Chandan Singh
14 papers · 2019–2025 · 7 conferences · across top CS/AI conferences
Achievements
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🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (7) 🏃 Academic Marathon (6) 🐝 Cross-Pollinator (14)
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Interdisciplinary Bridge
🌍
Conference Polyglot
(7)
🏃
Academic Marathon
(6)
🧬
Topic Evolution
💎
Century Club
(14)
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Keyword Collector
(58)
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Unstoppable
(7)
Conferences
EMNLP (3)
ICLR (3)
ICML (3)
NIPS (2)
COLING (1)
JMLR (1)
WACV (1)
Top co-authors
Research topics
Keywords
large language model
(3)
decision tree
(2)
ridge regression
(2)
prompt engineering
(2)
natural language explanation
(2)
neural network
(2)
multimodal learning
(1)
deep learning
(1)
statistical learning theory
(1)
feature attribution
(1)
feature importance
(1)
out-of-distribution generalization
(1)
ai safety
(1)
model interpretability
(1)
question answering
(1)
generalization bound
(1)
minimum description length
(1)
wavelet transform
(1)
synthetic datum
(1)
prior knowledge
(1)
Papers
Simplifying DINO via Coding Rate Regularization
ICML 2025
Vector-ICL: In-context Learning with Continuous Vector Representations
ICLR 2025
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
COLING 2025
MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities
EMNLP 2025
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
ICLR 2024
Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions
NIPS 2024
Orthogonal Transforms for Learning Invariant Representations in Equivariant Neural Networks
WACV 2023
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
EMNLP 2023
Explaining Data Patterns in Natural Language with Language Models
EMNLP 2023
Revisiting minimum description length complexity in overparameterized models
JMLR 2023
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.
ICML 2022
Adaptive wavelet distillation from neural networks through interpretations
NIPS 2021
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
ICML 2020
Hierarchical interpretations for neural network predictions
ICLR 2019