates gradient coupling that hampers hard examples. A distribution‑guided contrastive learning component further refines feature representations, leading to state‑of‑the‑art performance on several visual and biological regression benchmarks.
arXiv Machine Learning
Jun 25
Model-agnostic Mitigation Strategies of Data Imbalance for Regression
arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.