Algorithms & Society

My research studies machine learning as part of a broader sociotechnical ecosystem. I am broadly interested in developing theoretical as well as practical tools to support safe, reliable and trustworthy machine learning with a positive impact on society. Research areas of interest include interactive machine learning, optimization in dynamic environments, decentralized and resource-efficient training, social dynamics around algorithms, and the role of algorithmic decision making in digital economies.

Performative Prediction

Predictions when deployed in societal systems can trigger actions and reactions of individuals. Thereby they can change the way the broader system behaves -- a dynamic effect that traditional machine learning fails to account for. To formalize and reason about performativity in the context of machine learning, we have developed the framework of performative prediction. It extends the classical risk minimization framework in that it allows the data distribution to depend on the deployed model. This inherently dynamic viewpoint leads to new solution concepts and optimization challenges, it brings forth interesting connections to concepts from causality and game theory, and it relates machine learning to the study of century-old questions about the predictability of social events.

Performative Prediction
Performative Prediction

A thoeretical frameworks to reason about the impact of predictions on individual behavior and society.
https://arxiv.org/abs/2002.06673/

Power in Digital Economies

Algorithmic predictions play an increasingly important role in digital economies as they mediate services, platforms, and markets at societal scale. The fact that such services can steer consumption and behavior is a central concern in modern anti-trust. I am exploring how the concept of performativity can help quantify economic power in digital economies and support digital market investigations. Intuitively, the more powerful a firm the more performative their predictions, a causal effect strength we can measure from observational and experimental data.

Powermeter
Powermeter

A Chrome extension that measure how algorithmic decisions of search engines can impact user click behavior by collecting experimental data while you surf.

→ Sign up at https://powermeter.is.tue.mpg.de/

Scalable Learning Algorithms

When building societal-scale machine learning systems, training time and resource constraints can be a critical bottleneck for dynamic system optimization. Efficient training algorithms that are aware of distributed architectures, interconnect topologies, memory constraints and accelerator units form an important bilding block towards using available resources most efficiently. As part of my PhD research, we demonstrated that system-aware algroithms can lead to several orders of magnitude reduction in training time compared to standard system-agnostic methods. Today, the innovations of my PhD research form the backbone of the IBM Snap ML library that has been integrated with several of IBM's core AI products.

Snap ML
IBM Snap Machine Learning

Snap ML is a library that provides resource efficient and fast training of popular machine learning models on modern computing systems.
>400k downloads on PyPi
https://www.zurich.ibm.com/snapml/

Publications

*alphabetical order
Performative Prediction: Past and Future
M.Hardt and C.Mendler-Dünner
Arxiv, 2023.
Questioning the Survey Responses of Large Language Models
R. Dominguez-Olmedo, M.Hardt and C.Mendler-Dünner
Arxiv, 2023.
Collaborative Learning via Prediction Consensus
D.Fan, C.Mendler-Dünner and M.Jaggi
to appear in Advances in Neural Information Processing Systems (NeurIPS), 2023.
Algorithmic Collective Action in Machine Learning
M.Hardt*, E.Mazumdar*, C.Mendler-Dünner* and T.Zrnic*
International Conference on Machine Learning (ICML), 2023.
Anticipating Performativity by Predicting from Predictions
C.Mendler-Dünner, F.Ding and Y.Wang
Advances in Neural Information Processing Systems (NeurIPS), 2022.
Performative Power
M.Hardt*, M.Jagadeesan* and C.Mendler-Dünner*
Advances in Neural Information Processing Systems (NeurIPS), 2022.
Regret Minimization with Performative Feedback
M.Jagadeesan, T.Zrnic and C.Mendler-Dünner
International Conference on Machine Learning (ICML), 2022.
Symposium on Foundations of Responsible Computing (FORC), 2022.
Test-time Collective Prediction
C.Mendler-Dünner, W.Guo, S.Bates and M.I.Jordan
Advances in Neural Information Processing Systems (NeurIPS), 2021.
Alternative Microfoundations for Strategic Classification
M.Jagadeesan, C.Mendler-Dünner and M.Hardt
International Conference on Machine Learning (ICML), 2021.
Differentially Private Stochastic Coordinate Descent
G.Damaskinos, C.Mendler-Dünner, R.Guerraoui, N.Papandreou and T.Parnell
AAAI Conference on Artificial Intelligence (AAAI), 2021.
Stochastic Optimization for Performative Prediction
C.Mendler-Dünner*, J.C.Perdomo*, T.Zrnic* and M.Hardt
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Performative Prediction
J.C.Perdomo*, T.Zrnic*, C.Mendler-Dünner and M.Hardt
International Conference on Machine Learning (ICML), 2020.
Randomized Block-Diagonal Preconditioning for Parallel Learning
C.Mendler-Dünner and A.Lucchi
International Conference on Machine Learning (ICML), 2020.
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
N.Ioannou*, C.Mendler-Dünner* and T.Parnell
Advances in Neural Information Processing Systems (NeurIPS -- Spotlight), 2019.
On Linear Learning with Manycore Processors
E.Wszola, C.Mendler-Dünner, M.Jaggi and M.Püschel
IEEE International Conference on High Performance Computing (HiPC -- best paper finalist), 2019.
System-Aware Algorithms for Machine Learning
C.Mendler-Dünner
ETH Research Collection (PhD Thesis -- ETH medal), 2019.
Snap ML: A Hierarchical Framework for Machine Learning
C.Dünner*, T.Parnell*, D.Sarigiannis, N.Ioannou, A.Anghel, G.Ravi, M.Kandasamy and H.Pozidis
Advances in Neural Information Processing Systems (NeurIPS), 2018.
A Distributed Second-Order Algorithm You Can Trust
C.Dünner, M.Gargiani, A.Lucchi, A.Bian, T.Hofmann and M.Jaggi
International Conference on Machine Learning (ICML), 2018.
Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems
C.Dünner*, M. Vlachos*, R.Heckel, V.Vassiliaadis, T.Parnell and K.Atasu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.
Tera-Scale Coordinate Descent on GPUs
T.Parnell, C.Dünner, K.Atasu, M.Sifalakis and H.Pozidis
Journal of Future Generation Computer Systems (FGCS), 2018.
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
C.Dünner, T.Parnell and M.Jaggi
Advances in Neural Information Processing Systems (NIPS), 2017.
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark
C.Dünner, T.Parnell, K.Atasu, M.Sifalakis and H.Pozidis
IEEE International Conference on Big Data (IEEE Big Data), 2017.
High-Performance Recommender System Training Using Co-Clustering on CPU/GPU Clusters
K.Atasu, T.Parnell, C.Dünner, M.Vlachos and H.Pozidis
International Conference on Parallel Processing (ICPP), 2017.
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering
R.Heckel, M.Vlachos, T.Parnell and C.Dünner
IEEE International Conference on Data Engineering (ICDE), 2017.
Primal-Dual Rates and Certificates
C.Dünner, S.Forte, M.Takac and M.Jaggi
International Conference on Machine Learning (ICML), 2016.

Peer-reviewed Workshop Contributions

Causal Inference out of Control: Estimating the Steerability of Consumption
G.Cheng, M.Hardt and C.Mendler-Dünner
A Causal View on Dynamical Systems Workshop (CDS@NeurIPS -- Oral presentation), 2022.
Revisiting Design Choices in Proximal Policy Optimization
C.C.-Y.Hsu, C.Mendler-Dünner and M.Hardt
Workshop on Real World Challenges in RL (RWRL@NeurIPS), 2020.
Differentially Private Stochastic Coordinate Descent
G.Damaskinos, C.Mendler-Dünner, R.Guerraoui, N.Papandreou and T.Parnell
Workshop on Privacy Preserving ML (PPML@NeurIPS), 2020.
Breadth-first, Depth-next Training of Random Forests
A.Anghel*, N.Ioannou*, T.Parnell, N.Papandreou, C.Mendler-Dünner and H.Pozidis
Workshop on Systems for ML (MLSys@NeurIPS), 2019.
Snap ML
C.Mendler-Dünner and A.Anghel
Women in Machine Learning Workshop (WiML@NeurIPS), 2018.
Sampling Acquisition Functions for Batch Bayesian Optimization
A.De Palma, C.Mendler-Dünner, T.Parnell, A.Anghel and H.Pozidis
Workshop on Bayesian Nonparametrics (BNP@NeurIPS), 2018.
Parallel training of linear models without compromising convergence
N.Ioannou, C.Mendler-Dünner, K.Kourtis, T.Parnell
Workshop on Systems for ML (MLSys@NeurIPS), 2018.
Large-Scale Stochastic Learning using GPUs
T.Parnell, C.Dünner, K.Atasu, M.Sifalakis and H.Pozidis
IEEE International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning), 2017.

Patents

US20210264320A1 - T. Parnell, A. Anghel, N. Ioannou, N. Papandreou, C. Mendler-Dünner, D. Sarigiannis, H. Pozidis.
US11562270B2 - M. Kaufmann, T. Parnell, A. Kourtis, C. Mendler-Dünner.
US11573803B2 - N. Ioannou, C. Dünner, T. Parnell.
US11295236B2 - C. Dünner, T. Parnell, H. Pozidis.
US11315035B2 - T. Parnell, C. Dünner, H. Pozidis, D. Sarigiannis
US11461694B2 - T. Parnell, C. Dünner, D. Sarigiannis, H. Pozidis.
US11301776B2 - C. Dünner, T. Parnell, H. Pozidis.
US10147103B2 - C. Dünner, T. Parnell, H. Pozidis, V. Vasileiadis, M. Vlachos.
US10839255B2 - K. Atasu, C. Dünner, T. Mittelholzer, T. Parnell, H. Pozidis, M. Vlachos.