Karthikeyan Shanmugam

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