Search Results for author: Mahantesh Halappanavar

Found 20 papers, 7 papers with code

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

1 code implementation20 Sep 2024 Fabiana Ferracina, Payton Beeler, Mahantesh Halappanavar, Bala Krishnamoorthy, Marco Minutoli, Laura Fierce

As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC.

Graph Neural Network

WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain

no code implementations21 Aug 2024 Rounak Meyur, Hung Phan, Sridevi Wagle, Jan Strube, Mahantesh Halappanavar, Sameera Horawalavithana, Anurag Acharya, Sai Munikoti

Benchmarking is essential to evaluate and compare the performance of the different RAG configurations in terms of retriever and generator, providing insights into their effectiveness, scalability, and suitability for the specific domain and applications.

Answer Generation Benchmarking +4

Examining Long-Context Large Language Models for Environmental Review Document Comprehension

no code implementations10 Jul 2024 Hung Phan, Anurag Acharya, Rounak Meyur, Sarthak Chaturvedi, Shivam Sharma, Mike Parker, Dan Nally, Ali Jannesari, Karl Pazdernik, Mahantesh Halappanavar, Sai Munikoti, Sameera Horawalavithana

We test the LLMs' internal prior NEPA knowledge by providing questions without any context, as well as assess how LLMs synthesize the contextual information present in long NEPA documents to facilitate the question/answering task.

Question Answering RAG

Predictive Analytics of Varieties of Potatoes

1 code implementation4 Apr 2024 Fabiana Ferracina, Bala Krishnamoorthy, Mahantesh Halappanavar, Shengwei Hu, Vidyasagar Sathuvalli

We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.

Binary Classification Decision Making +3

A Robust, Efficient Predictive Safety Filter

1 code implementation14 Nov 2023 Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar

In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.

Novel Concepts

Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

no code implementations19 Oct 2023 Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

no code implementations24 Aug 2023 Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li

Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.

reinforcement-learning Reinforcement Learning +1

There is more to graphs than meets the eye: Learning universal features with self-supervision

no code implementations31 May 2023 Laya Das, Sai Munikoti, Nrushad Joshi, Mahantesh Halappanavar

State-of-the-art graph self-supervision restricts training to only one graph, resulting in graph-specific models that are incompatible with different but related graphs.

Node Classification Representation Learning +1

Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties

no code implementations3 Feb 2023 Ashutosh Dutta, Samrat Chatterjee, Arnab Bhattacharya, Mahantesh Halappanavar

Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics.

reinforcement-learning Reinforcement Learning (RL)

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains

1 code implementation5 Oct 2022 Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh Halappanavar

Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e. g., IID data).

Conformal Prediction valid

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach

1 code implementation3 Aug 2022 Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie

We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.

Model Predictive Control

Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications

no code implementations16 Jun 2022 Sai Munikoti, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, Balasubramaniam Natarajan

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.

Recommendation Systems

GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

no code implementations30 May 2022 Sai Munikoti, Balasubramaniam Natarajan, Mahantesh Halappanavar

However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network.

Computational Efficiency Marketing +6

Neural Lyapunov Differentiable Predictive Control

no code implementations22 May 2022 Sayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie

We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.

Model Predictive Control

Learning Stochastic Parametric Differentiable Predictive Control Policies

1 code implementation2 Mar 2022 Ján Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie

The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.

Computational Efficiency Model Predictive Control

On the Stochastic Stability of Deep Markov Models

no code implementations NeurIPS 2021 Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar

Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.

Representation Learning

V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities

1 code implementation23 Feb 2021 Siddhartha Shankar Das, Edoardo Serra, Mahantesh Halappanavar, Alex Pothen, Ehab Al-Shaer

Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system.

General Classification Link Prediction +1

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