Search Results for author:
Found 9 papers, 2 papers with code
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
Parametric, feature-based reward models are employed by a variety of algorithms in decision-making settings such as bandits and Markov decision processes (MDPs).
Incremental Randomized Smoothing Certification
We experimentally demonstrate the effectiveness of our approach, showing up to 3x certification speedup over the certification that applies randomized smoothing of the approximate model from scratch.
Incremental Verification of Neural Networks
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 of inputs or not.
Interpreting Robustness Proofs of Deep Neural Networks
In recent years numerous methods have been developed to formally verify the robustness of deep neural networks (DNNs).
On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces
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 action spaces.
Markov Chain Concentration with an Application in Reinforcement Learning
Given $X_1,\cdot , X_N$ random variables whose joint distribution is given as $\mu$ we will use the Martingale Method to show any Lipshitz Function $f$ over these random variables is subgaussian.
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
Furthermore, while the previous result is shown to hold only in the asymptotic regime (as $n \to \infty$), our result for these "locally rich" action spaces is any-time.
Critic Algorithms using Cooperative Networks
This algorithm tracks the Projected Bellman Algorithm and is therefore different from the class of residual algorithms.
HEX and Neurodynamic Programming
Instead what we use is reinforcement learning through self play and approximations through neural networks to by pass the problem of high branching factor and maintaining large tables for state-action evaluations.
