arXiv Machine Learning
Jul 30
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
arXiv:2607. 26680v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks.
Supervising the Chain Ladder
The paper proposes treating the chain ladder’s pattern selection as a supervised‑learning problem, framing adjustments as penalties and hyperparameters on a convex loss function. It introduces generalized data weights with decay and power parameters, benchmark shaping, and Whittaker‑Henderson smoothing, all combined into a single linear system for minimisation. The authors demonstrate the approach with a real Schedule P triangle, showing how experience‑based and prospective adjustments can be objectively set via a training loop and reserve validation.
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arXiv AI
Jul 22
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
arXiv:2607. 18573v1 Announce Type: new Abstract: Delay-risk models are usually judged by predictive accuracy.
arXiv Machine Learning
Jun 2
Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler
arXiv:2606. 01827v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) has established itself as a powerful and widely adopted optimizer for training machine learning models.
arXiv Machine Learning
Sep 15
Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting
arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...
arXiv Machine Learning
Oct 21
Accelerating Optimization via Differentiable Stopping Time
arXiv:2505.22509v2 Announce Type: replace Abstract: Optimization is an important module of modern machine learning applications. Tremendous efforts have been made to accelerate optimization algorithm...
Hugging Face Trending Papers
Jul 20
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
Delay-risk models are usually judged by predictive accuracy. What matters in practice is narrower: with capacity to review only a few shipments, which ones should a manager check first?
arXiv Machine Learning
Aug 18
Decision-Driven Regularization: A Blended Model for Learning and Optimization
arXiv:2608. 15124v1 Announce Type: new Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features.
arXiv Machine Learning
Jun 2
Iterated Population Based Training with Task-Agnostic Restarts
arXiv:2511. 09190v2 Announce Type: replace Abstract: Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks.
arXiv Machine Learning
Sep 2
Post-Training Science for Supervised Fine-Tuning
arXiv:2609.01244v1 Announce Type: new Abstract: Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, whic...
arXiv Machine Learning
Sep 11
Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates
arXiv:2604. 13130v2 Announce Type: replace Abstract: We study learning to learn through the lens of hyperparameter tuning.
arXiv Computer Vision
Aug 24
Difficulty-Calibrated Interpolation Paths for Conditional Flow Matching
The paper introduces Difficulty-Calibrated Flow Matching, a method that adapts the noise-to-data interpolation schedule in Conditional Flow Matching based on a pilot run’s loss profile. By setting the schedule to the quantile function of this difficulty profile, the training trajectory spends more time where the velocity is hardest to learn. Experiments on CIFAR-10, MNIST, and Fashion‑MNIST show that this calibrated path achieves the best FID on CIFAR‑10 and outperforms all fixed schedules in large‑batch, few‑update settings, where compute is most limited.
arXiv Machine Learning
Jul 17
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
arXiv:2512. 18390v2 Announce Type: replace Abstract: Organizations often have an incumbent predictive model in production when new data sources become available.