Search Results for author:
Found 9 papers, 2 papers with code
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample
We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning.
Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes
Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations.
Fast and Safe: Accelerated gradient methods with optimality certificates and underestimate sequences
In this work we introduce the concept of an Underestimate Sequence (UES), which is motivated by Nesterov's estimate sequence.
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empirical loss is under satisfactory statistical accuracy.
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
In this paper, we present a scalable distributed implementation of the Sampled Limited-memory Symmetric Rank-1 (S-LSR1) algorithm.
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
Therefore, we propose a student-teacher RL mechanism in which the RL (the "student") learns to maximize its reward, subject to a constraint that bounds the difference between the RL policy and the "teacher" policy.
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
This work presents a new algorithm for empirical risk minimization.
DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction
Data-driven models for predicting dynamic responses of linear and nonlinear systems are of great importance due to their wide application from probabilistic analysis to inverse problems such as system identification and damage diagnosis.
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
We present a novel adaptive optimization algorithm for large-scale machine learning problems.
