arXiv AI
Jun 30
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
arXiv:2601. 07965v2 Announce Type: replace Abstract: When a model knows when it does not know, many possibilities emerge.
Toward Calibrated Mixture-of-Experts Under Distribution Shift
arXiv:2606. 20544v1 Announce Type: new Abstract: Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities.
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arXiv Machine Learning
Jul 15
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs
arXiv:2602. 19938v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets.
arXiv AI
Jun 2
ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
arXiv:2606. 01509v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale by activating only a small subset of experts per token.
arXiv Machine Learning
Aug 6
Elbow-Based MoE Routing: A Training-Free Inference Time Plugin for Expert Selection
arXiv:2608. 04401v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable model scaling while maintaining low inference-time compute by activating only a subset of experts per token.
arXiv Machine Learning
Jul 7
Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
arXiv Machine Learning
Aug 12
Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration
arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.
arXiv Machine Learning
Aug 11
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.
arXiv Machine Learning
Aug 4
Nonparametric Distribution Regression Re-calibration
arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.
arXiv AI
Jul 24
Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement
arXiv:2607. 20529v1 Announce Type: cross Abstract: Large Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs.
arXiv Machine Learning
Jun 17
Learning Credal Ensembles via Distributionally Robust Optimization
arXiv:2602. 08470v3 Announce Type: replace Abstract: Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions.
arXiv AI
Jul 1
Improving Certified Robustness via Adversarial Distillation
arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.
arXiv Machine Learning
Jun 17
MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
arXiv:2602. 06154v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models scale large language models efficiently by sparsely activating experts, but once an expert is selected, it is executed fully.