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2020 – today
- 2024
[j6]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)- [c22]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: 2416-2430 - [c21]Inkit Padhi, Pierre L. Dognin, Jesus Rios, Ronny Luss, Swapnaja Achintalwar, Matthew Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf:
ComVas: Contextual Moral Values Alignment System. IJCAI 2024: 8759-8762 - [i25]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 abs/2403.12805 (2024) - [i24]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 abs/2403.14459 (2024) - [i23]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 abs/2404.01306 (2024) - [i22]Ronny Luss, Erik Miehling, Amit Dhurandhar:
CELL your Model: Contrastive Explanation Methods for Large Language Models. CoRR abs/2406.11785 (2024) - 2023
- [c20]Ronny Luss, Amit Dhurandhar, Miao Liu:
Local Explanations for Reinforcement Learning. AAAI 2023: 9002-9010 - [c19]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: 1707-1718 - [c18]Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, Anak Agung Julius:
Weighted Clock Logic Point Process. ICLR 2023 - [c17]Debarun Bhattacharjya, Oktie Hassanzadeh, Ronny Luss, Keerthiram Murugesan:
Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models. IJCAI 2023: 5667-5675 - 2022
- [c16]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: 12651-12657 - [c15]Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar:
Let the CAT out of the bag: Contrastive Attributed explanations for Text. EMNLP 2022: 7190-7206 - [c14]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: 147-159 - [c13]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferable Representations. ICLR 2022 - [i21]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferrable Representations. CoRR abs/2202.01011 (2022) - [i20]Ronny Luss, Amit Dhurandhar, Miao Liu:
Local Explanations for Reinforcement Learning. CoRR abs/2202.03597 (2022) - [i19]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 abs/2206.10847 (2022) - 2021
- [c12]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: 376-379 - [c11]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu:
Leveraging Latent Features for Local Explanations. KDD 2021: 1139-1149 - [i18]Ronny Luss, Amit Dhurandhar:
Towards Better Model Understanding with Path-Sufficient Explanations. CoRR abs/2109.06181 (2021) - [i17]Saneem A. Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar:
Let the CAT out of the bag: Contrastive Attributed explanations for Text. CoRR abs/2109.07983 (2021) - [i16]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 abs/2109.12151 (2021) - 2020
- [j5]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: 130:1-130:6 (2020) - [c10]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: 696 - [c9]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss:
Enhancing Simple Models by Exploiting What They Already Know. ICML 2020: 2525-2534
2010 – 2019
- 2019
- [c8]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: 1193-1202 - [i15]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu:
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR abs/1905.12698 (2019) - [i14]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss:
Leveraging Simple Model Predictions for Enhancing its Performance. CoRR abs/1905.13565 (2019) - [i13]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 abs/1909.03012 (2019) - 2018
- [c7]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: 400-409 - [c6]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: 590-601 - [c5]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen:
Improving Simple Models with Confidence Profiles. NeurIPS 2018: 10317-10327 - [i12]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 abs/1802.07623 (2018) - [i11]Anna Choromanska, Sadhana Kumaravel, Ronny Luss, Irina Rish, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf:
Beyond Backprop: Alternating Minimization with co-Activation Memory. CoRR abs/1806.09077 (2018) - [i10]Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen:
Improving Simple Models with Confidence Profiles. CoRR abs/1807.07506 (2018) - [i9]Jie Chen, Ronny Luss:
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators. CoRR abs/1807.11880 (2018) - 2017
- [i8]Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam:
TIP: Typifying the Interpretability of Procedures. CoRR abs/1706.02952 (2017) - [i7]Amit Dhurandhar, Vijay S. Iyengar, Ronny Luss, Karthikeyan Shanmugam:
A Formal Framework to Characterize Interpretability of Procedures. CoRR abs/1707.03886 (2017) - 2016
- [c4]Marek Petrik, Ronny Luss:
Interpretable Policies for Dynamic Product Recommendations. UAI 2016 - 2014
- [j4]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) - [c3]Aleksandr Y. Aravkin, Aurélie C. Lozano, Ronny Luss, Prabhanjan Kambadur:
Orthogonal Matching Pursuit for Sparse Quantile Regression. ICDM 2014: 11-19 - [i6]Aleksandr Y. Aravkin, Anju Kambadur, Aurélie C. Lozano, Ronny Luss:
Sparse Quantile Huber Regression for Efficient and Robust Estimation. CoRR abs/1402.4624 (2014) - 2013
- [j3]Ronny Luss, Marc Teboulle:
Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint. SIAM Rev. 55(1): 65-98 (2013) - 2011
- [j2]Ronny Luss, Marc Teboulle:
Convex approximations to sparse PCA via Lagrangian duality. Oper. Res. Lett. 39(1): 57-61 (2011) - [i5]Ronny Luss, Saharon Rosset, Moni Shahar:
Isotonic Recursive Partitioning. CoRR abs/1102.5496 (2011) - [i4]Ronny Luss, Marc Teboulle:
Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint. CoRR abs/1107.1163 (2011) - 2010
- [c2]Ronny Luss, Saharon Rosset, Moni Shahar:
Decomposing Isotonic Regression for Efficiently Solving Large Problems. NIPS 2010: 1513-1521
2000 – 2009
- 2009
- [j1]Ronny Luss, Alexandre d'Aspremont:
Support vector machine classification with indefinite kernels. Math. Program. Comput. 1(2-3): 97-118 (2009) - 2008
- [i3]Ronny Luss, Alexandre d'Aspremont:
Support Vector Machine Classification with Indefinite Kernels. CoRR abs/0804.0188 (2008) - [i2]Ronny Luss, Alexandre d'Aspremont:
A Cutting Plane Method for Multiple Kernel Learning. CoRR abs/0809.2792 (2008) - 2007
- [c1]Ronny Luss, Alexandre d'Aspremont:
Support Vector Machine Classification with Indefinite Kernels. NIPS 2007: 953-960 - [i1]Ronny Luss, Alexandre d'Aspremont:
Clustering and Feature Selection using Sparse Principal Component Analysis. CoRR abs/0707.0701 (2007)
Coauthor Index
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