Himabindu Lakkaraju

According to our database, Himabindu Lakkaraju authored at least 99 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning.

Jiaqi Ma, Vivian Lai, Yiming Zhang, Chacha Chen, Paul Hamilton, Davor Ljubenkov, Himabindu Lakkaraju, Chenhao Tan

CoRR, 2024

Towards Safe and Aligned Large Language Models for Medicine.

Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2024

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems.

Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju

CoRR, 2024

Opening the Black Box of Large Language Models: Two Views on Holistic Interpretability.

Haiyan Zhao, Fan Yang, Himabindu Lakkaraju, Mengnan Du

CoRR, 2024

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE).

Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju

CoRR, 2024

Understanding the Effects of Iterative Prompting on Truthfulness.

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2024

Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models.

Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju

CoRR, 2024

2023

Explaining machine learning models with interactive natural language conversations using TalkToModel.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

Nat. Mac. Intell., August, 2023

Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Experts.

Tessa Han, Yasha Ektefaie, Maha Farhat, Marinka Zitnik, Himabindu Lakkaraju

CoRR, 2023

A Study on the Calibration of In-context Learning.

Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade

CoRR, 2023

Quantifying Uncertainty in Natural Language Explanations of Large Language Models.

Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2023

Investigating the Fairness of Large Language Models for Predictions on Tabular Data.

Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju

CoRR, 2023

In-Context Unlearning: Language Models as Few Shot Unlearners.

Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju

CoRR, 2023

Are Large Language Models Post Hoc Explainers?

Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2023

On the Trade-offs between Adversarial Robustness and Actionable Explanations.

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

CoRR, 2023

Certifying LLM Safety against Adversarial Prompting.

Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, Hima Lakkaraju

CoRR, 2023

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage.

Catherine Huang, Chelse Swoopes, Christina Xiao, Jiaqi Ma, Himabindu Lakkaraju

CoRR, 2023

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability.

Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Fair Machine Unlearning: Data Removal while Mitigating Disparities.

Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Hima Lakkaraju

CoRR, 2023

Efficient Estimation of the Local Robustness of Machine Learning Models.

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions.

Skyler Wu, Eric Meng Shen, Charumathi Badrinath, Jiaqi Ma, Himabindu Lakkaraju

CoRR, 2023

Consistent Explanations in the Face of Model Indeterminacy via Ensembling.

Dan Ley, Leonard Tang, Matthew Nazari, Hongjin Lin, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Word-Level Explanations for Analyzing Bias in Text-to-Image Models.

Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju

CoRR, 2023

Tutorials at The Web Conference 2023.

Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

On Minimizing the Impact of Dataset Shifts on Actionable Explanations.

Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness.

Suraj Srinivas, Sebastian Bordt, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Post Hoc Explanations of Language Models Can Improve Language Models.

Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability.

Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

M⁴: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models.

Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generative AI meets Responsible AI: Practical Challenges and Opportunities.

Krishnaram Kenthapadi, Himabindu Lakkaraju, Nazneen Rajani

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten.

Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju

Proceedings of the International Conference on Machine Learning, 2023

On the Impact of Algorithmic Recourse on Social Segregation.

Ruijiang Gao, Himabindu Lakkaraju

Proceedings of the International Conference on Machine Learning, 2023

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse.

Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On the Privacy Risks of Algorithmic Recourse.

Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Evaluating Explainability for Graph Neural Networks.

Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, Marinka Zitnik

CoRR, 2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

CoRR, 2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

CoRR, 2022

A Human-Centric Take on Model Monitoring.

Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi

CoRR, 2022

Rethinking Stability for Attribution-based Explanations.

Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju

CoRR, 2022

Algorithmic Recourse in the Face of Noisy Human Responses.

Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju

CoRR, 2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective.

Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh

CoRR, 2022

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju

CoRR, 2022

Data poisoning attacks on off-policy policy evaluation methods.

Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Efficient Training of Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations.

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations.

Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model Monitoring in Practice: Lessons Learned and Open Challenges.

Krishnaram Kenthapadi, Himabindu Lakkaraju, Pradeep Natarajan, Mehrnoosh Sameki

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

A Human-Centric Perspective on Model Monitoring.

Murtuza N. Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi

Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.

Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods.

Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Robust Off-Policy Evaluation via Human Inputs.

Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations.

Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021

What will it take to generate fairness-preserving explanations?

Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju

CoRR, 2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

CoRR, 2021

On the Connections between Counterfactual Explanations and Adversarial Examples.

Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju

CoRR, 2021

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations.

Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju

CoRR, 2021

Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh

CoRR, 2021

Learning Under Adversarial and Interventional Shifts.

Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

CoRR, 2021

Towards a unified framework for fair and stable graph representation learning.

Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Towards Robust and Reliable Algorithmic Recourse.

Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability.

Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Models for Actionable Recourse.

Alexis Ross, Himabindu Lakkaraju, Osbert Bastani

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.

Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju

Proceedings of the 38th International Conference on Machine Learning, 2021

Towards Reliable and Practicable Algorithmic Recourse.

Himabindu Lakkaraju

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring.

Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

Fair Influence Maximization: a Welfare Optimization Approach.

Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses.

Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju

CoRR, 2020

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making.

Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, Himabindu Lakkaraju

CoRR, 2020

Ensuring Actionable Recourse via Adversarial Training.

Alexis Ross, Himabindu Lakkaraju, Osbert Bastani

CoRR, 2020

Interpretable and Interactive Summaries of Actionable Recourses.

Kaivalya Rawal, Himabindu Lakkaraju

CoRR, 2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations.

Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

CoRR, 2020

Fair Influence Maximization: A Welfare Optimization Approach.

Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe

CoRR, 2020

Incorporating Interpretable Output Constraints in Bayesian Neural Networks.

Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses.

Kaivalya Rawal, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust and Stable Black Box Explanations.

Himabindu Lakkaraju, Nino Arsov, Osbert Bastani

Proceedings of the 37th International Conference on Machine Learning, 2020

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations.

Himabindu Lakkaraju, Osbert Bastani

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods.

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

CoRR, 2019

Faithful and Customizable Explanations of Black Box Models.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

Human-centric machine learning: enabling machine learning for high-stakes decision-making.

Himabindu Lakkaraju

PhD thesis, 2018

2017

Interpretable & Explorable Approximations of Black Box Models.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

CoRR, 2017

The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables.

Himabindu Lakkaraju, Jon M. Kleinberg, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Learning Cost-Effective and Interpretable Treatment Regimes.

Himabindu Lakkaraju, Cynthia Rudin

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Psycho-Demographic Analysis of the Facebook Rainbow Campaign.

Yilun Wang, Himabindu Lakkaraju, Michal Kosinski, Jure Leskovec

CoRR, 2016

Learning Cost-Effective Treatment Regimes using Markov Decision Processes.

Himabindu Lakkaraju, Cynthia Rudin

CoRR, 2016

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration.

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

CoRR, 2016

Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making.

Himabindu Lakkaraju, Jure Leskovec

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Interpretable Decision Sets: A Joint Framework for Description and Prediction.

Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

A Bayesian Framework for Modeling Human Evaluations.

Himabindu Lakkaraju, Jure Leskovec, Jon M. Kleinberg, Sendhil Mullainathan

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time.

Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia L. Addison

Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 2015

A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes.

Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, Kecia L. Addison

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2013

What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media.

Himabindu Lakkaraju, Julian J. McAuley, Jure Leskovec

Proceedings of the Seventh International Conference on Weblogs and Social Media, 2013

2012

TEM: a novel perspective to modeling content onmicroblogs.

Himabindu Lakkaraju, Hyung-Il Ahn

Proceedings of the 21st World Wide Web Conference, 2012

Dynamic Multi-relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media.

Himabindu Lakkaraju, Indrajit Bhattacharya, Chiranjib Bhattacharyya

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

2011

Smart news feeds for social networks using scalable joint latent factor models.

Himabindu Lakkaraju, Angshu Rai, Srujana Merugu

Proceedings of the 20th International Conference on World Wide Web, 2011

Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments.

Himabindu Lakkaraju, Chiranjib Bhattacharyya, Indrajit Bhattacharya, Srujana Merugu

Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Attention prediction on social media brand pages.

Himabindu Lakkaraju, Jitendra Ajmera

Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011