Ronny Luss

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 39

Top Topics

Alternating Minimization

Machine Learning Models

Top Venues

Publications

Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf
Contextual Moral Value Alignment Through Context-Based Aggregation. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR (2024)
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
Multi-Level Explanations for Generative Language Models. CoRR (2024)
Ronny Luss, Erik Miehling, Amit Dhurandhar
CELL your Model: Contrastive Explanation Methods for Large Language Models. CoRR (2024)
Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. Trans. Mach. Learn. Res. 2024 (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) (2024)
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
Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge. EMNLP (Findings) (2023)
Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, Anak Agung Julius
Weighted Clock Logic Point Process. ICLR (2023)
Ronny Luss, Amit Dhurandhar, Miao Liu
Local Explanations for Reinforcement Learning. AAAI (2023)
Debarun Bhattacharjya, Oktie Hassanzadeh, Ronny Luss, Keerthiram Murugesan
Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models. IJCAI (2023)
Ronny Luss, Amit Dhurandhar, Miao Liu
Local Explanations for Reinforcement Learning. CoRR (2022)
Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
Let the CAT out of the bag: Contrastive Attributed explanations for Text. EMNLP (2022)
Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. CoRR (2022)
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
AI Explainability 360: Impact and Design. AAAI (2022)
Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. HCOMP (2022)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferable Representations. ICLR (2022)
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Auto-Transfer: Learning to Route Transferrable Representations. CoRR (2022)
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
AI Explainability 360 Toolkit. COMAD/CODS (2021)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
Leveraging Latent Features for Local Explanations. KDD (2021)
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
AI Explainability 360: Impact and Design. CoRR (2021)
Saneem Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar
Let the CAT out of the bag: Contrastive Attributed explanations for Text. CoRR (2021)
Ronny Luss, Amit Dhurandhar
Towards Better Model Understanding with Path-Sufficient Explanations. CoRR (2021)
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
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
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
AI explainability 360: hands-on tutorial. FAT* (2020)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Enhancing Simple Models by Exploiting What They Already Know. ICML (2020)
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. ICML (2019)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR (2019)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Leveraging Simple Model Predictions for Enhancing its Performance. CoRR (2019)
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
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR (2019)
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. NeurIPS (2018)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
Improving Simple Models with Confidence Profiles. CoRR (2018)
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen
Improving Simple Models with Confidence Profiles. NeurIPS (2018)
Anna Choromanska, Sadhana Kumaravel, Ronny Luss, Irina Rish, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf
Beyond Backprop: Alternating Minimization with co-Activation Memory. CoRR (2018)
Peder A. Olsen, Karthikeyan Natesan Ramamurthy, Javier Ribera, Yuhao Chen, Addie M. Thompson, Ronny Luss, Mitch Tuinstra, Naoki Abe
Detecting and Counting Panicles in Sorghum Images. DSAA (2018)
Jie Chen, Ronny Luss
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators. CoRR (2018)
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. CoRR (2018)
Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
TIP: Typifying the Interpretability of Procedures. CoRR (2017)
Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam
A Formal Framework to Characterize Interpretability of Procedures. CoRR (2017)
Marek Petrik, Ronny Luss
Interpretable Policies for Dynamic Product Recommendations. UAI (2016)
Aleksandr Y. Aravkin, Anju Kambadur, Aurelie C. Lozano, Ronny Luss
Sparse Quantile Huber Regression for Efficient and Robust Estimation. CoRR (2014)
Stephen J. Buckley, Markus Ettl, Prateek Jain, Ronny Luss, Marek Petrik, Rajesh Kumar Ravi, Chitra Venkatramani
Social media and customer behavior analytics for personalized customer engagements. IBM J. Res. Dev. 58 (5/6) (2014)
Aleksandr Y. Aravkin, Aurelie C. Lozano, Ronny Luss, Prabhanjan Kambadur
Orthogonal Matching Pursuit for Sparse Quantile Regression. ICDM (2014)
Ronny Luss, Marc Teboulle
Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint. SIAM Rev. 55 (1) (2013)
Ronny Luss, Marc Teboulle
Convex approximations to sparse PCA via Lagrangian duality. Oper. Res. Lett. 39 (1) (2011)
Ronny Luss, Marc Teboulle
Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint CoRR (2011)
Ronny Luss, Saharon Rosset, Moni Shahar
Isotonic Recursive Partitioning CoRR (2011)
Ronny Luss, Saharon Rosset, Moni Shahar
Decomposing Isotonic Regression for Efficiently Solving Large Problems. NIPS (2010)
Ronny Luss, Alexandre d'Aspremont
Support vector machine classification with indefinite kernels. Math. Program. Comput. 1 (2-3) (2009)
Ronny Luss, Alexandre d'Aspremont
Support Vector Machine Classification with Indefinite Kernels CoRR (2008)
Ronny Luss, Alexandre d'Aspremont
A Cutting Plane Method for Multiple Kernel Learning CoRR (2008)
Ronny Luss, Alexandre d'Aspremont
Support Vector Machine Classification with Indefinite Kernels. NIPS (2007)
Ronny Luss, Alexandre d'Aspremont
Clustering and Feature Selection using Sparse Principal Component Analysis CoRR (2007)