Search Results for author: Souradip Chakraborty

Found 9 papers, 2 papers with code

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies

no code implementations12 Jun 2022 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Pratap Tokekar, Dinesh Manocha

In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems.

Continuous Control OpenAI Gym

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

no code implementations2 Jun 2022 Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha

In this work, we propose a novel ${\bf K}$ernelized ${\bf S}$tein Discrepancy-based Posterior Sampling for ${\bf RL}$ algorithm (named $\texttt{KSRL}$) which extends model-based RL based upon posterior sampling (PSRL) in several ways: we (i) relax the need for any smoothness or Gaussian assumptions, allowing for complex mixture models; (ii) ensure it is applicable to large-scale training by incorporating a compression step such that the posterior consists of a \emph{Bayesian coreset} of only statistically significant past state-action pairs; and (iii) develop a novel regret analysis of PSRL based upon integral probability metrics, which, under a smoothness condition on the constructed posterior, can be evaluated in closed form as the kernelized Stein discrepancy (KSD).

Continuous Control Model-based Reinforcement Learning +2

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

no code implementations28 Jan 2022 Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel

Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space.

Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning

no code implementations SEMEVAL 2020 Ekansh Verma, Vinodh Motupalli, Souradip Chakraborty

In this paper, we present our approach for the {'}Detection of Propaganda Techniques in News Articles{'} task as a part of the 2020 edition of International Workshop on Semantic Evaluation.

Ensemble Learning

FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints

no code implementations7 Oct 2020 Souradip Chakraborty, Ekansh Verma, Saswata Sahoo, Jyotishka Datta

Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold.

Fairness Representation Learning

G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

1 code implementation28 Sep 2020 Souradip Chakraborty, Aritra Roy Gosthipaty, Sayak Paul

In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.

Contrastive Learning Denoising +2

G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

1 code implementation25 Sep 2020 Souradip Chakraborty, Aritra Roy Gosthipaty, Sayak Paul

In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.

Contrastive Learning Denoising +1

Learning Representation for Mixed Data Types with a Nonlinear Deep Encoder-Decoder Framework

no code implementations21 Sep 2020 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 manifold.

Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type

no code implementations6 May 2020 Saswata Sahoo, Souradip Chakraborty

In this work, we propose a novel strategy to explicitly model the probabilistic dependence structure among the mixed type of variables by an undirected graph.

Cannot find the paper you are looking for? You can Submit a new open access paper.