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Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…
Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
Chao Han, Debabrota Basu, Michael Mangan +2
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov D…
Active Fourier Auditor for Estimating Distributional Properties of ML Models
Ayoub Ajarra, Bishwamittra Ghosh, Debabrota Basu
With the pervasive deployment of Machine Learning (ML) models in real-world applications, verifying and auditing properties of ML models have become a central concern. In this work…
Testing Credibility of Public and Private Surveys through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in ge…
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
Udvas Das, Debabrota Basu
Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, reso…
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat, Debabrota Basu, Yulia Gel +1
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational t…