3 citations · 6 across the 10 of their papers we have counts for
6 papers · 1 filter
Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Stipe Frković, Stipe Frkovic, Metod Jazbec +4
Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel tok…
Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
Ha Manh Bui, Metod Jazbec, Eric Nalisnick +1
Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficient, O2O-RL is susceptible to…
Rethinking Calibration for Early-Exit Neural Networks
Piotr Kubaty, Filip Szatkowski, Grzegorz Choczyński +2
Early-exit neural networks (EENNs) accelerate inference by allowing intermediate classifiers to stop computation once predictions are confident enough. Most methods rely on confide…
On Calibration in Multi-Distribution Learning
Rajeev Verma, Volker Fischer, Eric Nalisnick
Modern challenges of robustness, fairness, and decision-making in machine learning have led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor…
Learning to Defer to a Population: A Meta-Learning Approach
Dharmesh Tailor, Aditya Patra, Rajeev Verma +2
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert. All existing work on L2D assumes that eac…
Calibrated Learning to Defer with One-vs-All Classifiers
Rajeev Verma, Eric Nalisnick
The learning to defer (L2D) framework has the potential to make AI systems safer. For a given input, the system can defer the decision to a human if the human is more likely than t…