3 accepted papers
Self-correction in language models remains elusive. In this work, we explore whether language models can explicitly localize errors in incorrect reasoning, as a path toward building AI systems that can effectively correct themselves. We introduce a prompting method that structures reasoning as discr…
Compressed Stochastic Gradient Descent (SGD) algorithms have been proposed to address the communication bottleneck in distributed and decentralized optimization problems such as federated machine learning. Many existing compressed SGD algorithms use non-adaptive step-sizes (constant or diminishing)…
The problem of robust binary hypothesis testing is studied. Under both hypotheses, the data-generating distributions are assumed to belong to uncertainty sets constructed through moments; in particular, the sets contain distributions whose moments are centered around the empirical moments obtained f…