Balls into Bins Made Faster. - Megha Khosla (ESA, 2013)

Multiple choice allocations with small maximum loads. - Megha Khosla (2014)

The Multiple-Orientability Thresholds for Random Hypergraphs. - Nikolaos Fountoulakis; Megha Khosla; Konstantinos Panagiotou (Comb. Probab. Comput., 2016)

Delusive PageRank in Incomplete Graphs. - Helge Holzmann; Avishek Anand; Megha Khosla (COMPLEX NETWORKS (1), 2018)

User Fairness in Recommender Systems. - Jurek Leonhardt; Avishek Anand; Megha Khosla (WWW (Companion Volume), 2018)

Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases. - Maximilian Idahl; Megha Khosla; Avishek Anand (PKDD/ECML Workshops (1), 2019)

Node Representation Learning for Directed Graphs. - Megha Khosla; Jurek Leonhardt; Wolfgang Nejdl; Avishek Anand (ECML/PKDD (1), 2019)

Asynchronous Training of Word Embeddings for Large Text Corpora. - Avishek Anand; Megha Khosla; Jaspreet Singh; Jan-Hendrik Zab; Zijian Zhang 006 (WSDM, 2019)

A Faster Algorithm for Cuckoo Insertion and Bipartite Matching in Large Graphs. - Megha Khosla; Avishek Anand (Algorithmica, 2019)

Estimating PageRank deviations in crawled graphs. - Helge Holzmann; Avishek Anand; Megha Khosla (Appl. Netw. Sci., 2019)

A Comprehensive Comparison of Unsupervised Network Representation Learning Methods. - Megha Khosla; Avishek Anand; Vinay Setty (CoRR, 2019)

Revisiting Feature Selection with Data Complexity. - Ngan Thi Dong; Megha Khosla (BIBE, 2020)

Towards a consistent evaluation of miRNA-disease association prediction models. - Thi Ngan Dong; Megha Khosla (BIBM, 2020)

Boilerplate Removal using a Neural Sequence Labeling Model. - Jurek Leonhardt; Avishek Anand; Megha Khosla (WWW (Companion Volume), 2020)

Deep Reinforcement Learning with Graph-based State Representations. - Vikram Waradpande; Daniel Kudenko; Megha Khosla (CoRR, 2020)

Valid Explanations for Learning to Rank Models. - Jaspreet Singh; Megha Khosla; Avishek Anand (CoRR, 2020)

Knowledge-Aware Neural Networks for Medical Forum Question Classification. - Soumyadeep Roy; Sudip Chakraborty; Aishik Mandal; Gunjan Balde; Prakhar Sharma; Anandhavelu Natarajan; Megha Khosla; Shamik Sural; Niloy Ganguly (CIKM, 2021)

Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models. - Jaspreet Singh; Megha Khosla; Zhenye Wang; Avishek Anand (ICTIR, 2021)

Membership Inference Attack on Graph Neural Networks. - Iyiola E. Olatunji; Wolfgang Nejdl; Megha Khosla (TPS-ISA, 2021)

A multitask transfer learning framework for the prediction of virus-human protein-protein interactions. - Thi Ngan Dong; Graham Brogden; Gisa Gerold; Megha Khosla (BMC Bioinform., 2021)

Revisiting the Auction Algorithm for Weighted Bipartite Perfect Matchings. - Megha Khosla; Avishek Anand (CoRR, 2021)

Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning. - Jens Rauch; Iyiola E. Olatunji; Megha Khosla (CoRR, 2021)

Learnt Sparsification for Interpretable Graph Neural Networks. - Mandeep Rathee; Zijian Zhang 006; Thorben Funke; Megha Khosla; Avishek Anand (CoRR, 2021)

MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction. - Thi Ngan Dong; Megha Khosla (CoRR, 2021)

Fast Forward Indexes for Efficient Document Ranking. - Jurek Leonhardt; Koustav Rudra; Megha Khosla; Abhijit Anand; Avishek Anand (CoRR, 2021)

A Comparative Study for Unsupervised Network Representation Learning. - Megha Khosla; Vinay Setty; Avishek Anand (IEEE Trans. Knowl. Data Eng., 2021)

Efficient Neural Ranking using Forward Indexes. - Jurek Leonhardt; Koustav Rudra; Megha Khosla; Abhijit Anand; Avishek Anand (WWW, 2022)

BAGEL: A Benchmark for Assessing Graph Neural Network Explanations. - Mandeep Rathee; Thorben Funke; Avishek Anand; Megha Khosla (CoRR, 2022)

Privacy and Transparency in Graph Machine Learning: A Unified Perspective. - Megha Khosla (CoRR, 2022)

MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction. - Ngan Dong; Stefanie Mücke; Megha Khosla (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk? - Iyiola E. Olatunji; Anmar Hizber; Oliver Sihlovec; Megha Khosla (CoRR, 2023)

Releasing Graph Neural Networks with Differential Privacy Guarantees. - Iyiola E. Olatunji; Thorben Funke; Megha Khosla (Trans. Mach. Learn. Res., 2023)

Multi-label Node Classification On Graph-Structured Data. - Tianqi Zhao; Ngan Thi Dong; Alan Hanjalic; Megha Khosla (Trans. Mach. Learn. Res., 2023)

Private Graph Extraction via Feature Explanations. - Iyiola E. Olatunji; Mandeep Rathee; Thorben Funke; Megha Khosla (Proc. Priv. Enhancing Technol., 2023)

Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks. - Thorben Funke; Megha Khosla; Mandeep Rathee; Avishek Anand (IEEE Trans. Knowl. Data Eng., 2023)

Model Selection with Model Zoo via Graph Learning. - Ziyu Li; Hilco van der Wilk; Danning Zhan; Megha Khosla; Alessandro Bozzon; Rihan Hai 001 (ICDE, 2024)

A Review of Anonymization for Healthcare Data. - Iyiola E. Olatunji; Jens Rauch; Matthias Katzensteiner; Megha Khosla (Big Data, 2024)

A data-centric approach for assessing progress of Graph Neural Networks. - Tianqi Zhao; Ngan Thi Dong; Alan Hanjalic; Megha Khosla (CoRR, 2024)

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification. - Tianqi Zhao; Megha Khosla (CoRR, 2024)

AGALE: A Graph-Aware Continual Learning Evaluation Framework. - Tianqi Zhao; Alan Hanjalic; Megha Khosla (Trans. Mach. Learn. Res., 2024)

Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders. - Jurek Leonhardt; Henrik Müller; Koustav Rudra; Megha Khosla; Abhijit Anand; Avishek Anand (ACM Trans. Inf. Syst., 2024)

DINE: Dimensional Interpretability of Node Embeddings. - Simone Piaggesi; Megha Khosla; André Panisson; Avishek Anand (IEEE Trans. Knowl. Data Eng., 2024)

Disentangled and Self-Explainable Node Representation Learning. - Simone Piaggesi; André Panisson; Megha Khosla (Trans. Mach. Learn. Res., 2025)

Graph learning-based generation of abstractions for reinforcement learning. - Yuan Xue; Daniel Kudenko; Megha Khosla (Neural Comput. Appl., 2025)