The Difference and the Norm - Characterising Similarities and Differences Between Databases. - Kailash Budhathoki; Jilles Vreeken (ECML/PKDD (2), 2015)

Causal Inference by Compression. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2016)

MDL for Causal Inference on Discrete Data. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2017)

Correlation by Compression. - Kailash Budhathoki; Jilles Vreeken (SDM, 2017)

Causal Inference by Stochastic Complexity. - Kailash Budhathoki; Jilles Vreeken (CoRR, 2017)

Accurate Causal Inference on Discrete Data. - Kailash Budhathoki; Jilles Vreeken (ICDM, 2018)

Ranking the Teams in European Football Leagues with Agony. - Stefan Neumann 003; Julian Ritter; Kailash Budhathoki (MLSA@PKDD/ECML, 2018)

Causal Inference on Event Sequences. - Kailash Budhathoki; Jilles Vreeken (SDM, 2018)

Origo: causal inference by compression. - Kailash Budhathoki; Jilles Vreeken (Knowl. Inf. Syst., 2018)

Causal inference on discrete data. - Kailash Budhathoki (2020)

Why did the distribution change? - Kailash Budhathoki; Dominik Janzing; Patrick Blöbaum; Hoiyi Ng (AISTATS, 2021)

Discovering Reliable Causal Rules. - Kailash Budhathoki; Mario Boley; Jilles Vreeken (SDM, 2021)

Causal structure-based root cause analysis of outliers. - Kailash Budhathoki; Lenon Minorics; Patrick Blöbaum; Dominik Janzing (ICML, 2022)

Explaining the root causes of unit-level changes. - Kailash Budhathoki; George Michailidis; Dominik Janzing (CoRR, 2022)

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. - Junaid Ali 001; Matthäus Kleindessner; Florian Wenzel; Kailash Budhathoki; Volkan Cevher; Chris Russell 001 (AIES, 2023)

Quantifying intrinsic causal contributions via structure preserving interventions. - Dominik Janzing; Patrick Blöbaum; Atalanti-Anastasia Mastakouri; Philipp Michael Faller; Lenon Minorics; Kailash Budhathoki (AISTATS, 2024)

Meaningful Causal Aggregation and Paradoxical Confounding. - Yuchen Zhu; Kailash Budhathoki; Jonas M. Kübler; Dominik Janzing (CLeaR, 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 003; George Karypis (KDD, 2024)

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models. - David Hoffmann; Kailash Budhathoki; Matthäus Kleindessner (CoRR, 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)

A Proximal Operator for Inducing 2:4-Sparsity. - Jonas M. Kübler; Yu-Xiang Wang 003; Shoham Sabach; Navid Ansari; Matthäus Kleindessner; Kailash Budhathoki; Volkan Cevher; George Karypis (CoRR, 2025)