Europe
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
Neural Information Processing Systems http://nips.cc/
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Neural Information Processing Systems http://nips.cc/
Mitigating Source Bias for Fairer Weak Supervision
Theoretically, we show that it is possible for our approach to simultaneously improve both accuracy and fairness--in contrast to standard fairness approaches that suffer from tradeoffs. Empirically, we show that our technique improves accuracy on weak supervision baselines by as much as 32% while reducing demographic parity gap by 82.5%.
Tensor Monte Carlo: Particle Methods for the GPU era
Neural Information Processing Systems http://nips.cc/
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Further analysis suggests a positive relationship (Spearman's r
Zero-Shot Tokenizer Transfer Benjamin Minixhofer
Our method comes close to the original models' performance in cross-lingual and
7c78335a8924215ea5c22fda1aac7b75-Paper.pdf
Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning.