18 citations · 20 across the 19 of their papers we have counts for
14 papers · 1 filter
SEVerA: Verified Synthesis of Self-Evolving Agents
Debangshu Banerjee, Changming Xu, Eugene Ie +5
Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an ag…
Why DPO is a Misspecified Estimator and How to Fix It
Aditya Gopalan, Sayak Ray Chowdhury, Debangshu Banerjee
Direct alignment algorithms such as Direct Preference Optimization (DPO) fine-tune models based on preference data, using only supervised learning instead of two-stage reinforcemen…
Data Shifts Hurt CoT: A Theoretical Study
Lang Yin, Debangshu Banerjee, Gagandeep Singh
Chain of Thought (CoT) has been applied to various large language models (LLMs) and proven to be effective in improving the quality of outputs. In recent studies, transformers are…
DINGO: Constrained Inference for Diffusion LLMs
Tarun Suresh, Debangshu Banerjee, Shubham Ugare +2
Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffus…
Support is All You Need for Certified VAE Training
Changming Xu, Debangshu Banerjee, Deepak Vasisht +1
Variational Autoencoders (VAEs) have become increasingly popular and deployed in safety-critical applications. In such applications, we want to give certified probabilistic guarant…
Relational DNN Verification With Cross Executional Bound Refinement
Debangshu Banerjee, Gagandeep Singh
We focus on verifying relational properties defined over deep neural networks (DNNs) such as robustness against universal adversarial perturbations (UAP), certified worst-case hamm…
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…
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury +1
We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behav…
HEX and Neurodynamic Programming
Debangshu Banerjee
Hex is a complex game with a high branching factor. For the first time Hex is being attempted to be solved without the use of game tree structures and associated methods of pruning…