Kailash Budhathoki

According to our database, Kailash Budhathoki authored at least 20 papers between 2015 and 2024.

Collaborative distances:

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

2024

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models.

Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing

J. Mach. Learn. Res., 2024

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models.

David Hoffmann, Kailash Budhathoki, Matthäus Kleindessner

CoRR, 2024

Inference Optimization of Foundation Models on AI Accelerators.

Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Meaningful Causal Aggregation and Paradoxical Confounding.

Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing

Proceedings of the Causal Learning and Reasoning, 2024

Quantifying intrinsic causal contributions via structure preserving interventions.

Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision.

Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

Explaining the root causes of unit-level changes.

Kailash Budhathoki, George Michailidis, Dominik Janzing

CoRR, 2022

Causal structure-based root cause analysis of outliers.

Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing

Proceedings of the International Conference on Machine Learning, 2022

2021

Discovering Reliable Causal Rules.

Kailash Budhathoki, Mario Boley, Jilles Vreeken

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Why did the distribution change?

Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Causal inference on discrete data.

Kailash Budhathoki

PhD thesis, 2020

2018

Origo: causal inference by compression.

Kailash Budhathoki, Jilles Vreeken

Knowl. Inf. Syst., 2018

Causal Inference on Event Sequences.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Ranking the Teams in European Football Leagues with Agony.

Stefan Neumann, Julian Ritter, Kailash Budhathoki

Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018), 2018

Accurate Causal Inference on Discrete Data.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the IEEE International Conference on Data Mining, 2018

2017

Causal Inference by Stochastic Complexity.

Kailash Budhathoki, Jilles Vreeken

CoRR, 2017

Correlation by Compression.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

MDL for Causal Inference on Discrete Data.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

2016

Causal Inference by Compression.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

2015

The Difference and the Norm - Characterising Similarities and Differences Between Databases.

Kailash Budhathoki, Jilles Vreeken

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015