95 citations · 430 across the 31 of their papers we have counts for
12 papers · 1 filter
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau +4
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is…
A Generic Approach for Escaping Saddle points
Sashank J Reddi, Manzil Zaheer, Suvrit Sra +4
A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly d…
Recurrent Estimation of Distributions
Junier B. Oliva, Kumar Avinava Dubey, Barnabas Poczos +2
This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural ne…
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider +1
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be p…
Data-driven Random Fourier Features using Stein Effect
Wei-Cheng Chang, Chun-Liang Li, Yiming Yang +1
Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht,…
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon S. Du, Chi Jin, Jason D. Lee +3
Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes a…