← Authors

Amit Dhurandhar

29 papers · 2008–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (50) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (8) 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (17) 🌟 Keyword Trendsetter Combo (5) 🀝 Dynamic Duo (12) πŸ† Grand Slam πŸ”¬ Deep Specialist (12) 🧬 Topic Evolution πŸ† Keyword Champion (4) πŸ‘₯ Mega-Team (20) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (5) ❓ The Questioner (3) πŸ’Ž Century Club (29) πŸ—ƒοΈ Keyword Collector (109)

Conferences

NIPS (8) AAAI (4) ACL (4) ICML (4) ICLR (3) JMLR (3) AISTATS (1) ECCV (1) EMNLP (1)

Top co-authors

Karthikeyan Shanmugam (12) Ronny Luss (10) Karthikeyan Natesan Ramamurthy (8) Dennis Wei (6) Pin-Yu Chen (6) Moninder Singh (5) Kartik Ahuja (4) Yunfeng Zhang (3) Vijay Arya (3) Payel Das (3)

Keywords

local explanation (4) feature attribution (4) explainable ai (4) large language model (4) neural network (3) transfer learning (3) invariant risk minimization (3) decision tree (3) domain generalization (3) model interpretability (3) contrastive explanation (2) interpretable model (2) nash equilibrium (2) data augmentation (1) knowledge distillation (1) model selection (1) knowledge transfer (1) text classification (1) semi-supervised learning (1) contrastive learning (1)

Papers

Multi-Level Explanations for Generative Language Models ACL 2025
Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents ACL 2025
Programming Refusal with Conditional Activation Steering ICLR 2025
Ranking Large Language Models without Ground Truth ACL 2024
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models ACL 2024
Trust Regions for Explanations via Black-Box Probabilistic Certification ICML 2024
Integrating Markov Blanket Discovery into Causal Representation Learning for Domain Generalization ECCV 2024
Local Explanations for Reinforcement Learning AAAI 2023
Reprogramming Pretrained Language Models for Antibody Sequence Infilling ICML 2023
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning NIPS 2023
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective AAAI 2023
Auto-Transfer: Learning to Route Transferable Representations ICLR 2022
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations NIPS 2022
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach NIPS 2022
AI Explainability 360: Impact and Design AAAI 2022
Let the CAT out of the bag: Contrastive Attributed explanations for Text EMNLP 2022
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions AISTATS 2021
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions NIPS 2021
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective ICLR 2021
Anomaly Attribution with Likelihood Compensation AAAI 2021
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models JMLR 2020
Learning Global Transparent Models consistent with Local Contrastive Explanations NIPS 2020
Model Agnostic Multilevel Explanations NIPS 2020
Invariant Risk Minimization Games ICML 2020
Enhancing Simple Models by Exploiting What They Already Know ICML 2020
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives NIPS 2018
Improving Simple Models with Confidence Profiles NIPS 2018
Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions JMLR 2014
Probabilistic Characterization of Random Decision Trees JMLR 2008