1 code implementation • 17 Dec 2012 • Peter Richtárik, Majid Jahani, Selin Damla Ahipaşaoğlu, Martin Takáč
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.
no code implementations • 10 Oct 2017 • Majid Jahani, Naga Venkata C. Gudapati, Chenxin Ma, Rachael Tappenden, Martin Takáč
In this work we introduce the concept of an Underestimate Sequence (UES), which is motivated by Nesterov's estimate sequence.
no code implementations • 26 Oct 2018 • Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takáč
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.
1 code implementation • 28 Jan 2019 • Albert S. Berahas, Majid Jahani, Peter Richtárik, Martin Takáč
We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning.
no code implementations • 30 May 2019 • Majid Jahani, MohammadReza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takáč
In this paper, we present a scalable distributed implementation of the Sampled Limited-memory Symmetric Rank-1 (S-LSR1) algorithm.
no code implementations • 30 May 2019 • Mohammadreza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takáč
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.
no code implementations • 6 Jun 2020 • Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takáč
This work presents a new algorithm for empirical risk minimization.
no code implementations • 3 Jul 2020 • Soheil Sadeghi Eshkevari, Martin Takáč, Shamim N. Pakzad, Majid Jahani
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.
no code implementations • ICLR 2022 • Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takáč
We present a novel adaptive optimization algorithm for large-scale machine learning problems.