@misc{padhi2024graniteguardian,title={Granite Guardian},author={Padhi, Inkit and Nagireddy, Manish and Cornacchia, Giandomenico and Chaudhury, Subhajit and Pedapati, Tejaswini and Dognin, Pierre and Murugesan, Keerthiram and Miehling, Erik and Cooper, Martín Santillán and Fraser, Kieran and Zizzo, Giulio and Hameed, Muhammad Zaid and Purcell, Mark and Desmond, Michael and Pan, Qian and Vejsbjerg, Inge and Daly, Elizabeth M. and Hind, Michael and Geyer, Werner and Rawat, Ambrish and Varshney, Kush R. and Sattigeri, Prasanna},year={2024},eprint={2412.07724},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2412.07724},}
Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods
Dennis Wei, Inkit Padhi, Soumya Ghosh, and 3 more authors
@misc{wei2024finalmodelonlydataattributionunifying,title={Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods},author={Wei, Dennis and Padhi, Inkit and Ghosh, Soumya and Dhurandhar, Amit and Ramamurthy, Karthikeyan Natesan and Chang, Maria},year={2024},eprint={2412.03906},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2412.03906},}
Value Alignment from Unstructured Text
Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, and 3 more authors
In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, Nov 2024
@inproceedings{padhi-etal-2024-value,title={Value Alignment from Unstructured Text},author={Padhi, Inkit and Natesan Ramamurthy, Karthikeyan and Sattigeri, Prasanna and Nagireddy, Manish and Dognin, Pierre and Varshney, Kush},editor={Dernoncourt, Franck and Preo{\c{t}}iuc-Pietro, Daniel and Shimorina, Anastasia},booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},month=nov,year={2024},address={Miami, Florida, US},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2024.emnlp-industry.81},pages={1083--1095},}
Programming Refusal with Conditional Activation Steering
Bruce W. Lee, Inkit Padhi, Karthikeyan Natesan Ramamurthy, and 4 more authors
@misc{lee2024programmingrefusalconditionalactivation,title={Programming Refusal with Conditional Activation Steering},author={Lee, Bruce W. and Padhi, Inkit and Ramamurthy, Karthikeyan Natesan and Miehling, Erik and Dognin, Pierre and Nagireddy, Manish and Dhurandhar, Amit},year={2024},eprint={2409.05907},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2409.05907},}
When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails
Manish Nagireddy, Inkit Padhi, Soumya Ghosh, and 1 more author
@article{Nagireddy2024WhenID,title={When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails},author={Nagireddy, Manish and Padhi, Inkit and Ghosh, Soumya and Sattigeri, Prasanna},journal={ArXiv},year={2024},volume={abs/2407.06323},url={https://api.semanticscholar.org/CorpusID:271064989},}
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, and 5 more authors
@misc{hou2024wikicontradictbenchmarkevaluatingllms,title={WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia},author={Hou, Yufang and Pascale, Alessandra and Carnerero-Cano, Javier and Tchrakian, Tigran and Marinescu, Radu and Daly, Elizabeth and Padhi, Inkit and Sattigeri, Prasanna},year={2024},eprint={2406.13805},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2406.13805},}
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs
Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei, and 2 more authors
@misc{kadhe2024splitunlearnmergeleveraging,title={Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs},author={Kadhe, Swanand Ravindra and Ahmed, Farhan and Wei, Dennis and Baracaldo, Nathalie and Padhi, Inkit},year={2024},eprint={2406.11780},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2406.11780},}
Contextual Moral Value Alignment Through Context-Based Aggregation
Pierre Dognin, Jesus Rios, Ronny Luss, and 7 more authors
@misc{dognin2024contextualmoralvaluealignment,title={Contextual Moral Value Alignment Through Context-Based Aggregation},author={Dognin, Pierre and Rios, Jesus and Luss, Ronny and Padhi, Inkit and Riemer, Matthew D and Liu, Miao and Sattigeri, Prasanna and Nagireddy, Manish and Varshney, Kush R. and Bouneffouf, Djallel},year={2024},eprint={2403.12805},archiveprefix={arXiv},primaryclass={cs.AI},url={https://arxiv.org/abs/2403.12805},}
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, and 35 more authors
@misc{achintalwar2024detectorssafereliablellms,title={Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations},author={Achintalwar, Swapnaja and Garcia, Adriana Alvarado and Anaby-Tavor, Ateret and Baldini, Ioana and Berger, Sara E. and Bhattacharjee, Bishwaranjan and Bouneffouf, Djallel and Chaudhury, Subhajit and Chen, Pin-Yu and Chiazor, Lamogha and Daly, Elizabeth M. and DB, Kirushikesh and de Paula, Rogério Abreu and Dognin, Pierre and Farchi, Eitan and Ghosh, Soumya and Hind, Michael and Horesh, Raya and Kour, George and Lee, Ja Young and Madaan, Nishtha and Mehta, Sameep and Miehling, Erik and Murugesan, Keerthiram and Nagireddy, Manish and Padhi, Inkit and Piorkowski, David and Rawat, Ambrish and Raz, Orna and Sattigeri, Prasanna and Strobelt, Hendrik and Swaminathan, Sarathkrishna and Tillmann, Christoph and Trivedi, Aashka and Varshney, Kush R. and Wei, Dennis and Witherspooon, Shalisha and Zalmanovici, Marcel},year={2024},eprint={2403.06009},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2403.06009},}
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, and 16 more authors
@article{10666776,author={Achintalwar, Swapnaja and Baldini, Ioana and Bouneffouf, Djallel and Byamugisha, Joan and Chang, Maria and Dognin, Pierre and Farchi, Eitan and Makondo, Ndivhuwo and Mojsilović, Aleksandra and Nagireddy, Manish and NatesanRamamurthy, Karthikeyan and Padhi, Inkit and Raz, Orna and Rios, Jesus and Sattigeri, Prasanna and Singh, Moninder and Thwala, Siphiwe and Uceda-Sosa, Rosario A. and Varshney, Kush R.},journal={IEEE Internet Computing},title={Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations},year={2024},volume={},number={},pages={1-6},keywords={Data models;Regulation;Internet;Guidelines;Synthetic data;Chatbots;Tuning},doi={10.1109/MIC.2024.3453671},}
ComVas: Contextual Moral Values Alignment System
Inkit Padhi, Pierre Dognin, Jesus Rios, and 8 more authors
In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Aug 2024
@inproceedings{ijcai2024p1026,title={ComVas: Contextual Moral Values Alignment System},author={Padhi, Inkit and Dognin, Pierre and Rios, Jesus and Luss, Ronny and Achintalwar, Swapnaja and Riemer, Matthew and Liu, Miao and Sattigeri, Prasanna and Nagireddy, Manish and Varshney, Kush R. and Bouneffouf, Djallel},booktitle={Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},publisher={International Joint Conferences on Artificial Intelligence Organization},editor={Larson, Kate},pages={8759--8762},year={2024},month=aug,note={Demo Track},doi={10.24963/ijcai.2024/1026},url={https://doi.org/10.24963/ijcai.2024/1026},}
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Brian Belgodere, Pierre Dognin, Adam Ivankay, and 11 more authors
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Aug 2024
@article{Belgodere2024,title={Auditing and Generating Synthetic Data with Controllable Trust Trade-offs},issn={2156-3365},url={http://dx.doi.org/10.1109/JETCAS.2024.3477976},doi={10.1109/jetcas.2024.3477976},journal={IEEE Journal on Emerging and Selected Topics in Circuits and Systems},publisher={Institute of Electrical and Electronics Engineers (IEEE)},author={Belgodere, Brian and Dognin, Pierre and Ivankay, Adam and Melnyk, Igor and Mroueh, Youssef and Mojsilovic, Aleksandra and Navratil, Jiri and Nitsure, Apoorva and Padhi, Inkit and Rigotti, Mattia and Ross, Jerret and Schiff, Yair and Vedpathak, Radhika and Young, Richard A.},year={2024},pages={1–1},}
2023
The Impact of Positional Encoding on Length Generalization in Transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, and 2 more authors
In Advances in Neural Information Processing Systems, Aug 2023
@inproceedings{NEURIPS2023_4e85362c,author={Kazemnejad, Amirhossein and Padhi, Inkit and Natesan Ramamurthy, Karthikeyan and Das, Payel and Reddy, Siva},booktitle={Advances in Neural Information Processing Systems},editor={Oh, A. and Naumann, T. and Globerson, A. and Saenko, K. and Hardt, M. and Levine, S.},pages={24892--24928},publisher={Curran Associates, Inc.},title={The Impact of Positional Encoding on Length Generalization in Transformers},url={https://proceedings.neurips.cc/paper_files/paper/2023/file/4e85362c02172c0c6567ce593122d31c-Paper-Conference.pdf},volume={36},year={2023},}
Influence Based Approaches to Algorithmic Fairness: A Closer Look
Soumya Ghosh, Prasanna Sattigeri, Inkit Padhi, and 2 more authors
In XAI in Action: Past, Present, and Future Applications, Aug 2023
@inproceedings{ghosh2023influence,title={Influence Based Approaches to Algorithmic Fairness: A Closer Look},author={Ghosh, Soumya and Sattigeri, Prasanna and Padhi, Inkit and Nagireddy, Manish and Chen, Jie},booktitle={XAI in Action: Past, Present, and Future Applications},year={2023},url={https://openreview.net/forum?id=3oysFpd6Pq},}
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, and 4 more authors
In Proceedings of the 40th International Conference on Machine Learning, 23–29 jul 2023
@inproceedings{pmlr-v202-melnyk23a,title={Reprogramming Pretrained Language Models for Antibody Sequence Infilling},author={Melnyk, Igor and Chenthamarakshan, Vijil and Chen, Pin-Yu and Das, Payel and Dhurandhar, Amit and Padhi, Inkit and Das, Devleena},booktitle={Proceedings of the 40th International Conference on Machine Learning},pages={24398--24419},year={2023},editor={Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},volume={202},series={Proceedings of Machine Learning Research},month={23--29 Jul},publisher={PMLR},url={https://proceedings.mlr.press/v202/melnyk23a.html},}
The incentive gap in data work in the era of large models
Katy Ilonka Gero, Payel Das, Pierre Dognin, and 3 more authors
@article{Gero2023,title={The incentive gap in data work in the era of large models},volume={5},issn={2522-5839},url={http://dx.doi.org/10.1038/s42256-023-00673-x},doi={10.1038/s42256-023-00673-x},number={6},journal={Nature Machine Intelligence},publisher={Springer Science and Business Media LLC},author={Gero, Katy Ilonka and Das, Payel and Dognin, Pierre and Padhi, Inkit and Sattigeri, Prasanna and Varshney, Kush R.},year={2023},month=jun,pages={565–567},}
Accelerating material design with the generative toolkit for scientific discovery
Matteo Manica, Jannis Born, Joris Cadow, and 21 more authors
@article{Manica2023,title={Accelerating material design with the generative toolkit for scientific discovery},volume={9},issn={2057-3960},url={http://dx.doi.org/10.1038/s41524-023-01028-1},doi={10.1038/s41524-023-01028-1},number={1},journal={npj Computational Materials},publisher={Springer Science and Business Media LLC},author={Manica, Matteo and Born, Jannis and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Clarke, Dean and Teukam, Yves Gaetan Nana and Giannone, Giorgio and Hoffman, Samuel C. and Buchan, Matthew and Chenthamarakshan, Vijil and Donovan, Timothy and Hsu, Hsiang Han and Zipoli, Federico and Schilter, Oliver and Kishimoto, Akihiro and Hamada, Lisa and Padhi, Inkit and Wehden, Karl and McHugh, Lauren and Khrabrov, Alexy and Das, Payel and Takeda, Seiji and Smith, John R.},year={2023},month=may,}
Explainable Cross-Topic Stance Detection for Search Results
Tim Draws, Karthikeyan Natesan Ramamurthy, Ioana Baldini, and 4 more authors
In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, Austin, TX, USA, May 2023
@inproceedings{10.1145/3576840.3578296,author={Draws, Tim and Natesan Ramamurthy, Karthikeyan and Baldini, Ioana and Dhurandhar, Amit and Padhi, Inkit and Timmermans, Benjamin and Tintarev, Nava},title={Explainable Cross-Topic Stance Detection for Search Results},year={2023},isbn={9798400700354},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3576840.3578296},doi={10.1145/3576840.3578296},booktitle={Proceedings of the 2023 Conference on Human Information Interaction and Retrieval},pages={221–235},numpages={15},keywords={bias, explainability, stance detection, viewpoint, web search},location={Austin, TX, USA},series={CHIIR '23},}
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
Brian Belgodere, Vijil Chenthamarakshan, Payel Das, and 9 more authors
@inbook{Belgodere2023,title={Cloud-Based Real-Time Molecular Screening Platform with MolFormer},isbn={9783031264221},issn={1611-3349},url={http://dx.doi.org/10.1007/978-3-031-26422-1_47},doi={10.1007/978-3-031-26422-1_47},booktitle={Machine Learning and Knowledge Discovery in Databases},publisher={Springer Nature Switzerland},author={Belgodere, Brian and Chenthamarakshan, Vijil and Das, Payel and Dognin, Pierre and Kurien, Toby and Melnyk, Igor and Mroueh, Youssef and Padhi, Inkit and Rigotti, Mattia and Ross, Jarret and Schiff, Yair and Young, Richard A.},year={2023},pages={641–644},}
2022
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, and 2 more authors
In Advances in Neural Information Processing Systems, May 2022
@inproceedings{NEURIPS2022_e94481b9,author={Sattigeri, Prasanna and Ghosh, Soumya and Padhi, Inkit and Dognin, Pierre and Varshney, Kush R},booktitle={Advances in Neural Information Processing Systems},editor={Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.},pages={35894--35906},publisher={Curran Associates, Inc.},title={Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting},url={https://proceedings.neurips.cc/paper_files/paper/2022/file/e94481b99473c83b2e79d91c64eb37d1-Paper-Conference.pdf},volume={35},year={2022},}
Large-scale chemical language representations capture molecular structure and properties
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, and 3 more authors
@article{Ross2022,title={Large-scale chemical language representations capture molecular structure and properties},volume={4},issn={2522-5839},url={http://dx.doi.org/10.1038/s42256-022-00580-7},doi={10.1038/s42256-022-00580-7},number={12},journal={Nature Machine Intelligence},publisher={Springer Science and Business Media LLC},author={Ross, Jerret and Belgodere, Brian and Chenthamarakshan, Vijil and Padhi, Inkit and Mroueh, Youssef and Das, Payel},year={2022},month=dec,pages={1256–1264},}
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Pierre Dognin, Igor Melnyk, Youssef Mroueh, and 6 more authors
Journal of Artificial Intelligence Research, Jan 2022
@article{Dognin2022,title={Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge},volume={73},issn={1076-9757},url={http://dx.doi.org/10.1613/jair.1.13113},doi={10.1613/jair.1.13113},journal={Journal of Artificial Intelligence Research},publisher={AI Access Foundation},author={Dognin, Pierre and Melnyk, Igor and Mroueh, Youssef and Padhi, Inkit and Rigotti, Mattia and Ross, Jarret and Schiff, Yair and Young, Richard A. and Belgodere, Brian},year={2022},month=jan,pages={437–459},}
2021
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Pierre Dognin, Inkit Padhi, Igor Melnyk, and 1 more author
In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov 2021
@inproceedings{dognin-etal-2021-regen,title={{R}e{G}en: {R}einforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models},author={Dognin, Pierre and Padhi, Inkit and Melnyk, Igor and Das, Payel},editor={Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},month=nov,year={2021},address={Online and Punta Cana, Dominican Republic},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2021.emnlp-main.83},doi={10.18653/v1/2021.emnlp-main.83},pages={1084--1099},}
Tabular Transformers for Modeling Multivariate Time Series
Inkit Padhi, Yair Schiff, Igor Melnyk, and 6 more authors
In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021
@article{Das2021,title={Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations},volume={5},issn={2157-846X},url={http://dx.doi.org/10.1038/s41551-021-00689-x},doi={10.1038/s41551-021-00689-x},number={6},journal={Nature Biomedical Engineering},publisher={Springer Science and Business Media LLC},author={Das, Payel and Sercu, Tom and Wadhawan, Kahini and Padhi, Inkit and Gehrmann, Sebastian and Cipcigan, Flaviu and Chenthamarakshan, Vijil and Strobelt, Hendrik and dos Santos, Cicero and Chen, Pin-Yu and Yang, Yi Yan and Tan, Jeremy P. K. and Hedrick, James and Crain, Jason and Mojsilovic, Aleksandra},year={2021},month=mar,pages={613–623},}
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Nishtha Madaan, Inkit Padhi, Naveen Panwar, and 1 more author
Proceedings of the AAAI Conference on Artificial Intelligence, May 2021
@article{Madaan2021,title={Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text},volume={35},issn={2159-5399},url={http://dx.doi.org/10.1609/aaai.v35i15.17594},doi={10.1609/aaai.v35i15.17594},number={15},journal={Proceedings of the AAAI Conference on Artificial Intelligence},publisher={Association for the Advancement of Artificial Intelligence (AAAI)},author={Madaan, Nishtha and Padhi, Inkit and Panwar, Naveen and Saha, Diptikalyan},year={2021},month=may,pages={13516–13524},}
2020
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Pierre Dognin, Igor Melnyk, Youssef Mroueh, and 4 more authors
@inproceedings{dognin-etal-2020-dualtkb,title={{D}ual{TKB}: {A} {D}ual {L}earning {B}ridge between {T}ext and {K}nowledge {B}ase},author={Dognin, Pierre and Melnyk, Igor and Padhi, Inkit and Nogueira dos Santos, Cicero and Das, Payel},editor={Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang},booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},month=nov,year={2020},address={Online},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2020.emnlp-main.694},doi={10.18653/v1/2020.emnlp-main.694},pages={8605--8616},}
Learning Implicit Text Generation via Feature Matching
Inkit Padhi, Pierre Dognin, Ke Bai, and 4 more authors
In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020
@inproceedings{padhi-etal-2020-learning,title={Learning Implicit Text Generation via Feature Matching},author={Padhi, Inkit and Dognin, Pierre and Bai, Ke and Nogueira dos Santos, C{\'\i}cero and Chenthamarakshan, Vijil and Mroueh, Youssef and Das, Payel},editor={Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel},booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},month=jul,year={2020},address={Online},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2020.acl-main.354},doi={10.18653/v1/2020.acl-main.354},pages={3855--3863},required={true},}
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, and 8 more authors
In Advances in Neural Information Processing Systems, Jul 2020
@inproceedings{NEURIPS2020_2d16ad19,author={Chenthamarakshan, Vijil and Das, Payel and Hoffman, Samuel and Strobelt, Hendrik and Padhi, Inkit and Lim, Kar Wai and Hoover, Benjamin and Manica, Matteo and Born, Jannis and Laino, Teodoro and Mojsilovic, Aleksandra},booktitle={Advances in Neural Information Processing Systems},editor={Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M.F. and Lin, H.},pages={4320--4332},publisher={Curran Associates, Inc.},title={CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models},url={https://proceedings.neurips.cc/paper_files/paper/2020/file/2d16ad1968844a4300e9a490588ff9f8-Paper.pdf},volume={33},year={2020},}
2019
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection
Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, and 5 more authors
@inproceedings{Santos_2019_ICCV,author={Santos, Cicero Nogueira dos and Mroueh, Youssef and Padhi, Inkit and Dognin, Pierre},title={Learning Implicit Generative Models by Matching Perceptual Features},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month=oct,year={2019},}
Sobolev Independence Criterion
Youssef Mroueh, Tom Sercu, Mattia Rigotti, and 2 more authors
In Advances in Neural Information Processing Systems, Oct 2019
@inproceedings{NEURIPS2019_79a3308b,author={Mroueh, Youssef and Sercu, Tom and Rigotti, Mattia and Padhi, Inkit and Nogueira dos Santos, Cicero},booktitle={Advances in Neural Information Processing Systems},editor={Wallach, H. and Larochelle, H. and Beygelzimer, A. and d\textquotesingle Alch\'{e}-Buc, F. and Fox, E. and Garnett, R.},pages={},publisher={Curran Associates, Inc.},title={Sobolev Independence Criterion},url={https://proceedings.neurips.cc/paper_files/paper/2019/file/79a3308b13cd31f096d8a4a34f96b66b-Paper.pdf},volume={32},year={2019},}
Generative Feature Matching Networks
Cicero Nogueira Santos, Inkit Padhi, Pierre Dognin, and 1 more author
@misc{santos2019generative,title={Generative Feature Matching Networks},author={dos Santos, Cicero Nogueira and Padhi, Inkit and Dognin, Pierre and Mroueh, Youssef},year={2019},url={https://openreview.net/forum?id=Syfz6sC9tQ},}
2018
Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry
Yuanshuo Zhao, Ioana Baldini, Prasanna Sattigeri, and 3 more authors
In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, New Orleans, LA, USA, Oct 2018
@inproceedings{10.1145/3278721.3278755,author={Zhao, Yuanshuo and Baldini, Ioana and Sattigeri, Prasanna and Padhi, Inkit and Lee, Yoong Keok and Smith, Ethan},title={Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry},year={2018},isbn={9781450360128},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3278721.3278755},doi={10.1145/3278721.3278755},booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},pages={347–353},numpages={7},keywords={biomimicry, machine learning},location={New Orleans, LA, USA},series={AIES '18},}
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Payel Das, Kahini Wadhawan, Oscar Chang, and 6 more authors
@misc{das2018pepcvaesemisupervisedtargeteddesign,title={PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences},author={Das, Payel and Wadhawan, Kahini and Chang, Oscar and Sercu, Tom and Santos, Cicero Dos and Riemer, Matthew and Chenthamarakshan, Vijil and Padhi, Inkit and Mojsilovic, Aleksandra},year={2018},eprint={1810.07743},archiveprefix={arXiv},primaryclass={q-bio.QM},url={https://arxiv.org/abs/1810.07743},}
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
Cicero Santos, Igor Melnyk, and Inkit Padhi
In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jul 2018
@inproceedings{nogueira-dos-santos-etal-2018-fighting,title={Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer},author={Nogueira dos Santos, Cicero and Melnyk, Igor and Padhi, Inkit},editor={Gurevych, Iryna and Miyao, Yusuke},booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},month=jul,year={2018},address={Melbourne, Australia},publisher={Association for Computational Linguistics},url={https://aclanthology.org/P18-2031},doi={10.18653/v1/P18-2031},pages={189--194},}
2017
Improved Neural Text Attribute Transfer with Non-parallel Data
Igor Melnyk, Cicero Nogueira Santos, Kahini Wadhawan, and 2 more authors
@misc{melnyk2017improvedneuraltextattribute,title={Improved Neural Text Attribute Transfer with Non-parallel Data},author={Melnyk, Igor and dos Santos, Cicero Nogueira and Wadhawan, Kahini and Padhi, Inkit and Kumar, Abhishek},year={2017},eprint={1711.09395},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/1711.09395},}
2016
Does String-Based Neural MT Learn Source Syntax?
Xing Shi, Inkit Padhi, and Kevin Knight
In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Nov 2016
@inproceedings{shi-etal-2016-string,title={Does String-Based Neural {MT} Learn Source Syntax?},author={Shi, Xing and Padhi, Inkit and Knight, Kevin},editor={Su, Jian and Duh, Kevin and Carreras, Xavier},booktitle={Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},month=nov,year={2016},address={Austin, Texas},publisher={Association for Computational Linguistics},url={https://aclanthology.org/D16-1159},doi={10.18653/v1/D16-1159},pages={1526--1534},}