Clustering and Feature Selection using Sparse Principal Component Analysis - Ronny Luss; Alexandre d'Aspremont (CoRR, 2007)

A Cutting Plane Method for Multiple Kernel Learning - Ronny Luss; Alexandre d'Aspremont (CoRR, 2008)

Support vector machine classification with indefinite kernels. - Ronny Luss; Alexandre d'Aspremont (Math. Program. Comput., 2009)

Decomposing Isotonic Regression for Efficiently Solving Large Problems. - Ronny Luss; Saharon Rosset; Moni Shahar (NIPS, 2010)

Isotonic Recursive Partitioning - Ronny Luss; Saharon Rosset; Moni Shahar (CoRR, 2011)

Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint - Ronny Luss; Marc Teboulle (CoRR, 2011)

Convex approximations to sparse PCA via Lagrangian duality. - Ronny Luss; Marc Teboulle (Oper. Res. Lett., 2011)

Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint. - Ronny Luss; Marc Teboulle (SIAM Rev., 2013)

Orthogonal Matching Pursuit for Sparse Quantile Regression. - Aleksandr Y. Aravkin; Aurélie C. Lozano; Ronny Luss; Prabhanjan Kambadur (ICDM, 2014)

Sparse Quantile Huber Regression for Efficient and Robust Estimation. - Aleksandr Y. Aravkin; Anju Kambadur; Aurélie C. Lozano; Ronny Luss (CoRR, 2014)

Social media and customer behavior analytics for personalized customer engagements. - Stephen J. Buckley; Markus Ettl; Prateek Jain 001; Ronny Luss; Marek Petrik; Rajesh Kumar Ravi; Chitra Venkatramani (IBM J. Res. Dev., 2014)

Interpretable Policies for Dynamic Product Recommendations. - Marek Petrik; Ronny Luss (UAI, 2016)

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

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

Detecting and Counting Panicles in Sorghum Images. - Peder A. Olsen; Karthikeyan Natesan Ramamurthy; Javier Ribera; Yuhao Chen 001; Addie M. Thompson; Ronny Luss; Mitch Tuinstra; Naoki Abe (DSAA, 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 (NeurIPS, 2018)

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

Beyond Backprop: Alternating Minimization with co-Activation Memory. - Anna Choromanska; Sadhana Kumaravel; Ronny Luss; Irina Rish; Brian Kingsbury; Ravi Tejwani; Djallel Bouneffouf 001 (CoRR, 2018)

Stochastic Gradient Descent with Biased but Consistent Gradient Estimators. - Jie Chen 007; Ronny Luss (CoRR, 2018)

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 001 (ICML, 2019)

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

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

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)

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

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)

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

Towards Better Model Understanding with Path-Sufficient Explanations. - Ronny Luss; Amit Dhurandhar (CoRR, 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)

Let the CAT out of the bag: Contrastive Attributed explanations for Text. - Saneem A. Chemmengath; Amar Prakash Azad; Ronny Luss; Amit Dhurandhar (EMNLP, 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 (HCOMP, 2022)

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

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

Local Explanations for Reinforcement Learning. - Ronny Luss; Amit Dhurandhar; Miao Liu (AAAI, 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; et al. (EMNLP (Findings), 2023)

Weighted Clock Logic Point Process. - Ruixuan Yan; Yunshi Wen; Debarun Bhattacharjya; Ronny Luss; Tengfei Ma 001; Achille Fokoue; Anak Agung Julius (ICLR, 2023)

Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models. - Debarun Bhattacharjya; Oktie Hassanzadeh; Ronny Luss; Keerthiram Murugesan (IJCAI, 2023)

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. - Amit Dhurandhar; Tejaswini Pedapati; Ronny Luss; Soham Dan; Aurélie C. Lozano; Payel Das; Georgios Kollias (ACL (Findings), 2024)

ComVas: Contextual Moral Values Alignment System. - Inkit Padhi; Pierre L. Dognin; Jesus Rios; Ronny Luss; Swapnaja Achintalwar; Matthew Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (IJCAI, 2024)

Contextual Moral Value Alignment Through Context-Based Aggregation. - Pierre L. Dognin; Jesus Rios; Ronny Luss; Inkit Padhi; Matthew D. Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (CoRR, 2024)

CELL your Model: Contrastive Explanation Methods for Large Language Models. - Ronny Luss; Erik Miehling; Amit Dhurandhar (CoRR, 2024)

When Stability meets Sufficiency: Informative Explanations that do not Overwhelm. - Ronny Luss; Amit Dhurandhar (Trans. Mach. Learn. Res., 2024)

To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. - Vijay Sadashivaiah; Keerthiram Murugesan; Ronny Luss; Pin-Yu Chen; Chris R. Sims; James A. Hendler; Amit Dhurandhar (Trans. Mach. Learn. Res., 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 (ACL (1), 2025)

Contextual Value Alignment. - Pierre Dognin; Jesus Rios; Ronny Luss; Prasanna Sattigeri; Miao Liu 001; Inkit Padhi; Matthew Riemer; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (ICASSP, 2025)

Shedding Light on Time Series Classification using Interpretability Gated Networks. - Yunshi Wen; Tengfei Ma 001; Ronny Luss; Debarun Bhattacharjya; Achille Fokoue; Anak Agung Julius (ICLR, 2025)

Sparsity May Be All You Need: Sparse Random Parameter Adaptation. - Jesus Rios; Pierre L. Dognin; Ronny Luss; Karthikeyan Ramamurthy (CoRR, 2025)