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ORCIDMegha Khosla
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2020 – today
- 2024
[j13]Simone Piaggesi, Megha Khosla, André Panisson, Avishek Anand:
DINE: Dimensional Interpretability of Node Embeddings. IEEE Trans. Knowl. Data Eng. 36(12): 7986-7997 (2024)- [j12]Tianqi Zhao, Alan Hanjalic, Megha Khosla:
AGALE: A Graph-Aware Continual Learning Evaluation Framework. Trans. Mach. Learn. Res. 2024 (2024) - [j11]Jurek Leonhardt
, Henrik Müller, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand:
Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders. ACM Trans. Inf. Syst. 42(5): 117:1-117:34 (2024) - [c15]Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai:
Model Selection with Model Zoo via Graph Learning. ICDE 2024: 1296-1309 - [i32]Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai:
Model Selection with Model Zoo via Graph Learning. CoRR abs/2404.03988 (2024) - [i31]Tianqi Zhao, Alan Hanjalic, Megha Khosla:
AGALE: A Graph-Aware Continual Learning Evaluation Framework. CoRR abs/2406.01229 (2024) - [i30]Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla:
A data-centric approach for assessing progress of Graph Neural Networks. CoRR abs/2406.12439 (2024) - [i29]Simone Piaggesi, André Panisson, Megha Khosla:
Disentangled and Self-Explainable Node Representation Learning. CoRR abs/2410.21043 (2024) - 2023
- [j10]Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke, Megha Khosla:
Private Graph Extraction via Feature Explanations. Proc. Priv. Enhancing Technol. 2023(2): 59-78 (2023) - [j9]Thorben Funke, Megha Khosla, Mandeep Rathee, Avishek Anand:
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks. IEEE Trans. Knowl. Data Eng. 35(8): 8687-8698 (2023) - [j8]Iyiola E. Olatunji, Thorben Funke, Megha Khosla:
Releasing Graph Neural Networks with Differential Privacy Guarantees. Trans. Mach. Learn. Res. 2023 (2023) - [j7]Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla:
Multi-label Node Classification On Graph-Structured Data. Trans. Mach. Learn. Res. 2023 (2023) - [i28]Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla:
Multi-label Node Classification On Graph-Structured Data. CoRR abs/2304.10398 (2023) - [i27]Iyiola E. Olatunji, Anmar Hizber, Oliver Sihlovec, Megha Khosla:
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk? CoRR abs/2306.00578 (2023) - [i26]Simone Piaggesi, Megha Khosla, André Panisson, Avishek Anand:
DINE: Dimensional Interpretability of Node Embeddings. CoRR abs/2310.01162 (2023) - [i25]Jurek Leonhardt, Henrik Müller, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand:
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders. CoRR abs/2311.01263 (2023) - 2022
- [j6]Ngan Dong, Stefanie Mücke, Megha Khosla:
MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 19(6): 3081-3092 (2022) - [c14]Jurek Leonhardt, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand:
Efficient Neural Ranking using Forward Indexes. WWW 2022: 266-276 - [i24]Mandeep Rathee, Thorben Funke, Avishek Anand, Megha Khosla:
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations. CoRR abs/2206.13983 (2022) - [i23]Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke, Megha Khosla:
Private Graph Extraction via Feature Explanations. CoRR abs/2206.14724 (2022) - [i22]Megha Khosla:
Privacy and Transparency in Graph Machine Learning: A Unified Perspective. CoRR abs/2207.10896 (2022) - 2021
- [j5]Thi Ngan Dong, Graham Brogden, Gisa Gerold, Megha Khosla:
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions. BMC Bioinform. 22(1): 572 (2021) - [j4]Megha Khosla, Vinay Setty, Avishek Anand:
A Comparative Study for Unsupervised Network Representation Learning. IEEE Trans. Knowl. Data Eng. 33(5): 1807-1818 (2021) - [c13]Soumyadeep Roy, Sudip Chakraborty, Aishik Mandal, Gunjan Balde, Prakhar Sharma, Anandhavelu Natarajan, Megha Khosla, Shamik Sural, Niloy Ganguly:
Knowledge-Aware Neural Networks for Medical Forum Question Classification. CIKM 2021: 3398-3402 - [c12]Jaspreet Singh, Megha Khosla, Zhenye Wang, Avishek Anand:
Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models. ICTIR 2021: 203-210 - [c11]Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla:
Membership Inference Attack on Graph Neural Networks. TPS-ISA 2021: 11-20 - [i21]Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla:
Membership Inference Attack on Graph Neural Networks. CoRR abs/2101.06570 (2021) - [i20]Megha Khosla, Avishek Anand:
Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings. CoRR abs/2101.07155 (2021) - [i19]Iyiola E. Olatunji, Jens Rauch, Matthias Katzensteiner, Megha Khosla:
A Review of Anonymization for Healthcare Data. CoRR abs/2104.06523 (2021) - [i18]Jens Rauch, Iyiola E. Olatunji, Megha Khosla:
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning. CoRR abs/2104.07938 (2021) - [i17]Thorben Funke, Megha Khosla, Avishek Anand:
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks. CoRR abs/2105.08621 (2021) - [i16]Mandeep Rathee, Zijian Zhang, Thorben Funke, Megha Khosla, Avishek Anand:
Learnt Sparsification for Interpretable Graph Neural Networks. CoRR abs/2106.12920 (2021) - [i15]Thi Ngan Dong, Megha Khosla:
MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction. CoRR abs/2108.04820 (2021) - [i14]Iyiola E. Olatunji, Thorben Funke, Megha Khosla:
Releasing Graph Neural Networks with Differential Privacy Guarantees. CoRR abs/2109.08907 (2021) - [i13]Soumyadeep Roy, Sudip Chakraborty, Aishik Mandal, Gunjan Balde, Prakhar Sharma, Anandhavelu Natarajan, Megha Khosla, Shamik Sural, Niloy Ganguly:
Knowledge-Aware Neural Networks for Medical Forum Question Classification. CoRR abs/2109.13141 (2021) - [i12]Jurek Leonhardt, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand:
Fast Forward Indexes for Efficient Document Ranking. CoRR abs/2110.06051 (2021) - [i11]Thi Ngan Dong, Graham Brogden, Gisa Gerold, Megha Khosla:
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions. CoRR abs/2111.13346 (2021) - 2020
- [c10]Ngan Thi Dong, Megha Khosla:
Revisiting Feature Selection with Data Complexity. BIBE 2020: 211-216 - [c9]Thi Ngan Dong, Megha Khosla:
Towards a consistent evaluation of miRNA-disease association prediction models. BIBM 2020: 1835-1842 - [c8]Jurek Leonhardt, Avishek Anand, Megha Khosla:
Boilerplate Removal using a Neural Sequence Labeling Model. WWW (Companion Volume) 2020: 226-229 - [i10]Vikram Waradpande, Daniel Kudenko, Megha Khosla:
Deep Reinforcement Learning with Graph-based State Representations. CoRR abs/2004.13965 (2020) - [i9]Jaspreet Singh, Megha Khosla, Avishek Anand:
Valid Explanations for Learning to Rank Models. CoRR abs/2004.13972 (2020) - [i8]Jurek Leonhardt, Avishek Anand, Megha Khosla:
Boilerplate Removal using a Neural Sequence Labeling Model. CoRR abs/2004.14294 (2020)
2010 – 2019
- 2019
- [j3]Megha Khosla, Avishek Anand:
A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs. Algorithmica 81(9): 3707-3724 (2019) - [j2]Helge Holzmann, Avishek Anand, Megha Khosla:
Estimating PageRank deviations in crawled graphs. Appl. Netw. Sci. 4(1): 86:1-86:22 (2019) - [c7]Maximilian Idahl, Megha Khosla, Avishek Anand:
Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases. PKDD/ECML Workshops (1) 2019: 229-240 - [c6]Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl, Avishek Anand:
Node Representation Learning for Directed Graphs. ECML/PKDD (1) 2019: 395-411 - [c5]Avishek Anand, Megha Khosla, Jaspreet Singh, Jan-Hendrik Zab, Zijian Zhang:
Asynchronous Training of Word Embeddings for Large Text Corpora. WSDM 2019: 168-176 - [i7]Megha Khosla, Avishek Anand, Vinay Setty:
A Comprehensive Comparison of Unsupervised Network Representation Learning Methods. CoRR abs/1903.07902 (2019) - [i6]Maximilian Idahl, Megha Khosla, Avishek Anand:
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases. CoRR abs/1910.05030 (2019) - 2018
- [c4]Helge Holzmann, Avishek Anand, Megha Khosla:
Delusive PageRank in Incomplete Graphs. COMPLEX NETWORKS (1) 2018: 104-117 - [c3]Jurek Leonhardt, Avishek Anand, Megha Khosla:
User Fairness in Recommender Systems. WWW (Companion Volume) 2018: 101-102 - [i5]Jurek Leonhardt, Avishek Anand, Megha Khosla:
User Fairness in Recommender Systems. CoRR abs/1807.06349 (2018) - [i4]Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl, Avishek Anand:
Node Representation Learning for Directed Graphs. CoRR abs/1810.09176 (2018) - [i3]Avishek Anand, Megha Khosla, Jaspreet Singh, Jan-Hendrik Zab, Zijian Zhang:
Asynchronous Training of Word Embeddings for Large Text Corpora. CoRR abs/1812.03825 (2018) - 2016
- [j1]Nikolaos Fountoulakis, Megha Khosla, Konstantinos Panagiotou:
The Multiple-Orientability Thresholds for Random Hypergraphs. Comb. Probab. Comput. 25(6): 870-908 (2016) - [i2]Megha Khosla, Avishek Anand:
A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs. CoRR abs/1611.07786 (2016) - 2014
- [b1]Megha Khosla:
Multiple choice allocations with small maximum loads. Saarland University, 2014 - 2013
- [c2]Megha Khosla:
Balls into Bins Made Faster. ESA 2013: 601-612 - [i1]Nikolaos Fountoulakis, Megha Khosla, Konstantinos Panagiotou:
The Multiple-orientability Thresholds for Random Hypergraphs. CoRR abs/1309.6772 (2013) - 2011
- [c1]Nikolaos Fountoulakis, Megha Khosla, Konstantinos Panagiotou:
The Multiple-Orientability Thresholds for Random Hypergraphs. SODA 2011: 1222-1236
Coauthor Index
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last updated on 2024-12-01 00:09 CET by the dblp team
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