3k citations · 24.2k across the 299 of their papers we have counts for
6 papers · 2 filters
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin +2
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed algorithm efficiently escapes saddle…
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Nesterov's accelerated gradient descent (AGD), an instance of the general family of "momentum methods", provably achieves faster convergence rate than gradient descent (GD) in the…
Partial Transfer Learning with Selective Adversarial Networks
Zhangjie Cao, Mingsheng Long, Jianmin Wang +1
Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains.…
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, Trus…
Conditional Adversarial Domain Adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang +1
Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation metho…
How to Escape Saddle Points Efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli +2
This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension…