Attention prediction on social media brand pages. - Himabindu Lakkaraju; Jitendra Ajmera (CIKM, 2011)

Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments. - Himabindu Lakkaraju; Chiranjib Bhattacharyya; Indrajit Bhattacharya; Srujana Merugu (SDM, 2011)

Smart news feeds for social networks using scalable joint latent factor models. - Himabindu Lakkaraju; Angshu Rai; Srujana Merugu (WWW (Companion Volume), 2011)

Dynamic Multi-relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media. - Himabindu Lakkaraju; Indrajit Bhattacharya; Chiranjib Bhattacharyya (ICDM, 2012)

TEM: a novel perspective to modeling content onmicroblogs. - Himabindu Lakkaraju; Hyung-Il Ahn (WWW (Companion Volume), 2012)

What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media. - Himabindu Lakkaraju; Julian J. McAuley; Jure Leskovec (ICWSM, 2013)

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 (KDD, 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 (LAK, 2015)

A Bayesian Framework for Modeling Human Evaluations. - Himabindu Lakkaraju; Jure Leskovec; Jon M. Kleinberg; Sendhil Mullainathan (SDM, 2015)

Interpretable Decision Sets: A Joint Framework for Description and Prediction. - Himabindu Lakkaraju; Stephen H. Bach; Jure Leskovec (KDD, 2016)

Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making. - Himabindu Lakkaraju; Jure Leskovec (NIPS, 2016)

Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Eric Horvitz (CoRR, 2016)

Learning Cost-Effective Treatment Regimes using Markov Decision Processes. - Himabindu Lakkaraju; Cynthia Rudin (CoRR, 2016)

Psycho-Demographic Analysis of the Facebook Rainbow Campaign. - Yilun Wang; Himabindu Lakkaraju; Michal Kosinski; Jure Leskovec (CoRR, 2016)

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Eric Horvitz (AAAI, 2017)

Learning Cost-Effective and Interpretable Treatment Regimes. - Himabindu Lakkaraju; Cynthia Rudin (AISTATS, 2017)

The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables. - Himabindu Lakkaraju; Jon M. Kleinberg; Jure Leskovec; Jens Ludwig; Sendhil Mullainathan (KDD, 2017)

Interpretable & Explorable Approximations of Black Box Models. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Jure Leskovec (CoRR, 2017)

Human-centric machine learning: enabling machine learning for high-stakes decision-making. - Himabindu Lakkaraju (2018)

Faithful and Customizable Explanations of Black Box Models. - Himabindu Lakkaraju; Ece Kamar; Rich Caruana; Jure Leskovec (AIES, 2019)

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. - Dylan Slack; Sophie Hilgard; Emily Jia; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2019)

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations. - Himabindu Lakkaraju; Osbert Bastani (AIES, 2020)

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. - Dylan Slack; Sophie Hilgard; Emily Jia; Sameer Singh 001; Himabindu Lakkaraju (AIES, 2020)

Robust and Stable Black Box Explanations. - Himabindu Lakkaraju; Nino Arsov; Osbert Bastani (ICML, 2020)

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. - Kaivalya Rawal; Himabindu Lakkaraju (NeurIPS, 2020)

Incorporating Interpretable Output Constraints in Bayesian Neural Networks. - Wanqian Yang; Lars Lorch; Moritz A. Graule; Himabindu Lakkaraju; Finale Doshi-Velez (NeurIPS, 2020)

Fair Influence Maximization: A Welfare Optimization Approach. - Aida Rahmattalabi; Shahin Jabbari; Himabindu Lakkaraju; Phebe Vayanos; Eric Rice; Milind Tambe (CoRR, 2020)

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. - Dylan Slack; Sophie Hilgard; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2020)

Interpretable and Interactive Summaries of Actionable Recourses. - Kaivalya Rawal; Himabindu Lakkaraju (CoRR, 2020)

Ensuring Actionable Recourse via Adversarial Training. - Alexis Ross; Himabindu Lakkaraju; Osbert Bastani (CoRR, 2020)

Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses. - Kaivalya Rawal; Ece Kamar; Himabindu Lakkaraju (CoRR, 2020)

Fair Influence Maximization: a Welfare Optimization Approach. - Aida Rahmattalabi; Shahin Jabbari; Himabindu Lakkaraju; Phebe Vayanos; Max Izenberg; Ryan Brown; Eric Rice; Milind Tambe (AAAI, 2021)

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring. - Tom Sühr; Sophie Hilgard; Himabindu Lakkaraju (AIES, 2021)

Towards Reliable and Practicable Algorithmic Recourse. - Himabindu Lakkaraju (CIKM, 2021)

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. - Sushant Agarwal; Shahin Jabbari; Chirag Agarwal; Sohini Upadhyay; Steven Wu 001; Himabindu Lakkaraju (ICML, 2021)

Learning Models for Actionable Recourse. - Alexis Ross; Himabindu Lakkaraju; Osbert Bastani (NeurIPS, 2021)

Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Anna Hilgard; Himabindu Lakkaraju; Sameer Singh 001 (NeurIPS, 2021)

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. - Dylan Slack; Anna Hilgard; Sameer Singh 001; Himabindu Lakkaraju (NeurIPS, 2021)

Towards Robust and Reliable Algorithmic Recourse. - Sohini Upadhyay; Shalmali Joshi; Himabindu Lakkaraju (NeurIPS, 2021)

Towards a unified framework for fair and stable graph representation learning. - Chirag Agarwal; Himabindu Lakkaraju; Marinka Zitnik (UAI, 2021)

Learning Under Adversarial and Interventional Shifts. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (CoRR, 2021)

Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Sophie Hilgard; Himabindu Lakkaraju; Sameer Singh 001 (CoRR, 2021)

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations. - Chirag Agarwal; Marinka Zitnik; 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)

Feature Attributions and Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Sophie Hilgard; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2021)

What will it take to generate fairness-preserving explanations? - Jessica Dai; Sohini Upadhyay; Stephen H. Bach; Himabindu Lakkaraju (CoRR, 2021)

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. - Jessica Dai; Sohini Upadhyay; Ulrich Aïvodji; Stephen H. Bach; Himabindu Lakkaraju (AIES, 2022)

Towards Robust Off-Policy Evaluation via Human Inputs. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (AIES, 2022)

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. - Chirag Agarwal; Marinka Zitnik; Himabindu Lakkaraju (AISTATS, 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 (AISTATS, 2022)

A Human-Centric Perspective on Model Monitoring. - Murtuza N. Shergadwala; Himabindu Lakkaraju; Krishnaram Kenthapadi (HCOMP, 2022)

Model Monitoring in Practice: Lessons Learned and Open Challenges. - Krishnaram Kenthapadi; Himabindu Lakkaraju; Pradeep Natarajan; Mehrnoosh Sameki (KDD, 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 (NeurIPS, 2022)

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2022)

Efficient Training of Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (NeurIPS, 2022)

Data poisoning attacks on off-policy policy evaluation methods. - Elita A. Lobo; Harvineet Singh; Marek Petrik; Cynthia Rudin; Himabindu Lakkaraju (UAI, 2022)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. - Satyapriya Krishna; Tessa Han; Alex Gu; Javin Pombra; Shahin Jabbari; Steven Wu 001; Himabindu Lakkaraju (CoRR, 2022)

Rethinking Explainability as a Dialogue: A Practitioner's Perspective. - Himabindu Lakkaraju; Dylan Slack; Yuxin Chen 001; Chenhao Tan; Sameer Singh 001 (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 Stability for Attribution-based Explanations. - Chirag Agarwal; Nari Johnson; Martin Pawelczyk; Satyapriya Krishna; Eshika Saxena; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2022)

A Human-Centric Take on Model Monitoring. - Murtuza N. Shergadwala; Himabindu Lakkaraju; Krishnaram Kenthapadi (CoRR, 2022)

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (CoRR, 2022)

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. - Dylan Slack; Satyapriya Krishna; Himabindu Lakkaraju; Sameer Singh 001 (CoRR, 2022)

Evaluating Explainability for Graph Neural Networks. - Chirag Agarwal; Owen Queen; Himabindu Lakkaraju; Marinka Zitnik (CoRR, 2022)

On the Privacy Risks of Algorithmic Recourse. - Martin Pawelczyk; Himabindu Lakkaraju; Seth Neel (AISTATS, 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 (ICLR, 2023)

On the Impact of Algorithmic Recourse on Social Segregation. - Ruijiang Gao; Himabindu Lakkaraju (ICML, 2023)

Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. - Satyapriya Krishna; Jiaqi Ma 004; Himabindu Lakkaraju (ICML, 2023)

Generative AI meets Responsible AI: Practical Challenges and Opportunities. - Krishnaram Kenthapadi; Himabindu Lakkaraju; Nazneen Rajani (KDD, 2023)

M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models. - Xuhong Li 002; Mengnan Du; Jiamin Chen; Yekun Chai; Himabindu Lakkaraju; Haoyi Xiong (NeurIPS, 2023)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (NeurIPS, 2023)

Post Hoc Explanations of Language Models Can Improve Language Models. - Satyapriya Krishna; Jiaqi Ma 004; Dylan Slack; Asma Ghandeharioun; Sameer Singh 001; Himabindu Lakkaraju (NeurIPS, 2023)

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness. - Suraj Srinivas; Sebastian Bordt; Himabindu Lakkaraju (NeurIPS, 2023)

On Minimizing the Impact of Dataset Shifts on Actionable Explanations. - Anna P. Meyer; Dan Ley; Suraj Srinivas; Himabindu Lakkaraju (UAI, 2023)

Tutorials at The Web Conference 2023. - Valeria Fionda; Olaf Hartig; Reyhaneh Abdolazimi; Sihem Amer-Yahia; Hongzhi Chen; Xiao Chen 016; Peng Cui 001; Jeffrey Dalton 001; Xin Luna Dong; Lisette Espín-Noboa; Wenqi Fan; Manuela Fritz; et al. (WWW (Companion Volume), 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)

Consistent Explanations in the Face of Model Indeterminacy via Ensembling. - Dan Ley; Leonard Tang; Matthew Nazari; Hongjin Lin; 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 004; Himabindu Lakkaraju (CoRR, 2023)

Efficient Estimation of the Local Robustness of Machine Learning Models. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. - Usha Bhalla; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage. - Catherine Huang; Chelse Swoopes; Christina Xiao; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 2023)

Certifying LLM Safety against Adversarial Prompting. - Aounon Kumar; Chirag Agarwal; Suraj Srinivas; Soheil Feizi; Hima Lakkaraju (CoRR, 2023)

Are Large Language Models Post Hoc Explainers? - Nicholas Kroeger; Dan Ley; Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2023)

Investigating the Fairness of Large Language Models for Predictions on Tabular Data. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 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 R. Farhat; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2023)

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. - Sean McGrath; Parth Mehta 003; Alexandra Zytek; Isaac Lage; Himabindu Lakkaraju (Trans. Mach. Learn. Res., 2023)

Explaining machine learning models with interactive natural language conversations using TalkToModel. - Dylan Slack; Satyapriya Krishna; Himabindu Lakkaraju; Sameer Singh 001 (Nat. Mac. Intell., 2023)

On the Trade-offs between Adversarial Robustness and Actionable Explanations. - Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (AIES (1), 2024)

Fair Machine Unlearning: Data Removal while Mitigating Disparities. - Alex Oesterling; Jiaqi Ma 004; Flávio P. Calmon; Himabindu Lakkaraju (AISTATS, 2024)

Quantifying Uncertainty in Natural Language Explanations of Large Language Models. - Sree Harsha Tanneru; Chirag Agarwal; Himabindu Lakkaraju (AISTATS, 2024)

Understanding the Effects of Iterative Prompting on Truthfulness. - Satyapriya Krishna; Chirag Agarwal; Himabindu Lakkaraju (ICML, 2024)

In-Context Unlearning: Language Models as Few-Shot Unlearners. - Martin Pawelczyk; Seth Neel; Himabindu Lakkaraju (ICML, 2024)

The First Workshop on AI Behavioral Science. - Himabindu Lakkaraju; Qiaozhu Mei; Chenhao Tan; Jie Tang 001; Yutong Xie 007 (KDD, 2024)

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (NAACL-HLT, 2024)

A Study on the Calibration of In-context Learning. - Hanlin Zhang 002; Yifan Zhang 004; Yaodong Yu; Dhruv Madeka; Dean P. Foster; Eric P. Xing; Himabindu Lakkaraju; Sham M. Kakade (NAACL-HLT, 2024)

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). - Usha Bhalla; Alex Oesterling; Suraj Srinivas; Flávio P. Calmon; Himabindu Lakkaraju (NeurIPS, 2024)

MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (NeurIPS, 2024)

Characterizing Data Point Vulnerability as Average-Case Robustness. - Tessa Han; Suraj Srinivas; Himabindu Lakkaraju (UAI, 2024)

Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models. - Chirag Agarwal; Sree Harsha Tanneru; Himabindu Lakkaraju (CoRR, 2024)

Opening the Black Box of Large Language Models: Two Views on Holistic Interpretability. - Haiyan Zhao 003; Fan Yang 023; Himabindu Lakkaraju; Mengnan Du (CoRR, 2024)

Towards Safe and Aligned Large Language Models for Medicine. - Tessa Han; Aounon Kumar; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning. - Jiaqi Ma 004; Vivian Lai; Yiming Zhang 022; Chacha Chen; Paul Hamilton; Davor Ljubenkov; Himabindu Lakkaraju; Chenhao Tan (CoRR, 2024)

Manipulating Large Language Models to Increase Product Visibility. - Aounon Kumar; Himabindu Lakkaraju (CoRR, 2024)

More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness. - Aaron Jiaxun Li; Satyapriya Krishna; Himabindu Lakkaraju (CoRR, 2024)

Interpretability Needs a New Paradigm. - Andreas Madsen; Himabindu Lakkaraju; Siva Reddy; Sarath Chandar (CoRR, 2024)

On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models. - Sree Harsha Tanneru; Dan Ley; Chirag Agarwal; Himabindu Lakkaraju (CoRR, 2024)

Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers. - Alex Oesterling; Usha Bhalla; Suresh Venkatasubramanian; Himabindu Lakkaraju (CoRR, 2024)

All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models. - Charumathi Badrinath; Usha Bhalla; Alex Oesterling; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2024)

Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. - Catherine Huang; Martin Pawelczyk; Himabindu Lakkaraju (CoRR, 2024)

Learning Recourse Costs from Pairwise Feature Comparisons. - Kaivalya Rawal; Himabindu Lakkaraju (CoRR, 2024)

Generalized Group Data Attribution. - Dan Ley; Suraj Srinivas; Shichang Zhang; Gili Rusak; Himabindu Lakkaraju (CoRR, 2024)

Towards Unifying Interpretability and Control: Evaluation via Intervention. - Usha Bhalla; Suraj Srinivas; Asma Ghandeharioun; Himabindu Lakkaraju (CoRR, 2024)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. - Satyapriya Krishna; Tessa Han; Alex Gu; Steven Wu 001; Shahin Jabbari; Himabindu Lakkaraju (Trans. Mach. Learn. Res., 2024)

More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness. - Aaron Jiaxun Li; Satyapriya Krishna; Himabindu Lakkaraju (ICLR, 2025)

Quantifying Generalization Complexity for Large Language Models. - Zhenting Qi; Hongyin Luo; Xuliang Huang; Zhuokai Zhao; Yibo Jiang; Xiangjun Fan; Himabindu Lakkaraju; James R. Glass (ICLR, 2025)

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. - Zhenting Qi; Hanlin Zhang 002; Eric P. Xing; Sham M. Kakade; Himabindu Lakkaraju (ICLR, 2025)

Counterfactual Explanations May Not Be the Best Algorithmic Recourse Approach. - Sohini Upadhyay; Himabindu Lakkaraju; Krzysztof Z. Gajos (IUI, 2025)

On the Impact of Fine-Tuning on Chain-of-Thought Reasoning. - Elita A. Lobo; Chirag Agarwal; Himabindu Lakkaraju (NAACL (Long Papers), 2025)

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models. - Martin Pawelczyk; Lillian Sun; Zhenting Qi; Aounon Kumar; Himabindu Lakkaraju (CoRR, 2025)

Building Bridges, Not Walls - Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution. - Shichang Zhang; Tessa Han; Usha Bhalla; Hima Lakkaraju (CoRR, 2025)

Detecting LLM-Written Peer Reviews. - Vishisht Rao; Aounon Kumar; Himabindu Lakkaraju; Nihar B. Shah (CoRR, 2025)

Towards Interpretable Soft Prompts. - Oam Patel; Jason Wang; Nikhil Shivakumar Nayak; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2025)

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence. - Hongzhe Du; Weikai Li 002; Min Cai; Karim Saraipour; Zimin Zhang; Himabindu Lakkaraju; Yizhou Sun; Shichang Zhang (CoRR, 2025)

Soft Best-of-n Sampling for Model Alignment. - Claudio Mayrink Verdun; Alex Oesterling; Himabindu Lakkaraju; Flávio P. Calmon (CoRR, 2025)

Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models. - Zidi Xiong; Shan Chen; Zhenting Qi; Himabindu Lakkaraju (CoRR, 2025)

Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations. - Aaron J. Li; Suraj Srinivas; Usha Bhalla; Himabindu Lakkaraju (CoRR, 2025)

How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior. - Zidi Xiong; Yuping Lin; Wenya Xie; Pengfei He; Jiliang Tang; Himabindu Lakkaraju; Zhen Xiang (CoRR, 2025)

Accountability Attribution: Tracing Model Behavior to Training Processes. - Shichang Zhang; Hongzhe Du; Karim Saraipour; Jiaqi W. Ma; Himabindu Lakkaraju (CoRR, 2025)

EvoLM: In Search of Lost Language Model Training Dynamics. - Zhenting Qi; Fan Nie; Alexandre Alahi; James Zou 001; Himabindu Lakkaraju; Yilun Du; Eric P. Xing; Sham M. Kakade; Hanlin Zhang 002 (CoRR, 2025)

Inference-Time Reward Hacking in Large Language Models. - Hadi Khalaf; Claudio Mayrink Verdun; Alex Oesterling; Himabindu Lakkaraju; Flávio du Pin Calmon (CoRR, 2025)

Advancing Science- and Evidence-based AI Policy. - Rishi Bommasani; Sanjeev Arora; Jennifer T. Chayes; Yejin Choi 001; Mariano-Florentino Cuellar; Li Fei-Fei 001; Daniel E. Ho; Dan Jurafsky; Sanmi Koyejo; Hima Lakkaraju; Arvind Narayanan; Alondra Nelson; et al. (CoRR, 2025)