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Nithish Kannen

6 accepted papers

2026

Fine-Tuning Diffusion Models via Intermediate Distribution Shaping

ICLR 2026poster

Diffusion models are widely used for generative tasks across domains. While pre-trained diffusion models effectively capture the training data distribution, it is often desirable to shape these distributions using reward functions to align with downstream applications. Policy gradient methods, such…

Cited by 0SourceScholar
2025

Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty

ICML 2025poster

User prompts for generative AI models are often underspecified, leading to a misalignment between the user intent and models' understanding. As a result, users commonly have to painstakingly refine their prompts. We study this alignment problem in text-to-image (T2I) generation and propose a prototy…

2024

Beyond Aesthetics: Cultural Competence in Text-to-Image Models

NeurIPS 2024poster

Text-to-Image (T2I) models are being increasingly adopted in diverse global communities where they create visual representations of their unique cultures. Current T2I benchmarks primarily focus on faithfulness, aesthetics, and realism of generated images, overlooking the critical dimension of *cultu…

Cited by 10SourcePDFScholar
2024

Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation

EMNLP 2024finding

News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pretrained language models (PLMs) to directly rank news items by using inference approaches that predominately fall into thr…

Cited by 0SourcePDFScholar
2023

Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction

EMNLP 2023short main

Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events. Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA. One of the major challenges faced by…

Cited by 0SourceScholar
2023

CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction

EMNLP 2023long findings

Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come up with a generic approach that can improve the downstream performances of multiple ABSA tasks simultaneously. Towards th…

Cited by 0SourcecodeScholar