3 citations · 4 across the 4 of their papers we have counts for
5 papers
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil +3
We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, whi…
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
Souradip Chakraborty, Ekansh Verma, Saswata Sahoo +1
Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold. Moreover, f…
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
Souradip Chakraborty, Aritra Roy Gosthipaty, Sayak Paul
In the realms of computer vision, it is evident that deep neural networks perform better in a supervised setting with a large amount of labeled data. The representations learned wi…
Learning Representation for Mixed Data Types with a Nonlinear Deep Encoder-Decoder Framework
Saswata Sahoo, Souradip Chakraborty
Representation of data on mixed variables, numerical and categorical types to get suitable feature map is a challenging task as important information lies in a complex non-linear m…
Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type
Saswata Sahoo, Souradip Chakraborty
Feature learning in the presence of a mixed type of variables, numerical and categorical types, is an important issue for related modeling problems. For simple neighborhood queries…