Summaries of and commentary on optimization & machine learning papers.

  1. The Method of Projections for Finding the Common Point of Convex Sets (Gubin 1966)
  2. YALMIP: A Toolbox for Modeling and Optimization in MATLAB (Lofberg 2004)
  3. Graph Implementations for Nonsmooth Convex Programs (Grant 2008)
  4. Code Generation for Embedded Second-Order Cone Programming (Chu 2013)
  5. A Neural Algorithm of Artistic Style (Gatys 2015)
  6. A Latent Variable Model Approach to PMI-based Word Embeddings (Arora 2016)
  7. CVXPY: A Python-Embedded Modeling Language for Convex Optimization (Diamond 2016)
  8. JuMP: A Modeling Language for Mathematical Optimization (Duninng 2016)
  9. Train Faster, Generalize Better: Stability of Stochastic Gradient Descent (Hardt 2016)
  10. TensorFlow: A System for Large-Scale Machine Learning (Mongat 2016)
  11. Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (O'Donoghue 2016)
  12. OptNet: Differentiable Optimization as a Layer in Neural Networks (Amos 2017)
  13. A Simple but Tough-to-Beat Baseline for Sentence Embeddings (Arora 2017)
  14. Occupy the Cloud: Distributed Computing for the 99% (Jonas 2017)
  15. The Mythos of Model Interpretability (Lipton 2017)
  16. Lifted Neural Networks (El Ghaoui 2018)