7 citations · 13 across the 2 of their papers we have counts for
4 papers
Hierarchically Regularized Deep Forecasting
Biswajit Paria, Rajat Sen, Amr Ahmed +1
Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series t…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger +5
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of expensive black box functions, which use introspective Bayesian models of the function to effi…
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization technique…