Many problems of recent interest in statistics and machine learning can be posed in the
framework of convex optimization. Due to the explosion in size and complexity of modern …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Dual decomposition, and more generally Lagrangian relaxation, is a classical method for
combinatorial optimization; it has recently been applied to several inference problems in …

Large language models have recently achieved state of the art performance across a wide
variety of natural language tasks. Meanwhile, the size of these models and their latency …

Events and entities are closely related; entities are often actors or participants in events and
events without entities are uncommon. The interpretation of events and entities is highly …

Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet
lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model …

Szeliski et al. published an influential study in 2006 on energy minimization methods for
Markov random fields. This study provided valuable insights in choosing the best …

Online optimization has emerged as powerful tool in large scale optimization. In this pa-per,
we introduce efficient online optimization algorithms based on the alternating direction …

Abstract Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family
of structured prediction models (Belanger and McCallum, 2016). An energy function over …

Seven years ago, Szeliski et al. published an influential study on energy minimization
methods for Markov random fields (MRF). This study provided valuable insights in choosing …