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2020 – today
- 2024
[i19]Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger
, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici:
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR abs/2403.06009 (2024)- [i18]Anna Sokol, Nuno Moniz, Elizabeth Daly, Michael Hind, Nitesh V. Chawla:
BenchmarkCards: Large Language Model and Risk Reporting. CoRR abs/2410.12974 (2024) - 2022
- [c37]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 - [i17]David Piorkowski, John T. Richards, Michael Hind:
Evaluating a Methodology for Increasing AI Transparency: A Case Study. CoRR abs/2201.13224 (2022) - [i16]David Piorkowski, Michael Hind, John T. Richards:
Quantitative AI Risk Assessments: Opportunities and Challenges. CoRR abs/2209.06317 (2022) - 2021
- [j23]John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic, Kush R. Varshney:
A Human-Centered Methodology for Creating AI FactSheets. IEEE Data Eng. Bull. 44(4): 47-58 (2021) - [c36]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 - [c35]Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind:
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. PERSUASIVE 2021: 135-149 - [i15]Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind:
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users. CoRR abs/2101.12715 (2021) - [i14]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
- [j22]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) - [c34]Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney:
Experiences with Improving the Transparency of AI Models and Services. CHI Extended Abstracts 2020: 1-8 - [c33]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 - [i13]Michael Hind, Dennis Wei, Yunfeng Zhang:
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness. CoRR abs/2001.05573 (2020) - [i12]Stacy Hobson, Michael Hind, Aleksandra Mojsilovic, Kush R. Varshney:
Trust and Transparency in Contact Tracing Applications. CoRR abs/2006.11356 (2020) - [i11]John T. Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic:
A Methodology for Creating AI FactSheets. CoRR abs/2006.13796 (2020)
2010 – 2019
- 2019
- [j21]Michael Hind:
Explaining explainable AI. XRDS 25(3): 16-19 (2019) - [j20]Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang:
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4/5): 4:1-4:15 (2019) - [j19]Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, Darrell Reimer, John T. Richards, Jason Tsay, Kush R. Varshney:
FactSheets: Increasing trust in AI services through supplier's declarations of conformity. IBM J. Res. Dev. 63(4/5): 6:1-6:13 (2019) - [j18]Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang:
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36(4): 76-80 (2019) - [c32]Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
TED: Teaching AI to Explain its Decisions. AIES 2019: 123-129 - [c31]Ravi Kiran Raman, Kush R. Varshney, Roman Vaculín, Nelson Kibichii Bore, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Michael Hind:
Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation. ICASSP 2019: 7500-7504 - [c30]Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney:
A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations. IEEE ICBC 2019: 277-284 - [c29]Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus, Sekou L. Remy, Oliver Bent, Michael Hind, Eleftheria Kyriaki Pissadaki, Biplav Srivastava, Roman Vaculín, Kush R. Varshney, Komminist Weldemariam:
Promoting Distributed Trust in Machine Learning and Computational Simulation. IEEE ICBC 2019: 311-319 - [i10]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR abs/1906.02299 (2019) - [i9]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) - [i8]Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John T. Richards, Kush R. Varshney:
Experiences with Improving the Transparency of AI Models and Services. CoRR abs/1911.08293 (2019) - 2018
- [c28]Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith:
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images. MLCN/DLF/iMIMIC@MICCAI 2018: 97-105 - [i7]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching Meaningful Explanations. CoRR abs/1805.11648 (2018) - [i6]Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogério Schmidt Feris, John R. Smith:
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images. CoRR abs/1805.12234 (2018) - [i5]Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, Kush R. Varshney:
Increasing Trust in AI Services through Supplier's Declarations of Conformity. CoRR abs/1808.07261 (2018) - [i4]Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy, Eleftheria Kyriaki Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney:
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach. CoRR abs/1809.08438 (2018) - [i3]Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang:
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR abs/1810.01943 (2018) - [i2]Nelson Kibichii Bore, Ravi Kiran Raman, Isaac M. Markus, Sekou L. Remy, Oliver Bent, Michael Hind, Eleftheria Kyriaki Pissadaki, Biplav Srivastava, Roman Vaculín, Kush R. Varshney, Komminist Weldemariam:
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network. CoRR abs/1810.11126 (2018) - [i1]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
TED: Teaching AI to Explain its Decisions. CoRR abs/1811.04896 (2018) - 2016
- [j17]Matthew Arnold, David Grove, Benjamin Herta, Michael Hind, Martin Hirzel, Arun Iyengar, Louis Mandel, Vijay A. Saraswat, Avraham Shinnar, Jérôme Siméon, Mikio Takeuchi, Olivier Tardieu, Wei Zhang:
META: Middleware for Events, Transactions, and Analytics. IBM J. Res. Dev. 60(2-3) (2016) - [j16]Stephen M. Blackburn, Amer Diwan, Matthias Hauswirth, Peter F. Sweeney, José Nelson Amaral, Tim Brecht, Lubomír Bulej, Cliff Click, Lieven Eeckhout, Sebastian Fischmeister, Daniel Frampton, Laurie J. Hendren, Michael Hind, Antony L. Hosking, Richard E. Jones, Tomas Kalibera, Nathan Keynes, Nathaniel Nystrom, Andreas Zeller:
The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations. ACM Trans. Program. Lang. Syst. 38(4): 15:1-15:20 (2016) - 2014
- [j15]Michael Hind:
SIGPLAN research highlights annual report. ACM SIGPLAN Notices 49(4S): 9 (2014) - 2013
- [j14]Michael Hind:
CACM research highlights annual report. ACM SIGPLAN Notices 48(4S): 10-11 (2013) - [e3]Michael Hind, David Grove:
Proceedings of the third ACM SIGPLAN X10 Workshop, X10 2013, Seattle, Washington, USA, June 20, 2013. ACM 2013, ISBN 978-1-4503-2157-0 [contents]
2000 – 2009
- 2009
- [e2]Michael Hind, Amer Diwan:
Proceedings of the 2009 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2009, Dublin, Ireland, June 15-21, 2009. ACM 2009, ISBN 978-1-60558-392-1 [contents] - 2008
- [j13]Michael Hind:
Addressing the disconnect between the good and the popular. ACM SIGPLAN Notices 43(11): 74-76 (2008) - 2007
- [j12]Martin Hirzel, Daniel von Dincklage, Amer Diwan, Michael Hind:
Fast online pointer analysis. ACM Trans. Program. Lang. Syst. 29(2): 11 (2007) - [c27]Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder:
A Loop Correlation Technique to Improve Performance Auditing. PACT 2007: 259-269 - [c26]Dries Buytaert, Andy Georges, Michael Hind, Matthew Arnold, Lieven Eeckhout, Koen De Bosschere:
Using hpm-sampling to drive dynamic compilation. OOPSLA 2007: 553-568 - [c25]Prasad A. Kulkarni, Matthew Arnold, Michael Hind:
Dynamic compilation: the benefits of early investing. VEE 2007: 94-104 - 2006
- [c24]Priya Nagpurkar, Chandra Krintz, Michael Hind, Peter F. Sweeney, V. T. Rajan:
Online Phase Detection Algorithms. CGO 2006: 111-123 - [c23]Jeremy Lau, Matthew Arnold, Michael Hind, Brad Calder:
Online performance auditing: using hot optimizations without getting burned. PLDI 2006: 239-251 - 2005
- [j11]Bowen Alpern, Steve Augart, Stephen M. Blackburn, Maria A. Butrico, Anthony Cocchi, Perry Cheng, Julian Dolby, Stephen J. Fink, David Grove, Michael Hind, Kathryn S. McKinley, Mark F. Mergen, J. Eliot B. Moss, Ton Anh Ngo, Vivek Sarkar, Martin Trapp:
The Jikes Research Virtual Machine project: Building an open-source research community. IBM Syst. J. 44(2): 399-418 (2005) - [j10]Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney:
A Survey of Adaptive Optimization in Virtual Machines. Proc. IEEE 93(2): 449-466 (2005) - [j9]Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind:
The Need for a Whole-System View of Performance. Stud. Inform. Univ. 4(1): 99-110 (2005) - [c22]Michael Hind:
Virtual Machine Learning: Thinking like a Computer Architect. CGO 2005: 11 - [c21]David F. Bacon, Perry Cheng, David Grove, Michael Hind, V. T. Rajan, Eran Yahav, Matthias Hauswirth, Christoph M. Kirsch, Daniel Spoonhower, Martin T. Vechev:
High-level real-time programming in Java. EMSOFT 2005: 68-78 - [e1]Michael Hind, Jan Vitek:
Proceedings of the 1st International Conference on Virtual Execution Environments, VEE 2005, Chicago, IL, USA, June 11-12, 2005. ACM 2005, ISBN 1-59593-047-7 [contents] - 2004
- [c20]Martin Hirzel, Amer Diwan, Michael Hind:
Pointer Analysis in the Presence of Dynamic Class Loading. ECOOP 2004: 96-122 - [c19]Peter F. Sweeney, Matthias Hauswirth, Brendon Cahoon, Perry Cheng, Amer Diwan, David Grove, Michael Hind:
Using Hardware Performance Monitors to Understand the Behavior of Java Applications. Virtual Machine Research and Technology Symposium 2004: 57-72 - [c18]Matthias Hauswirth, Peter F. Sweeney, Amer Diwan, Michael Hind:
Vertical profiling: understanding the behavior of object-priented applications. OOPSLA 2004: 251-269 - 2002
- [c17]Martin Hirzel, Johannes Henkel, Amer Diwan, Michael Hind:
Understanding the connectivity of heap objects. MSP/ISMM 2002: 143-156 - [c16]Matthew Arnold, Michael Hind, Barbara G. Ryder:
Online feedback-directed optimization of Java. OOPSLA 2002: 111-129 - 2001
- [j8]Michael Hind, Anthony Pioli:
Evaluating the effectiveness of pointer alias analyses. Sci. Comput. Program. 39(1): 31-55 (2001) - [c15]Michael Hind:
Pointer analysis: haven't we solved this problem yet? PASTE 2001: 54-61 - 2000
- [j7]Bowen Alpern, C. Richard Attanasio, John J. Barton, Michael G. Burke, Perry Cheng, Jong-Deok Choi, Anthony Cocchi, Stephen J. Fink, David Grove, Michael Hind, Susan Flynn Hummel, Derek Lieber, Vassily Litvinov, Mark F. Mergen, Ton Ngo, James R. Russell, Vivek Sarkar, Mauricio J. Serrano, Janice C. Shepherd, Stephen E. Smith, Vugranam C. Sreedhar, Harini Srinivasan, John Whaley:
The Jalapeño virtual machine. IBM Syst. J. 39(1): 211-238 (2000) - [j6]Michael Hind:
NPIC - New Paltz interprocedural compiler. ACM SIGSOFT Softw. Eng. Notes 25(1): 57-58 (2000) - [c14]Manish Gupta, Jong-Deok Choi, Michael Hind:
Optimizing Java Programs in the Presence of Exceptions. ECOOP 2000: 422-446 - [c13]Michael Hind, Anthony Pioli:
Which pointer analysis should I use? ISSTA 2000: 113-123 - [c12]Matthew Arnold, Michael Hind, Barbara G. Ryder:
An Empirical Study of Selective Optimization. LCPC 2000: 49-67 - [c11]Matthew Arnold, Stephen J. Fink, David Grove, Michael Hind, Peter F. Sweeney:
Adaptive optimization in the Jalapeño JVM. OOPSLA 2000: 47-65 - [c10]Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F. Sweeney:
Adaptive optimization in the Jalapeño JVM (poster session). OOPSLA Addendum 2000: 125-126 - [c9]Michael Hind, Anthony Pioli:
Traveling Through Dakota: Experiences with an Object-Oriented Program Analysis System. TOOLS (34) 2000: 49-60
1990 – 1999
- 1999
- [j5]Michael Hind:
SIGAda '98: ACM/SIGAda International Conference (Report). ACM SIGPLAN Notices 34(12): 12 (1999) - [j4]Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar:
Efficient and precise modeling of exceptions for the analysis of Java programs. ACM SIGSOFT Softw. Eng. Notes 24(5): 21-31 (1999) - [j3]Michael Hind, Michael G. Burke, Paul R. Carini, Jong-Deok Choi:
Interprocedural pointer alias analysis. ACM Trans. Program. Lang. Syst. 21(4): 848-894 (1999) - [c8]Michael G. Burke, Jong-Deok Choi, Stephen J. Fink, David Grove, Michael Hind, Vivek Sarkar, Mauricio J. Serrano, Vugranam C. Sreedhar, Harini Srinivasan, John Whaley:
The Jalapeño Dynamic Optimizing Compiler for Java. Java Grande 1999: 129-141 - [c7]Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar:
Efficient and Precise Modeling of Exceptions for the Analysis of Java Programs. PASTE 1999: 21-31 - 1998
- [c6]Michael Hind, Anthony Pioli:
Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses. SAS 1998: 57-81 - 1996
- [j2]Michael Hind, Phil Pfeiffer:
Using Regional Conferences to Mentor Student Development: A Case Study. ACM SIGPLAN Notices 31(7): 4-7 (1996) - 1995
- [c5]Paul R. Carini, Michael Hind:
Flow-Sensitive Interprocedural Constant Propagation. PLDI 1995: 23-31 - 1994
- [j1]Michael Hind, Michael G. Burke, Paul R. Carini, Samuel P. Midkiff:
An Empirical Study of Precise Interprocedural Array Analysis. Sci. Program. 3(3): 255-271 (1994) - [c4]Michael G. Burke, Paul R. Carini, Jong-Deok Choi, Michael Hind:
Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers. LCPC 1994: 234-250 - 1992
- [c3]Bor-Ming Hsieh, Michael Hind, Ron Cryton:
Loop Distribution with Multiple Exits. SC 1992: 204-213 - 1991
- [b1]Michael Hind:
Efficienty Loop-Level Parallelism in ADA. New York University, USA, 1991 - [c2]Michael Hind, Edmond Schonberg:
Efficient loop-level parallelism in Ada. TRI-Ada 1991: 166-179
1980 – 1989
- 1989
- [c1]Ron Cytron, Michael Hind, Wilson C. Hsieh:
Automatic Generation of DAG Parallelism. PLDI 1989: 54-68
Coauthor Index
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