Rizal Fathony

I graduated from UIC and moved to CMU as a post-doctoral fellow. Please visit my new homepage.
This page is no longer maintained.

Performance-Aligned Learning Algorithms with Statistical Guarantees

My research focuses on designing robust adversarial learning algorithms for various tasks that simultaneously align with the learning objective by incorporating the performance metrics or loss functions into the learning process and provide the statistical guarantee of Fisher consistency. In my previous and ongoing works, I design robust adversarial learning algorithms for the following tasks:
- Multiclass classification
- Ordinal classification with absolute loss and squared loss
- Classification with abstention
- Conditional graphical models (chain, tree, and low-treewidth graph structures)
- Bipartite matching in graphs
- Fairness in machine learning

 

Research interest:

Consistent learning algorithms	Fairness in machine learning
Multivariate metrics	Multi-task learning
Robust adversarial learning	Deep learning
Structured prediction	Causal machine learning
Approximate inference	Bayesian machine learning
Multivariate metrics	Multi-modal regression

In submission:

[1] Ashkan Rezaei*, Rizal Fathony*, Omid Memarrast, Brian D. Ziebart. “Fair Logistic Regression: An Adversarial Perspective”. [arXiv]

[2] Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Consistent Robust Adversarial Prediction for General Multiclass Classification”. Journal manuscript in submission. [arXiv]

PhD Thesis:

[3] Rizal Fathony. “Performance-Aligned Learning Algorithms with Statistical Guarantees”. PhD Thesis. University of Illinois at Chicago. 2019. [pdf] [slides]

Conferences:

[4] Rizal Fathony, Ashkan Rezaei, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Distributionally Robust Graphical Models”. Advances in Neural Information Processing Systems 31 (NeurIPS), 2018. [neurips] [pdf] [poster] [code]

[5] Rizal Fathony*, Sima Behpour*, Xinhua Zhang, Brian D. Ziebart. “Efficient and Consistent Adversarial Bipartite Matching”. International Conference on Machine Learning (ICML), 2018. [pmlr/icml] [pdf] [poster] [code]

[6] Rizal Fathony, Mohammad Bashiri, Brian D. Ziebart. “Adversarial Surrogate Losses for Ordinal Regression”. Advances in Neural Information Processing Systems 30 (NIPS), 2017. [nips] [pdf] [poster] [code]

[7] Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart. “Adversarial Multiclass Classification: A Risk Minimization Perspective”. Advances in Neural Information Processing Systems 29 (NIPS), 2016. [nips] [pdf] [poster] [code]

Workshops:

[8] Rizal Fathony, Ashkan Rezaei, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Distributionally Robust Graphical Models”. The 3rd Workshop On Tractable Probabilistic Modeling (TPM) @ ICML, 2019

[9] Anqi Liu, Rizal Fathony, Brian D. Ziebart. “Learning to Explore by Abstaining”. 13th Women in Machine Learning Workshop (WiML) @ NeurIPS, 2018.

Preprints:

[10] Anqi Liu, Rizal Fathony, Brian D. Ziebart. “Kernel Robust Bias-Aware Prediction under Covariate Shift”. ArXiv Preprints, 2016. [arXiv]

NeurIPS Travel Award 2018

NIPS Travel Award 2017

NIPS Travel Award 2016

Won an International Fulbright Master of Science and Technology Scholarship Award (2012 - 2014)

Runner Up Developer at Indonesia Open Source Festival - Android Apps Competition (2010)

Nominee of Research and Development Category at Asia Pacific Information and Communication Technology Award (APICTA), Melbourne, Australia (2009)

Best Research and Development Category at Indonesia Information and Communication Technology Award, Jakarta, Indonesia (2009)

Grantee of a full scholarship and monthly stipend from the Indonesian government during undergraduate study at the Institute of Statistics, Jakarta, Indonesia (2003 - 2007)