no code implementations • 25 May 2022 • Sachin Kumar, Biswajit Paria, Yulia Tsvetkov
Large pretrained language models generate fluent text but are notoriously hard to controllably sample from.
no code implementations • 21 Apr 2022 • Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen
Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy.
1 code implementation • 9 Dec 2021 • Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.
no code implementations • ICLR 2022 • Viraj Mehta, Biswajit Paria, Jeff Schneider, Willie Neiswanger, Stefano Ermon
In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.
no code implementations • 14 Jun 2021 • Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das
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 that are arranged in a pre-specified aggregation hierarchy.
1 code implementation • ICLR 2020 • Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification.
no code implementations • 22 Oct 2019 • Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabas Poczos, Jay Whitacre, Venkatasubramanian Viswanathan
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.
1 code implementation • 15 Mar 2019 • Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing
We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.
no code implementations • 30 May 2018 • Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret.
no code implementations • 15 Nov 2016 • Biswajit Paria, K. M. Annervaz, Ambedkar Dukkipati, Ankush Chatterjee, Sanjay Podder
In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference.
no code implementations • 5 Aug 2016 • Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas
Also, the proposed model is compared with traditional boosting and recent multiview boosting algorithms.
2 code implementations • 10 Apr 2016 • Biswajit Paria, Vikas Reddy, Anirban Santara, Pabitra Mitra
The success of deep neural networks is mostly due their ability to learn meaningful features from the data.