To view the publications from a specific year, select the year from the list
below. All are in pdf, unless noted.
Expedition: A System for the Unsupervised Learning of a Hierarchy of Concepts O. Madani. ArXiv, 2021.
Text Analysis via Binomial Tails O. Madani.
slides with notes,
and recording on YouTube)
DI 2021: Document Intelligence Workshop@KDD 2021.
Binomial Tails for Community Analysis
O. Madani, T. Ngo, W. Zeng, S. Averin, S. Evuru, V. Malhotra, S. Gandham, N. Yadav. ArXiv 2020 (to appear in workshop on
AI Cyber Security (AICS), AAAI 2022).
ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data.
V. Jeyakumar, O. Madani, A. Parandeh, A. Kulshreshtha, W. Zeng, N. Yadav. SIGMOD 2019.
Expanded verision, and
Presentation title slide
Data Driven Data Center Network Security (talk).
V. Jeyakumar, O. Madani, A. Parandeh, N. Yadav, at IWSPA (ACM
International Workshop on Security And Privacy Analytics). 2016.
An Empirical Comparison of Sparse vs.
Embedding Techniques on Many-Class Text Classification (slides).
A. Balsubramani and
O. Madani. NIPS workshop on Extreme Classification, 2013.
On Using Nearly-Independent Feature Families for High Precision and
Confidence. O. Madani, M. Georg, and D. Ross. Machine Learning
Journal (MLJ), Volume 92, Issues 2-3, pages 457-477, 2013.
unformatted version is available here. See next bullet for the
The dataset associated with the above paper, over 100k labeled
instances with over 10 feature (sub)families (of visual, auditory, text),
useful for research on multiview/multimodal
learning (co-training, late/early fusion, clustering, etc), is available at
UCI machine learning repository, and also
here, in multiple smaller partitions.
On Using Nearly-Independent Feature Families for High Precision and Confidence.
O. Madani, M. Georg, and D. Ross. ACML 2012.
We gave a long
presentation and the slides were also used for poster
presentation. The poster presentation was voted best!
Weakly Supervised Learning of Object
Segmentations from Web-Scale Video.
G. Hartmann, M. Grundmann, J. Hoffman, D. Tsai, V. Kwatra,
O. Madani, S. Vijayanarasimhan, I. Essa, J. Rehg, and R. Sukthankar.
Best paper award at Workshop on Web-scale Vision and Social Media, ECCV 2012.
Large-Scale Many-Class Prediction via Flat Techniques.
O. Madani and J. Huang.
PASCAL Challenge on Hierarchical Text Classification (short paper), Dec. 2009.
Learning When Concepts Abound. O. Madani, M. Connor, and W. Greiner.
Journal of Machine Learning Research (JMLR), 2009.
On the Empirical Complexity of Text Classification Problems. O. Madani, H. Raghavan, and R. Jones.
SRI AI Center Technical Report, 2009.
Efficient Online Learning and Prediction of Users' Desktop Actions. O. Madani, H. Bui, and E. Yeh.
Discounted Deterministic Markov Decision Processes
and Discounted All-Pairs Shortest Paths. O. Madani, M. Thorup, and U. Zwick.
ACM-SIAM SODA, 2009.
Massive Learning via a Prediction System. O. Madani. AAAI FSS07 (on
Computational Approaches to Representation Change During Learning and
Games in Infinitely Rich Worlds. O. Madani. Position paper at
AAAI FSS, 2007.
- Ranked Recall:
Efficient Classification by Efficient Learning of Indices that
Rank. O. Madani, M. Connor. Yahoo! Research
Technical Report, 2007.
- Recall Systems:
Efficient Learning and Use of Category Indices. O. Madani,
W. Greiner, D. Kempe, and M. Salavatipour. AISTATS, 2007.
- When will Feature
Feedback help? Quantifying the Complexity of Classification
Problems. H. Raghavan, O. Madani, R. Jones. IJCAI
Workshop on Human in the Loop Computing, 2007.
- Prediction Games in
Infinitely Rich Worlds. O. Madani. 2nd Utility Based Date Mining
Workshop (UBDM) at KDD, 2006.
- Learning when
Concepts Abound. O. Madani and W. Greiner. Yahoo! Research
Technical Report. May, 2006.
Learning with Feedback on Features and Instances. H. Raghavan, O.
Madani and R. Jones. JMLR, 2006.
- A Large
Scale Analysis of Query Logs for Assessing Personalization
Opportunities. S. Wedig and O. Madani. KDD 2006.
Query Substitutions. R. Jones, B. Rey, O. Madani, and W. Greiner.
Filterbots for Robust Cold-Start Recommendations. S.T. Park, D.
Pennock, O. Madani, N. Good, D. DeCoste. KDD 2006.
- An Empirical
Comparison of Algorithms for Aggregating Expert Predictions. V.
Dani, O. Madani, D. Pennock, S. Sanghai, and B. Galebach. UAI, 2006.
Prior to 2004
Model Selection. O. Madani, D. J. Lizotte, and R. Greiner. UAI,
Using Model Disagreement on Unlabeled Data to Validate Classification
Algorithms. O. Madani, D. Pennock, and G. Flake. Neural Information
Processing Systems (NIPS), 2004.
Budgeted Multi-Armed Bandit Problem. O. Madani, D. J. Lizotte,
and R. Greiner. COLT, 2004.
Learners! Manuscript (a summery of some extensions of classical
supervised learning, such as active learning), 2004.
Learning of Naive-Bayes Classifiers. D. J. Lizotte, O. Madani,
and R. Greiner. UAI, 2003.
the Undecidability of Probabilistic Planning and Related Stochastic
Optimization Problems. O. Madani, S. Hanks, and A. Condon. AI
Journal (AIJ), 2003.
Policy Iteration as a Newton's Method and Polynomial Policy Iteration
Algorithms . O. Madani. AAAI, 2002.
Value Iteration Algorithms for Deterministic MDPs. O. Madani,
- Performance of
Lookahead Control Policies in the Face of Abstractions and
Approximations. I. Levner, V. Bulitko, O. Madani and R.
Greiner. SARA, 2002.
Thesis: Complexity Results for Infinite-Horizon Markov Decision Processes
Information Gathering on the Internet with Time and Cost
Constraints. O. Etzioni, S. Hanks, T. Jiang, and O. Madani.
- On the
Undecidability of Probabilistic Planning and Partially Observable Markov
Decision Problems. O. Madani, S. Hanks, and A. Condon. AAAI,
- Fast and Intuitive
Clustering of Web Documents. O. Zamir, O. Etzioni, O. Madani,
R. M. Karp. KDD, 1997.
Information Gathering on the Internet. O. Etzioni, S. Hanks,
T. Jiang, R. M. Karp, O. Madani, and O. Waarts, IEEE FOCS, 1996.
Abbreviations of conference names,
- AAAI = National Conference on Artificial Intelligence (also, Association for Advancement of AI)
- ACM = Association for Computing Machinery
- ACML = Asian Machine Learning Conference
- AIJ = Artificial Intelligence Journal
- AISTATS = Conference on Artificial Intelligence and Statistics
- AAAI FSS = AAAI Fall Symposium Series
- CIKM = Conference on Information and Knowledge Management
- COLT = Conference on Learning Theory
- ECCV = European Conference on Computer Vision
- FOCS = IEEE International Conference on Foundations of Computer Science
- IEEE = Institute of Electrical and Electronics Engineers
- IJCAI = International Joint conference on Artificial Intelligence
- IWSPA = ACM International Workshop on Security And Privacy Analytics).
- JMLR = Journal of Machine Learning Research
- KDD or SIGKDD = ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining
- MLJ = Machine Learning Journal
- NIPS = Conference on Neural Information Processing Systems
- SARA = Symposium on Abstraction, Reformulation, and Approximation
- SDM = SIAM (Society of Industrial and Applied Mathematicians) Conference
on Data Mining
- SIAM = Society for Industrial and Applied Mathematics
- SICOMP = SIAM Journal on Computing
- SIGMOD = ACM Special Interest Group on Management of Data
- SODA = ACM-SIAM Simposium on Discrete Algorithms
- UAI = Conference on Uncertainty in Artificial Intelligence
- UBDM = Utility Based Data Mining (workshop)
- WWW = International World Wide Web Conference
Back to main page.