Search Results for author:
Found 9 papers, 2 papers with code
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
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.
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
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).
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
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
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.
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold.
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
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.
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
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.
Learning Representation for Mixed Data Types with a Nonlinear Deep Encoder-Decoder Framework
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
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.
