5 citations · 11 across the 4 of their papers we have counts for
14 papers
Generalizing Gaussian Smoothing for Random Search
Katelyn Gao, Ozan Sener
Gaussian smoothing (GS) is a derivative-free optimization (DFO) algorithm that estimates the gradient of an objective using perturbations of the current parameters sampled from a s…
Improving information retention in large scale online continual learning
Zhipeng Cai, Vladlen Koltun, Ozan Sener
Given a stream of data sampled from non-stationary distributions, online continual learning (OCL) aims to adapt efficiently to new data while retaining existing knowledge. The typi…
Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data
Zhipeng Cai, Ozan Sener, Vladlen Koltun
Continual learning is the problem of learning and retaining knowledge through time over multiple tasks and environments. Research has primarily focused on the incremental classific…
Modeling and Optimization Trade-off in Meta-learning
Katelyn Gao, Ozan Sener
By searching for shared inductive biases across tasks, meta-learning promises to accelerate learning on novel tasks, but with the cost of solving a complex bilevel optimization pro…
Drinking from a Firehose: Continual Learning with Web-scale Natural Language
Hexiang Hu, Ozan Sener, Fei Sha +1
Continual learning systems will interact with humans, with each other, and with the physical world through time -- and continue to learn and adapt as they do. An important open pro…
Learning to Guide Random Search
Ozan Sener, Vladlen Koltun
We are interested in derivative-free optimization of high-dimensional functions. The sample complexity of existing methods is high and depends on problem dimensionality, unlike the…