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2020 – today
- 2024
[c31]Xiang Gao, Kamalika Das:
Customizing Language Model Responses with Contrastive In-Context Learning. AAAI 2024: 18039-18046- [c30]Jiaxin Zhang, Kamalika Das, Kumar Sricharan:
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. AISTATS 2024: 2917-2925 - [c29]Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das:
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models. EACL (1) 2024: 2336-2346 - [c28]Wendi Cui, Zhuohang Li, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan, Jiaxin Zhang:
Divide-Conquer-Reasoning for Consistency Evaluation and Automatic Improvement of Large Language Models. EMNLP (Industry Track) 2024: 334-361 - [c27]Ankita Sinha, Wendi Cui, Kamalika Das, Jiaxin Zhang:
Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution. EMNLP (Industry Track) 2024: 1016-1027 - [c26]Zhuohang Li, Jiaxin Zhang, Chao Yan, Kamalika Das, Kumar Sricharan, Murat Kantarcioglu, Bradley A. Malin:
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation. EMNLP 2024: 6130-6151 - [c25]Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, Kumar Sricharan:
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models. EMNLP 2024: 21456-21473 - [c24]Magesh Rajasekaran, Md Saiful Islam Sajol, Frej Berglind, Supratik Mukhopadhyay, Kamalika Das:
COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification. SDM 2024: 643-651 - [c23]Jiaxin Zhang, Joy Rimchala, Lalla Mouatadid, Kamalika Das, Kumar Sricharan:
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models. WACV 2024: 8021-8030 - [i13]Wendi Cui, Jiaxin Zhang, Zhuohang Li, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan:
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models. CoRR abs/2401.02132 (2024) - [i12]Xiang Gao, Kamalika Das:
Customizing Language Model Responses with Contrastive In-Context Learning. CoRR abs/2401.17390 (2024) - [i11]Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan:
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models. CoRR abs/2402.11347 (2024) - [i10]Jiaxin Zhang, Kamalika Das, Kumar Sricharan:
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. CoRR abs/2402.12664 (2024) - [i9]Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das:
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models. CoRR abs/2403.02509 (2024) - [i8]Zhuohang Li, Jiaxin Zhang, Chao Yan, Kamalika Das, Kumar Sricharan, Murat Kantarcioglu, Bradley A. Malin:
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation. CoRR abs/2410.08320 (2024) - [i7]Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, Kumar Sricharan:
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models. CoRR abs/2410.09629 (2024) - [i6]Ankita Sinha, Wendi Cui, Kamalika Das, Jiaxin Zhang:
Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution. CoRR abs/2410.09652 (2024) - 2023
- [c22]Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A. Malin, Kumar Sricharan:
SAC³: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency. EMNLP (Findings) 2023: 15445-15458 - [c21]Jiaxin Zhang, Zhuohang Li, Kamalika Das, Kumar Sricharan:
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision. NeurIPS 2023 - [i5]Jiaxin Zhang, Zhuohang Li, Kamalika Das, Kumar Sricharan:
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision. CoRR abs/2310.20153 (2023) - [i4]Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A. Malin, Kumar Sricharan:
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency. CoRR abs/2311.01740 (2023) - [i3]Jiaxin Zhang, Joy Rimchala, Lalla Mouatadid, Kamalika Das, Kumar Sricharan:
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models. CoRR abs/2311.09625 (2023) - 2022
- [i2]Frej Berglind, Haron Temam, Supratik Mukhopadhyay, Kamalika Das, Md Saiful Islam Sajol, Kumar Sricharan, Kumar Kallurupalli:
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification. CoRR abs/2208.00629 (2022) - 2020
- [c20]Ata Akbari Asanjan, Kamalika Das, Alan S. Li, Ved Chirayath, Juan Torres-Perez, Soroosh Sorooshian:
Learning Instrument Invariant Characteristics for Generating High-resolution Global Coral Reef Maps. KDD 2020: 2617-2624
2010 – 2019
- 2018
- [c19]Anuradha Kodali, Marcin Szubert, Kamalika Das, Sangram Ganguly, Joshua Clifford Bongard:
Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis. PPSN (1) 2018: 525-536 - [i1]Aniruddha Basak, Kamalika Das, Ole J. Mengshoel:
CADDeLaG: Framework for distributed anomaly detection in large dense graph sequences. CoRR abs/1802.05421 (2018) - 2017
- [c18]Kamalika Das, Ilya Avrekh, Bryan L. Matthews, Manali Sharma, Nikunj C. Oza:
ASK-the-Expert: Active Learning Based Knowledge Discovery Using the Expert. ECML/PKDD (3) 2017: 395-399 - [e1]Yasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski
, Jesse Read, Marinka Zitnik, Michelangelo Ceci, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part III. Lecture Notes in Computer Science 10536, Springer 2017, ISBN 978-3-319-71272-7 [contents] - 2016
- [c17]Marco Stolpe, Kanishka Bhaduri, Kamalika Das:
Distributed Support Vector Machines: An Overview. Solving Large Scale Learning Tasks 2016: 109-138 - [c16]Marcin Szubert, Anuradha Kodali, Sangram Ganguly, Kamalika Das, Josh C. Bongard:
Reducing Antagonism between Behavioral Diversity and Fitness in Semantic Genetic Programming. GECCO 2016: 797-804 - [c15]Manali Sharma, Kamalika Das, Mustafa Bilgic, Bryan L. Matthews, David Nielsen, Nikunj C. Oza:
Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation. ECML/PKDD (3) 2016: 209-225 - [c14]Marcin Szubert, Anuradha Kodali, Sangram Ganguly, Kamalika Das, Josh C. Bongard:
Semantic Forward Propagation for Symbolic Regression. PPSN 2016: 364-374 - 2015
- [c13]Kamalika Das, Kanishka Bhaduri, Bryan L. Matthews, Nikunj C. Oza:
Large scale support vector regression for aviation safety. IEEE BigData 2015: 999-1006 - [c12]Chandrayee Basu, Christian Koehler, Kamalika Das, Anind K. Dey:
PerCCS: person-count from carbon dioxide using sparse non-negative matrix factorization. UbiComp 2015: 987-998 - 2014
- [j9]Bryan L. Matthews, Santanu Das, Kanishka Bhaduri, Kamalika Das, Rodney Martin, Nikunj C. Oza:
Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms. J. Aerosp. Inf. Syst. 11(7): 482 (2014) - [c11]Kumar Sricharan, Kamalika Das:
Localizing anomalous changes in time-evolving graphs. SIGMOD Conference 2014: 1347-1358 - 2013
- [j8]Bryan L. Matthews, Santanu Das, Kanishka Bhaduri, Kamalika Das, Rodney Martin, Nikunj C. Oza:
Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms. J. Aerosp. Inf. Syst. 10(10): 467-475 (2013) - [j7]Kamalika Das, Ashok N. Srivastava:
Sparse inverse kernel Gaussian Process regression. Stat. Anal. Data Min. 6(3): 205-220 (2013) - [c10]Marco Stolpe, Kanishka Bhaduri, Kamalika Das, Katharina Morik:
Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines. ECML/PKDD (3) 2013: 321-336 - 2011
- [j6]Kamalika Das, Kanishka Bhaduri, Hillol Kargupta:
Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks. Peer-to-Peer Netw. Appl. 4(2): 192-209 (2011) - [j5]Kanishka Bhaduri, Kamalika Das, Kirk D. Borne, Chris Giannella, Tushar Mahule, Hillol Kargupta:
Scalable, asynchronous, distributed eigen monitoring of astronomy data streams. Stat. Anal. Data Min. 4(3): 336-352 (2011) - [j4]Kamalika Das, Kanishka Bhaduri, Petr Votava:
Distributed anomaly detection using 1-class SVM for vertically partitioned data. Stat. Anal. Data Min. 4(4): 393-406 (2011) - [c9]Kamalika Das, Ashok N. Srivastava:
Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery. CIDU 2011: 233-247 - [c8]Kanishka Bhaduri, Kamalika Das, Bryan L. Matthews:
Detecting Abnormal Machine Characteristics in Cloud Infrastructures. ICDM Workshops 2011: 137-144 - [c7]Kanishka Bhaduri, Kamalika Das, Chris Giannella:
Distributed Monitoring of the R2 Statistic for Linear Regression. SDM 2011: 438-449 - 2010
- [j3]Kamalika Das, Kanishka Bhaduri, Hillol Kargupta:
A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks. Knowl. Inf. Syst. 24(3): 341-367 (2010) - [c6]Kanishka Bhaduri, Kamalika Das, Petr Votava:
Distributed Anomaly Detection using Satellite Data From Multiple Modalitie. CIDU 2010: 109-123 - [c5]Kamalika Das, Ashok N. Srivastava:
Block-GP: Scalable Gaussian Process Regression for Multimodal Data. ICDM 2010: 791-796
2000 – 2009
- 2009
- [c4]Kamalika Das, Kanishka Bhaduri, Hillol Kargupta:
A Local Distributed Peer-to-Peer Algorithm Using Multi-Party Optimization Based Privacy Preservation for Data Mining Primitive Computation. Peer-to-Peer Computing 2009: 212-221 - [c3]Kamalika Das, Kanishka Bhaduri, Sugandha Arora, Wesley Griffin, Kirk D. Borne, Chris Giannella, Hillol Kargupta:
Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms. SDM 2009: 247-258 - 2008
- [j2]Kamalika Das, Kanishka Bhaduri, Kun Liu, Hillol Kargupta:
Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network. IEEE Trans. Knowl. Data Eng. 20(4): 475-488 (2008) - [p2]Kun Liu, Kamalika Das, Tyrone Grandison, Hillol Kargupta:
Privacy-Preserving Data Analysis on Graphs and Social Networks. Next Generation of Data Mining 2008 - 2007
- [c2]Kanishka Bhaduri, Kamalika Das, Hillol Kargupta:
Peer-to-Peer Data Mining, Privacy Issues, and Games. AIS-ADM 2007: 1-10 - [c1]Hillol Kargupta, Kamalika Das, Kun Liu:
Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework. PKDD 2007: 523-531 - [p1]Kanishka Bhaduri, Kamalika Das, Krishnamoorthy Sivakumar, Hillol Kargupta, Ran Wolff, Rong Chen:
Algorithms for Distributed Data Stream Mining. Data Streams - Models and Algorithms 2007: 309-331 - 2006
- [j1]Kun Liu, Kanishka Bhaduri, Kamalika Das, Phuong Nguyen, Hillol Kargupta:
Client-side web mining for community formation in peer-to-peer environments. SIGKDD Explor. 8(2): 11-20 (2006)
Coauthor Index
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