Aneesh Komanduri
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Also affiliated: University of Arkansas System (2025)
Research Areas
Biography and Research Information
OverviewAI-generated summary
Aneesh Komanduri's research focuses on developing and evaluating advanced machine learning models, particularly those incorporating causal inference and generative techniques. His work has explored transformer-based language models for question answering and investigated causal diffusion autoencoders for counterfactual generation. Komanduri's publications also address learning identifiable causal representations, contributing to the understanding of structural knowledge in machine learning. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Xintao Wu and Karuna Bhaila, on multiple projects. Komanduri's scholarly output, though modest in volume, demonstrates activity in sophisticated areas of artificial intelligence and causal modeling.
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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