Ronny Luss

According to our database, Ronny Luss authored at least 40 papers between 2007 and 2024.

Collaborative distances:

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

2024

Multi-Level Explanations for Generative Language Models.

Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh

CoRR, 2024

Contextual Moral Value Alignment Through Context-Based Aggregation.

Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf

CoRR, 2024

2023

Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models.

Debarun Bhattacharjya, Oktie Hassanzadeh, Ronny Luss, Keerthiram Murugesan

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Weighted Clock Logic Point Process.

Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, Anak Agung Julius

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge.

Shajith Ikbal, Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar, Pavan Kapanipathi, Naweed Khan, Kyle Erwin, Ndivhuwo Makondo, Ibrahim Abdelaziz, Achille Fokoue, Alexander Gray, Maxwell Crouse, Subhajit Chaudhury, Chitra Subramanian

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Local Explanations for Reinforcement Learning.

Ronny Luss, Amit Dhurandhar, Miao Liu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Auto-Transfer: Learning to Route Transferrable Representations.

Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

CoRR, 2022

Auto-Transfer: Learning to Route Transferable Representations.

Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Proceedings of the Tenth International Conference on Learning Representations, 2022

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI.

Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar

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

Let the CAT out of the bag: Contrastive Attributed explanations for Text.

Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Towards Better Model Understanding with Path-Sufficient Explanations.

Ronny Luss, Amit Dhurandhar

CoRR, 2021

Leveraging Latent Features for Local Explanations.

Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD, 2021

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

J. Mach. Learn. Res., 2020

Enhancing Simple Models by Exploiting What They Already Know.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

Proceedings of the 37th International Conference on Machine Learning, 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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

Proceedings of the FAT* '20: Conference on Fairness, 2020

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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

CoRR, 2019

Leveraging Simple Model Predictions for Enhancing its Performance.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

CoRR, 2019

Generating Contrastive Explanations with Monotonic Attribute Functions.

Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu

CoRR, 2019

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables.

Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Stochastic Gradient Descent with Biased but Consistent Gradient Estimators.

Jie Chen, Ronny Luss

CoRR, 2018

Beyond Backprop: Alternating Minimization with co-Activation Memory.

Anna Choromanska, Sadhana Kumaravel, Ronny Luss, Irina Rish, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf

CoRR, 2018

Improving Simple Models with Confidence Profiles.

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 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, Payel Das

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Detecting and Counting Panicles in Sorghum Images.

Peder A. Olsen, Karthikeyan Natesan Ramamurthy, Javier Ribera, Yuhao Chen, Addie M. Thompson, Ronny Luss, Mitch Tuinstra, Naoki Abe

Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

2017

A Formal Framework to Characterize Interpretability of Procedures.

Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam

CoRR, 2017

TIP: Typifying the Interpretability of Procedures.

Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam

CoRR, 2017

2016

Interpretable Policies for Dynamic Product Recommendations.

Marek Petrik, Ronny Luss

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2014

Social media and customer behavior analytics for personalized customer engagements.

Stephen J. Buckley, Markus Ettl, Prateek Jain, Ronny Luss, Marek Petrik, Rajesh Kumar Ravi, Chitra Venkatramani

IBM J. Res. Dev., 2014

Sparse Quantile Huber Regression for Efficient and Robust Estimation.

Aleksandr Y. Aravkin, Anju Kambadur, Aurélie C. Lozano, Ronny Luss

CoRR, 2014

Orthogonal Matching Pursuit for Sparse Quantile Regression.

Aleksandr Y. Aravkin, Aurélie C. Lozano, Ronny Luss, Prabhanjan Kambadur

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013

Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint.

Ronny Luss, Marc Teboulle

SIAM Rev., 2013

2011

Convex approximations to sparse PCA via Lagrangian duality.

Ronny Luss, Marc Teboulle

Oper. Res. Lett., 2011

Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint

Ronny Luss, Marc Teboulle

CoRR, 2011

Isotonic Recursive Partitioning

Ronny Luss, Saharon Rosset, Moni Shahar

CoRR, 2011

2010

Decomposing Isotonic Regression for Efficiently Solving Large Problems.

Ronny Luss, Saharon Rosset, Moni Shahar

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Support vector machine classification with indefinite kernels.

Ronny Luss, Alexandre d'Aspremont

Math. Program. Comput., 2009

2008

A Cutting Plane Method for Multiple Kernel Learning

Ronny Luss, Alexandre d'Aspremont

CoRR, 2008

2007

Clustering and Feature Selection using Sparse Principal Component Analysis

Ronny Luss, Alexandre d'Aspremont

CoRR, 2007