Papers

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. Solving standard quadratic optimization problems via semidefinite and copositive programming (Bomze 2002)
  3. YALMIP: A Toolbox for Modeling and Optimization in MATLAB (Lofberg 2004)
  4. Graph Implementations for Nonsmooth Convex Programs (Grant 2008)
  5. Code Generation for Embedded Second-Order Cone Programming (Chu 2013)
  6. A Neural Algorithm of Artistic Style (Gatys 2015)
  7. A Latent Variable Model Approach to PMI-based Word Embeddings (Arora 2016)
  8. CVXPY: A Python-Embedded Modeling Language for Convex Optimization (Diamond 2016)
  9. JuMP: A Modeling Language for Mathematical Optimization (Duninng 2016)
  10. Train Faster, Generalize Better: Stability of Stochastic Gradient Descent (Hardt 2016)
  11. TensorFlow: A System for Large-Scale Machine Learning (Mongat 2016)
  12. Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (O'Donoghue 2016)
  13. OptNet: Differentiable Optimization as a Layer in Neural Networks (Amos 2017)
  14. A Simple but Tough-to-Beat Baseline for Sentence Embeddings (Arora 2017)
  15. Occupy the Cloud: Distributed Computing for the 99% (Jonas 2017)
  16. The Mythos of Model Interpretability (Lipton 2017)
  17. Lifted Neural Networks (El Ghaoui 2018)