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  • Indexing by Latent Semantic Analysis
  • The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge
  • word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method
  • Stability and Generalization
  • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
  • The Method of projections for finding the common point of convex sets
  • A semismooth newton method for fast, generic convex programming
  • SuperMann: A superlinearly convergent algorithm for finding fixed points of nonexpansive operators
  • On projection algorithms for solving convex feasibility problems
  • Monotone operator methods
  • Distributed optimization and statistical learning via the alternating directions method of multipliers
  • Line search for averaged operator iteration
  • A Convex Optimization Approach to Radiation Treatment Planning with Dose Constraints
  • The idea behind Krylov methods
  • Densely Connected Convolutional Networks
  • Learning from Simulated and Unsupervised Images through Adversarial Training
  • Computational Imaging on the Electric Grid
  • Convexified Convolutional Neural Networks
  • Feynman's lecture on quantum computers (https://people.eecs.berkeley.edu/~christos/classics/Feynman.pdf)
  • ANDERSON ACCELERATION FOR FIXED-POINT ITERATIONS
  • Two Classes of Multisecant Methods for Nonlinear Acceleration
  • ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION
  • Multi-Period Trading via Convex Optimization (http://stanford.edu/~boyd/papers/cvx_portfolio.html)
  • Breaking the Curse of Dimensionality with Convex Neural Networks
  • A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning [https://arxiv.org/pdf/1012.2599.pdf]
  • https://people.eecs.berkeley.edu/~jshun/thesis.pdf
  • A practical guide to CNNs and Fisher Vectors for image instance retrieval
  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
  • Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
  • Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
  • http://www.cds.caltech.edu/~doyle/hot/SDPrelaxations.pdf
  • Occupy the Cloud: Distributed Computing for the 99% (PyWren)
  • Improving Efficiency and Scalability of Sum of Squares Optimization: Recent Advances and Limitations
  • NP-hardness of deciding convexity of quartic polynomials and related problems
  • Tractable fitting with convex polynomials via sum-of-squares
  • LAGRANGE MULTIPLIERS AND OPTIMALITY
  • Mastering the game of Go without human knowledge
  • Interior-point methods for optimization (Nemirovski and Todd)
  • Convergence rate of incremental aggregated gradient algorithms (Pablo Parrilo)
  • Towards Generalization and Simplicity in Continuous Control (kakade)
  • Stochastic subgradient method converges on tame functions (kakade) (https://arxiv.org/pdf/1804.07795.pdf)
  • Lifted Neural Networks (El Ghaoui)
  • Minimizing Finite Sums with the Stochastic Average Gradient (Shmidt)
  • How to escape saddle points efficiently (jordan)
  • Local Minima and Convergence in Low-Rank Semidefinite Programming (Burer, Monteiro)
  • The non-convex Burer-Monteiro approach works on smooth semidefinite programs (Boumal)
  • Fast Exact Multiplication by the Hessian (Pearlmutter 1994)
  • The geometry of graphs and some of its algorithmic applications [LLR '94]
  • Expander Flows, Geometric Embeddings and Graph Partitioning [Arora 09]
  • Representation Tradeoffs for Hyperbolic Embeddings
  • improving distributional similarity with lessons learned from word embeddings
  • A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
  • A CONVERGENCE ANALYSIS OF GRADIENT DESCENT FOR DEEP LINEAR NEURAL NETWORKS
  • Computational Complexity, NP Completeness and Optimization Duality: A Survey
  • Bundle Methods for Regularized Risk Minimization (related to reducing convex programs to LPs)
  • Solving standard quadratic optimization problems via semidefinite and copositive programming (convex program that is NP-hard via copositive programming, https://www.ti.inf.ethz.ch/ew/lehre/ApproxSDP09/notes/copositive.pdf)
  • An Exact duality Theory for Semidefinite Programming and its Complexity Implications (strong duality without assumptions(?!))
  • Notes on interior-point methods by Matousek (https://www.ti.inf.ethz.ch/ew/lehre/ApproxSDP09/notes/interior.pdf)
  • Duality for SOCPs (https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture10.pdf)
  • Interior-point methods for optimization* Copositive Programming – a Survey* The Supporting Halfspace--Quadratic Programming Strategy for the Dual of the Best Approximation Problem (SJO)