Thomas Markovich

According to our database, Thomas Markovich authored at least 11 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Recent Link Classification on Temporal Graphs Using Graph Profiler.

Muberra Ozmen, Thomas Markovich

Trans. Mach. Learn. Res., 2024

QDC: Quantum Diffusion Convolution Kernels on Graphs.

Thomas Markovich

Trans. Mach. Learn. Res., 2024

Scaling Laws for Discriminative Classification in Large Language Models.

Dean Wyatte, Fatemeh Tahmasbi, Ming Li, Thomas Markovich

CoRR, 2024

2023

Causally-guided Regularization of Graph Attention Improves Generalizability.

Alexander P. Wu, Thomas Markovich, Bonnie Berger, Nils Yannick Hammerla, Rohit Singh

Trans. Mach. Learn. Res., 2023

Understanding convolution on graphs via energies.

Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

Trans. Mach. Learn. Res., 2023

bfkNN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval.

Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Graph Neural Networks for Link Prediction with Subgraph Sketching.

Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire

Proceedings of the Eleventh International Conference on Learning Representations, 2023

TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter.

Vanessa Cai, Pradeep Prabakar, Manuel Serrano Rebuelta, Lucas Rosen, Federico Monti, Katarzyna Janocha, Tomo Lazovich, Jeetu Raj, Yedendra Shrinivasan, Hao Li, Thomas Markovich

Proceedings of the Workshop on Data Mining for Online Advertising (AdKDD 2023) co-located with the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), 2023

2022

Graph Neural Networks as Gradient Flows.

Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

CoRR, 2022

kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval.

Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao

CoRR, 2022

TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation.

Ahmed El-Kishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022