Rate Gap Analysis for Rate-Adaptive Antenna Selection and Beamforming Schemes. - Karthikeyan Shanmugam 001; Srikrishna Bhashyam (GLOBECOM, 2010)

A repair framework for scalar MDS codes. - Karthikeyan Shanmugam 001; Dimitris S. Papailiopoulos; Alexandros G. Dimakis; Giuseppe Caire (Allerton Conference, 2012)

Wireless video content delivery through coded distributed caching. - Negin Golrezaei; Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Andreas F. Molisch; Giuseppe Caire (ICC, 2012)

FemtoCaching: Wireless video content delivery through distributed caching helpers. - Negin Golrezaei; Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Andreas F. Molisch; Giuseppe Caire (INFOCOM, 2012)

Wireless downloading delay under proportional fair scheduling with coupled service and requests: An approximated analysis. - Karthikeyan Shanmugam 001; Giuseppe Caire (ISIT, 2012)

Index coding problem with side information repositories. - Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Giuseppe Caire (Allerton, 2013)

Local graph coloring and index coding. - Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Michael Langberg (ISIT, 2013)

FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers. - Karthikeyan Shanmugam 001; Negin Golrezaei; Alexandros G. Dimakis; Andreas F. Molisch; Giuseppe Caire (IEEE Trans. Inf. Theory, 2013)

Finite length analysis of caching-aided coded multicasting. - Karthikeyan Shanmugam 001; Mingyue Ji; Antonia M. Tulino; Jaime Llorca; Alexandros G. Dimakis (Allerton, 2014)

Bounding multiple unicasts through index coding and Locally Repairable Codes. - Karthikeyan Shanmugam 001; Alexandros G. Dimakis (ISIT, 2014)

Graph theory versus minimum rank for index coding. - Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Michael Langberg (ISIT, 2014)

Sparse Polynomial Learning and Graph Sketching. - Murat Kocaoglu; Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Adam R. Klivans (NIPS, 2014)

On the Information Theoretic Limits of Learning Ising Models. - Rashish Tandon; Karthikeyan Shanmugam 001; Pradeep Ravikumar; Alexandros G. Dimakis (NIPS, 2014)

A Smoothed Analysis for Learning Sparse Polynomials. - Alexandros G. Dimakis; Adam R. Klivans; Murat Kocaoglu; Karthikeyan Shanmugam 001 (CoRR, 2014)

An efficient multiple-groupcast coded multicasting scheme for finite fractional caching. - Mingyue Ji; Karthikeyan Shanmugam 001; Giuseppe Vettigli; Jaime Llorca; Antonia M. Tulino; Giuseppe Caire (ICC, 2015)

On approximating the sum-rate for multiple-unicasts. - Karthikeyan Shanmugam 001; Megasthenis Asteris; Alexandros G. Dimakis (ISIT, 2015)

Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs. - Ethan R. Elenberg; Karthikeyan Shanmugam 001; Michael Borokhovich; Alexandros G. Dimakis (KDD, 2015)

Learning Causal Graphs with Small Interventions. - Karthikeyan Shanmugam 001; Murat Kocaoglu; Alexandros G. Dimakis; Sriram Vishwanath (NIPS, 2015)

Distributed Estimation of Graph 4-Profiles. - Ethan R. Elenberg; Karthikeyan Shanmugam 001; Michael Borokhovich; Alexandros G. Dimakis (WWW, 2016)

A unified Ruzsa-Szemerédi framework for finite-length coded caching. - Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Jaime Llorca; Antonia M. Tulino (ACSSC, 2017)

Contextual Bandits with Latent Confounders: An NMF Approach. - Rajat Sen; Karthikeyan Shanmugam 001; Murat Kocaoglu; Alexandros G. Dimakis; Sanjay Shakkottai (AISTATS, 2017)

Identifying Best Interventions through Online Importance Sampling. - Rajat Sen; Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Sanjay Shakkottai (ICML, 2017)

Coded caching with linear subpacketization is possible using Ruzsa-Szeméredi graphs. - Karthikeyan Shanmugam 001; Antonia M. Tulino; Alexandros G. Dimakis (ISIT, 2017)

Experimental Design for Learning Causal Graphs with Latent Variables. - Murat Kocaoglu; Karthikeyan Shanmugam 001; Elias Bareinboim (NIPS, 2017)

Model-Powered Conditional Independence Test. - Rajat Sen; Ananda Theertha Suresh; Karthikeyan Shanmugam 001; Alexandros G. Dimakis; Sanjay Shakkottai (NIPS, 2017)

TIP: Typifying the Interpretability of Procedures. - Amit Dhurandhar; Vijay S. Iyengar; Ronny Luss; Karthikeyan Shanmugam 001 (CoRR, 2017)

A Formal Framework to Characterize Interpretability of Procedures. - Amit Dhurandhar; Vijay S. Iyengar; Ronny Luss; Karthikeyan Shanmugam 001 (CoRR, 2017)

Contextual Bandits with Stochastic Experts. - Rajat Sen; Karthikeyan Shanmugam 001; Sanjay Shakkottai (AISTATS, 2018)

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. - Amit Dhurandhar; Pin-Yu Chen; Ronny Luss; Chun-Chen Tu; Pai-Shun Ting; Karthikeyan Shanmugam 001; Payel Das (NeurIPS, 2018)

Improving Simple Models with Confidence Profiles. - Amit Dhurandhar; Karthikeyan Shanmugam 001; Ronny Luss; Peder A. Olsen (NeurIPS, 2018)

Structure Learning from Time Series with False Discovery Control. - Bernat Guillen Pegueroles; Bhanukiran Vinzamuri; Karthikeyan Shanmugam 001; Steve Hedden; Jonathan D. Moyer; Kush R. Varshney (CoRR, 2018)

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. - Raj Agrawal; Chandler Squires; Karren D. Yang; Karthikeyan Shanmugam 001; Caroline Uhler (AISTATS, 2019)

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes. - Tongfei Chen; Jirí Navrátil 001; Vijay S. Iyengar; Karthikeyan Shanmugam 001 (AISTATS, 2019)

Size of Interventional Markov Equivalence Classes in random DAG models. - Dmitriy Katz; Karthikeyan Shanmugam 001; Chandler Squires; Caroline Uhler (AISTATS, 2019)

Blockchain Enabled AI Marketplace: The Price You Pay for Trust. - Kanthi K. Sarpatwar; Venkata Sitaramagiridharganesh Ganapavarapu; Karthikeyan Shanmugam 001; Akond Rahman; Roman Vaculín (CVPR Workshops, 2019)

Sample Efficient Active Learning of Causal Trees. - Kristjan H. Greenewald; Dmitriy Katz; Karthikeyan Shanmugam 001; Sara Magliacane; Murat Kocaoglu; Enric Boix Adserà; Guy Bresler (NeurIPS, 2019)

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. - Murat Kocaoglu; Amin Jaber; Karthikeyan Shanmugam 001; Elias Bareinboim (NeurIPS, 2019)

Differentially Private Distributed Data Summarization under Covariate Shift. - Kanthi K. Sarpatwar; Karthikeyan Shanmugam 001; Venkata Sitaramagiridharganesh Ganapavarapu; Ashish Jagmohan; Roman Vaculín (NeurIPS, 2019)

Generating Contrastive Explanations with Monotonic Attribute Functions. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Karthikeyan Shanmugam 001; Chun-Chen Tu (CoRR, 2019)

Leveraging Simple Model Predictions for Enhancing its Performance. - Amit Dhurandhar; Karthikeyan Shanmugam 001; Ronny Luss (CoRR, 2019)

Model Agnostic Contrastive Explanations for Structured Data. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam 001; Ruchir Puri (CoRR, 2019)

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (CoRR, 2019)

Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks. - Giuseppe Vettigli; Mingyue Ji; Karthikeyan Shanmugam 001; Jaime Llorca; Antonia M. Tulino; Giuseppe Caire (Entropy, 2019)

Event-Driven Continuous Time Bayesian Networks. - Debarun Bhattacharjya; Karthikeyan Shanmugam 001; Tian Gao; Nicholas Mattei; Kush R. Varshney; Dharmashankar Subramanian (AAAI, 2020)

A Multi-Channel Neural Graphical Event Model with Negative Evidence. - Tian Gao; Dharmashankar Subramanian; Karthikeyan Shanmugam 001; Debarun Bhattacharjya; Nicholas Mattei (AAAI, 2020)

Privacy Enhanced Decision Tree Inference. - Kanthi K. Sarpatwar; Nalini K. Ratha; Karthik Nandakumar; Karthikeyan Shanmugam 001; James T. Rayfield; Sharath Pankanti; Roman Vaculín (CVPR Workshops, 2020)

AI explainability 360: hands-on tutorial. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (FAT*, 2020)

Invariant Risk Minimization Games. - Kartik Ahuja; Karthikeyan Shanmugam 001; Kush R. Varshney; Amit Dhurandhar (ICML, 2020)

Enhancing Simple Models by Exploiting What They Already Know. - Amit Dhurandhar; Karthikeyan Shanmugam 001; Ronny Luss (ICML, 2020)

Combinatorial Black-Box Optimization with Expert Advice. - Hamid Dadkhahi; Karthikeyan Shanmugam 001; Jesus Rios; Payel Das; Samuel C. Hoffman; Troy David Loeffler; Subramanian Sankaranarayanan (KDD, 2020)

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. - Zaiwei Chen; Siva Theja Maguluri; Sanjay Shakkottai; Karthikeyan Shanmugam 001 (NeurIPS, 2020)

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. - Matthew Faw; Rajat Sen; Karthikeyan Shanmugam 001; Constantine Caramanis; Sanjay Shakkottai (NeurIPS, 2020)

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. - Amin Jaber; Murat Kocaoglu; Karthikeyan Shanmugam 001; Elias Bareinboim (NeurIPS, 2020)

Learning Global Transparent Models consistent with Local Contrastive Explanations. - Tejaswini Pedapati; Avinash Balakrishnan; Karthikeyan Shanmugam 001; Amit Dhurandhar (NeurIPS, 2020)

Active Structure Learning of Causal DAGs via Directed Clique Trees. - Chandler Squires; Sara Magliacane; Kristjan H. Greenewald; Dmitriy Katz; Murat Kocaoglu; Karthikeyan Shanmugam 001 (NeurIPS, 2020)

Hawkesian Graphical Event Models. - Xiufan Yu; Karthikeyan Shanmugam 001; Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; Lingzhou Xue (PGM, 2020)

Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes. - Zaiwei Chen; Siva Theja Maguluri; Sanjay Shakkottai; Karthikeyan Shanmugam 001 (CoRR, 2020)

Learning Global Transparent Models from Local Contrastive Explanations. - Tejaswini Pedapati; Avinash Balakrishnan; Karthikeyan Shanmugam 001; Amit Dhurandhar (CoRR, 2020)

Warm Starting Bandits with Side Information from Confounded Data. - Nihal Sharma; Soumya Basu 001; Karthikeyan Shanmugam 001; Sanjay Shakkottai (CoRR, 2020)

Fair Data Integration. - Sainyam Galhotra; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Kush R. Varshney (CoRR, 2020)

Active Structure Learning of Causal DAGs via Directed Clique Tree. - Chandler Squires; Sara Magliacane; Kristjan H. Greenewald; Dmitriy Katz; Murat Kocaoglu; Karthikeyan Shanmugam 001 (CoRR, 2020)

Stochastic Linear Bandits with Protected Subspace. - Advait Parulekar; Soumya Basu 001; Aditya Gopalan; Karthikeyan Shanmugam 001; Sanjay Shakkottai (CoRR, 2020)

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models. - Qing Wang 016; Larisa Shwartz; Genady Ya. Grabarnik; Vijay Arya; Karthikeyan Shanmugam 001 (CLOUD, 2021)

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. - Kartik Ahuja; Karthikeyan Shanmugam 001; Amit Dhurandhar (AISTATS, 2021)

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. - Kristjan H. Greenewald; Karthikeyan Shanmugam 001; Dmitriy A. Katz-Rogozhnikov (AISTATS, 2021)

AI Explainability 360 Toolkit. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (COMAD/CODS, 2021)

Evaluation of Causal Inference Techniques for AIOps. - Vijay Arya; Karthikeyan Shanmugam 001; Pooja Aggarwal; Qing Wang 016; Prateeti Mohapatra; Seema Nagar (COMAD/CODS, 2021)

Treatment Effect Estimation Using Invariant Risk Minimization. - Abhin Shah; Kartik Ahuja; Karthikeyan Shanmugam 001; Dennis Wei; Kush R. Varshney; Amit Dhurandhar (ICASSP, 2021)

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. - Kartik Ahuja; Jun Wang 006; Amit Dhurandhar; Karthikeyan Shanmugam 001; Kush R. Varshney (ICLR, 2021)

Leveraging Latent Features for Local Explanations. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Yunfeng Zhang; Karthikeyan Shanmugam 001; Chun-Chen Tu (KDD, 2021)

Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators. - Zaiwei Chen; Siva Theja Maguluri; Sanjay Shakkottai; Karthikeyan Shanmugam 001 (NeurIPS, 2021)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Karthikeyan Shanmugam 001; Dennis Wei; Kush R. Varshney (NeurIPS, 2021)

Scalable Intervention Target Estimation in Linear Models. - Burak Varici; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Ali Tajer (NeurIPS, 2021)

Conditionally independent data generation. - Kartik Ahuja; Prasanna Sattigeri; Karthikeyan Shanmugam 001; Dennis Wei; Karthikeyan Natesan Ramamurthy; Murat Kocaoglu (UAI, 2021)

A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants. - Zaiwei Chen; Siva Theja Maguluri; Sanjay Shakkottai; Karthikeyan Shanmugam 001 (CoRR, 2021)

Efficient Encrypted Inference on Ensembles of Decision Trees. - Kanthi K. Sarpatwar; Karthik Nandakumar; Nalini K. Ratha; James T. Rayfield; Karthikeyan Shanmugam 001; Sharath Pankanti; Roman Vaculín (CoRR, 2021)

Episodic Bandits with Stochastic Experts. - Nihal Sharma; Soumya Basu 001; Karthikeyan Shanmugam 001; Sanjay Shakkottai (CoRR, 2021)

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. - Sainyam Galhotra; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Kush R. Varshney (Entropy, 2021)

AI Explainability 360: Impact and Design. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (AAAI, 2022)

Fourier Representations for Black-Box Optimization over Categorical Variables. - Hamid Dadkhahi; Jesus Rios; Karthikeyan Shanmugam 001; Payel Das (AAAI, 2022)

Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. - Abhin Shah; Karthikeyan Shanmugam 001; Kartik Ahuja (AISTATS, 2022)

Process Independence Testing in Proximal Graphical Event Models. - Debarun Bhattacharjya; Karthikeyan Shanmugam 001; Tian Gao; Dharmashankar Subramanian (CLeaR, 2022)

Auto-Transfer: Learning to Route Transferable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam 001; Pin-Yu Chen; Amit Dhurandhar (ICLR, 2022)

Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations. - Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Karthikeyan Shanmugam 001 (NeurIPS, 2022)

Causal Feature Selection for Algorithmic Fairness. - Sainyam Galhotra; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Kush R. Varshney (SIGMOD Conference, 2022)

Intervention target estimation in the presence of latent variables. - Burak Varici; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Ali Tajer (UAI, 2022)

Auto-Transfer: Learning to Route Transferrable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam 001; Pin-Yu Chen; Amit Dhurandhar (CoRR, 2022)

Causal Bandits for Linear Structural Equation Models. - Burak Varici; Karthikeyan Shanmugam 001; Prasanna Sattigeri; Ali Tajer (CoRR, 2022)

Fault Injection Based Interventional Causal Learning for Distributed Applications. - Qing Wang 016; Jesus Rios; Saurabh Jha; Karthikeyan Shanmugam 001; Frank Bagehorn; Xi Yang; Robert Filepp; Naoki Abe; Larisa Shwartz (AAAI, 2023)

Optimal Algorithms for Latent Bandits with Cluster Structure. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (AISTATS, 2023)

InfoNCE Loss Provably Learns Cluster-Preserving Representations. - Advait Parulekar; Liam Collins; Karthikeyan Shanmugam 001; Aryan Mokhtari; Sanjay Shakkottai (COLT, 2023)

PAC Generalization via Invariant Representations. - Advait U. Parulekar; Karthikeyan Shanmugam 001; Sanjay Shakkottai (ICML, 2023)

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (NeurIPS, 2023)

Identifiability Guarantees for Causal Disentanglement from Soft Interventions. - Jiaqi Zhang; Kristjan H. Greenewald; Chandler Squires; Akash Srivastava; Karthikeyan Shanmugam 001; Caroline Uhler (NeurIPS, 2023)

Score-based Causal Representation Learning with Interventions. - Burak Varici; Emre Acartürk; Karthikeyan Shanmugam 001; Abhishek Kumar; Ali Tajer (CoRR, 2023)

Optimal Best-Arm Identification in Bandits with Access to Offline Data. - Shubhada Agrawal; Sandeep Juneja 001; Karthikeyan Shanmugam 001; Arun Sai Suggala (CoRR, 2023)

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge. - Abhin Shah; Karthikeyan Shanmugam 001; Murat Kocaoglu (CoRR, 2023)

Time-Reversal Provides Unsupervised Feedback to LLMs. - Yerram Varun; Rahul Madhavan; Sravanti Addepalli; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (NeurIPS, 2024)

A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food. - Conor M. Artman; Aditya Mate; Ezinne Nwankwo; Aliza Heching; Tsuyoshi Idé; Jirí Navrátil 001; Karthikeyan Shanmugam 001; Wei Sun; Kush R. Varshney; Lauri Goldkind; Gidi Kroch; Jaclyn Sawyer; et al. (CoRR, 2024)

A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation. - Zaiwei Chen; Siva Theja Maguluri; Sanjay Shakkottai; Karthikeyan Shanmugam 001 (Oper. Res., 2024)

Bandits with Mean Bounds. - Nihal Sharma; Soumya Basu 001; Karthikeyan Shanmugam 001; Sanjay Shakkottai (Trans. Mach. Learn. Res., 2024)

Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes. - Nihal Sharma; Rajat Sen; Soumya Basu 001; Karthikeyan Shanmugam 001; Sanjay Shakkottai (ACM Trans. Model. Perform. Evaluation Comput. Syst., 2024)

Model Agnostic Contrastive Explanations for Classification Models. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam 001; Ruchir Puri (IEEE J. Emerg. Sel. Topics Circuits Syst., 2024)

Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? - Sravanti Addepalli; Yerram Varun; Arun Sai Suggala; Karthikeyan Shanmugam 001; Prateek Jain 002 (ICLR, 2025)

Glauber Generative Model: Discrete Diffusion Models via Binary Classification. - Harshit Varma; Dheeraj Mysore Nagaraj; Karthikeyan Shanmugam 001 (ICLR, 2025)

Online Bidding under RoS Constraints without Knowing the Value. - Sushant Vijayan; Zhe Feng 004; Swati Padmanabhan; Karthikeyan Shanmugam 001; Arun Sai Suggala; Di Wang 005 (WWW, 2025)

Interleaved Gibbs Diffusion for Constrained Generation. - Gautham Govind Anil; Sachin Yadav; Dheeraj Nagaraj; Karthikeyan Shanmugam 001; Prateek Jain 002 (CoRR, 2025)

Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo. - Advait Parulekar; Litu Rout; Karthikeyan Shanmugam 001; Sanjay Shakkottai (CoRR, 2025)