← Authors

Karthikeyan Shanmugam

62 papers · 2014–2026 · 12 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (6) πŸƒ Academic Marathon (12) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (12) πŸ—ΊοΈ Taxonomy Completionist (21) 🌟 Keyword Trendsetter Combo (5) 🏠 Conference Loyalist (25) πŸ† Grand Slam πŸ”¬ Deep Specialist (24) πŸ† Keyword Champion (5) 🀝 Dynamic Duo (12) πŸ‘₯ Mega-Team (20) ❓ The Questioner (3) πŸ’Ž Century Club (62) πŸ—ƒοΈ Keyword Collector (50) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (10) ⚑ Prolific Year (11)

Conferences

NIPS (25) AISTATS (11) AAAI (6) ICLR (6) ICML (4) JMLR (3) UAI (2) CLEAR (1) COLT (1) MLHC (1) PGM (1) WACV (1)

Top co-authors

Amit Dhurandhar (12) Murat Kocaoglu (10) Sanjay Shakkottai (9) Ali Tajer (8) Ronny Luss (6) Prasanna Sattigeri (6) Burak Varici (5) Kartik Ahuja (5) Rajat Sen (5) Tian Gao (4)

Research topics

Differential Privacy (1) Statistics (1)

Keywords

causal inference (18) causal discovery (9) graphical model (6) soft intervention (5) latent variable (5) causal graph (5) causal representation learning (4) structural equation model (4) invariant risk minimization (3) experimental design (3) markov equivalence class (3) domain generalization (3) covariate shift (3) directed acyclic graph (3) regret bound (3) bayesian network (3) conditional independence (3) multi-armed bandit (3) importance sampling (2) semi-supervised learning (2)

Papers

Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation WACV 2026
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? ICLR 2025
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Optimizing Call Timing in Mobile Maternal Health MLHC 2025
Score-based Causal Representation Learning: Linear and General Transformations JMLR 2025
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms AISTATS 2025
Glauber Generative Model: Discrete Diffusion Models via Binary Classification ICLR 2025
Time-Reversal Provides Unsupervised Feedback to LLMs NIPS 2024
General Identifiability and Achievability for Causal Representation Learning AISTATS 2024
Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples AAAI 2024
Linear Causal Representation Learning from Unknown Multi-node Interventions NIPS 2024
Sample Complexity of Interventional Causal Representation Learning NIPS 2024
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation ICLR 2024
Learning model uncertainty as variance-minimizing instance weights ICLR 2024
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge NIPS 2023
Causal Bandits for Linear Structural Equation Models JMLR 2023
PAC Generalization via Invariant Representations ICML 2023
InfoNCE Loss Provably Learns Cluster-Preserving Representations COLT 2023
Optimal Algorithms for Latent Bandits with Cluster Structure AISTATS 2023
Fault Injection Based Interventional Causal Learning for Distributed Applications AAAI 2023
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints NIPS 2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions NIPS 2023
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge AISTATS 2022
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations NIPS 2022
Fourier Representations for Black-Box Optimization over Categorical Variables AAAI 2022
AI Explainability 360: Impact and Design AAAI 2022
Process Independence Testing in Proximal Graphical Event Models CLEAR 2022
Auto-Transfer: Learning to Route Transferable Representations ICLR 2022
Intervention target estimation in the presence of latent variables UAI 2022
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation AISTATS 2021
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions AISTATS 2021
Scalable Intervention Target Estimation in Linear Models NIPS 2021
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators NIPS 2021
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions NIPS 2021
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective ICLR 2021
Conditionally independent data generation UAI 2021
Learning Global Transparent Models consistent with Local Contrastive Explanations NIPS 2020
Active Structure Learning of Causal DAGs via Directed Clique Trees NIPS 2020
Hawkesian Graphical Event Models PGM 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions NIPS 2020
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning NIPS 2020
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes NIPS 2020
Event-Driven Continuous Time Bayesian Networks AAAI 2020
A Multi-Channel Neural Graphical Event Model with Negative Evidence AAAI 2020
Invariant Risk Minimization Games ICML 2020
Enhancing Simple Models by Exploiting What They Already Know ICML 2020
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models JMLR 2020
Size of Interventional Markov Equivalence Classes in random DAG models AISTATS 2019
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery AISTATS 2019
Sample Efficient Active Learning of Causal Trees NIPS 2019
Differentially Private Distributed Data Summarization under Covariate Shift NIPS 2019
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes AISTATS 2019
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions NIPS 2019
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives NIPS 2018
Improving Simple Models with Confidence Profiles NIPS 2018
Contextual Bandits with Stochastic Experts AISTATS 2018
Experimental Design for Learning Causal Graphs with Latent Variables NIPS 2017
Identifying Best Interventions through Online Importance Sampling ICML 2017
Model-Powered Conditional Independence Test NIPS 2017
Contextual Bandits with Latent Confounders: An NMF Approach AISTATS 2017
Learning Causal Graphs with Small Interventions NIPS 2015
On the Information Theoretic Limits of Learning Ising Models NIPS 2014
Sparse Polynomial Learning and Graph Sketching NIPS 2014