18 citations · 20 across the 19 of their papers we have counts for
6 papers · 1 filter
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
Debangshu Banerjee, Aditya Gopalan
Parametric, feature-based reward models are employed by a variety of algorithms in decision-making settings such as bandits and Markov decision processes (MDPs). The typical assump…
Incremental Randomized Smoothing Certification
Shubham Ugare, Tarun Suresh, Debangshu Banerjee +2
Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method cons…
Incremental Verification of Neural Networks
Shubham Ugare, Debangshu Banerjee, Sasa Misailovic +1
Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies a desired trustworthy property (e.g., robustness, fairness) on an infinite set…
Interpreting Robustness Proofs of Deep Neural Networks
Debangshu Banerjee, Avaljot Singh, Gagandeep Singh
In recent years numerous methods have been developed to formally verify the robustness of deep neural networks (DNNs). Though the proposed techniques are effective in providing mat…
On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces
Debangshu Banerjee, Aditya Gopalan
As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of acti…
Markov Chain Concentration with an Application in Reinforcement Learning
Debangshu Banerjee
Given random variables whose joint distribution is given as we will use the Martingale Method to show any Lipshitz Function over these random variables is…