Aneesh Komanduri
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Researcher
Also affiliated: University of Arkansas System (2025)
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Aneesh Komanduri's research focuses on the development and application of advanced machine learning models, particularly in the areas of causal inference and generative modeling. His work explores how to learn and utilize causal relationships within data to achieve more robust and controllable outcomes in artificial intelligence systems. Komanduri has investigated transformer-based language models for question answering and has published on methods for learning identifiable causal representations, such as through structural knowledge and independent causal mechanisms. His recent publications delve into causal diffusion autoencoders for counterfactual generation and emotion regulation in image sentiment recognition. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Karuna Bhaila and Xintao Wu, on shared publications. Komanduri's work contributes to the growing field of causal machine learning and its potential applications in various domains.
Metrics
- h-index: 1
- Publications: 5
- Citations: 13
Selected Publications
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Toward Causal Generative Modeling: From Representation Learning to Controllable Generation (2026)Journal of the Arkansas Academy of Science OpenAlex
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Leveraging Foundation Models for Causal Generative Modeling (2026)arXiv (Cornell University) OpenAlex
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CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025)
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Toward Causal Generative Modeling: From Representation to Generation (2025)
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Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models (2024)
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Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition (2024)
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SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge (2022)
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Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks (2021)
Collaboration Network
Top Collaborators
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
- Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition
- CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Counterfactual Thinking Driven Emotion Regulation for Image Sentiment Recognition
- Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
- Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
- CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
- Leveraging Foundation Models for Causal Generative Modeling
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