- TISS: (link)
- contact: Patrick Indri
- meeting link: https://tuwien.zoom.us/my/patrickindri
- everything important will be announced in TUWEL/TISS.

This seminar simulates a machine learning conference, where the students take on the role of authors and reviewers. It consists of multiple phases.

Attend the **mandatory** first meeting either in person or remotely (details on TUWEL).

You select

twotopics/papers (i.e., two bullet points) from one of the topics below. You will work with the material mentioned in the overview and the topic-specific resources.

You choose

your owntopic to work on. This can be some existing machine learning paper/work or an own creative idea in the context of machine learning. We strongly encourage you to start from existing papers from the following venues: NeurIPS, ICML, ICLR, COLT, AISTATS, UAI, JMLR, MLJ. Importantly, your idea has to be specific and worked out well. Nevertheless, chooseoneof our suggestions as well.

**Independent of the option you chose**, understand the fundamentals of your topic and try to answer the following questions:

**What**is the problem?**Why**is it an interesting problem?**How**do you plan to approach the problem? /**How**have the authors of your topic approached the problem?

Select topics and write a short description of them together with the answers to the questions (~3 sentences should be sufficient) in **TUWEL**.

We can only accept your own proposals if you can answer the mentioned questions and have a well worked out topic.

You and your fellow students will act as reviewers and bid on the topics of your peers you want to review. Based on the biddings, we (in the role as chairs of the conference) will select one of each student’s proposals as the actual project you will work on for the rest of this semester. You **do not** need to work on the other project, anymore. Additionally, we will also assign two different projects from other students to you, which you will have to review later in the semester.

Now the actual work starts. Gather deep understanding of your topic, write a first draft of your report and give a 5-minute presentation. Feel free to go beyond the given material.

You will schedule two meetings with your supervisor to discuss your progress, but do not hesitate to contact him/her if you have any questions.

You will again act as a reviewer for the conference by writing two reviews, one for each draft report assigned to you.

Based on the reviews from your peers (and our feedback) you will further work on your topic.

Give a final presentation and submit your report.

- Understanding machine learning: from theory to algorithms. Shai Shalev-Shwartz and Shai Ben-David (pdf)
- Foundations of machine learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (pdf)
- Foundations of data science. Avrim Blum, John Hopcroft, and Ravindran Kannan (pdf)
- Mathematics for machine learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (pdf)
- Mining of massive datasets. Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman (pdf)
- Reinforcement learning: an introduction. Richard Sutton and Andrew Barto (pdf)
- Deep learning and neural networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (pdf)

You should have access to the literature and papers through Google scholar, DBLP, the provided links, or the TU library.

- Shai Ben-David and Shai Shalev-Shwartz. Understanding Machine Learning. Chapter 2,3
- Shai Ben-David Lectures. (youtube-link) Lecture 1,2,3
- Martin Grohe and Martin Ritzert. Learning First-Order definable concepts over structures of small degree. 2017
- Grohe et al. Learning MSO-definable hypotheses on strings. 2017
- Bergeram et al. On Parameterized Complexity of learning Monadic Second-Order Formulas. 2023

**Motivation**: Ability to learn logically definable concepts from labelled data is a theoretical model of Machine Learning which is explainable by design, and integrates ideas from both logic (especially finite model theory) and PAC Learning.

**Overview**:

**Papers and topics**:

- P. Mazza et al. Machine learning time-local generators of open quantum dynamics, 2021
- F. Carnazza et al. Inferring Markovian quantum master equations of few-body observables in interacting spin chains
- https://github.com/fcarnazza/NN_Lindblad

**Motivation**

Full information about a many-body quantum system is usually out-of-reach due to the exponential growth – with the size of the system – of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phenomenology that can be observed in these systems, it is often sufficient to consider dynamical or stationary properties of local observables or, at most, of few-body correlation functions of a subsystem. One can formulate the problem of finding the generator of the subsystem dynamics as a variational problem, and solve them using the standard toolbox of machine learning for optimization.

**Overview**

H.-P. Breuer, F. Petruccione, The theory of open quantum systems Chapter 3.1 and 3.2

**Papers and topics**

- chapter 1 “Automating inquiry” of Burr Settles’ “Active learning” (AL) book, 2012.
- introduction to active learning: Sanjoy Dasgupta - Microsoft 2016 (youtube-link)
- Bayesian active learning on graphs (Ma, Yifet, et al., “σ-optimality for active learning on gaussian random fields.” NIPS 2013)
- active search on graphs (Wang, Xuezhi, et al., “Active search on graphs” KDD 2013)
- shortest-path-based active learning (Dasarathy, et al. “S2: an efficient graph based active learning algorithm with application to nonparametric classification.” COLT 2015)

**Motivation**: In active learning, the learning algorithm is allowed to select the data points it wants to see labelled, for example, where it is most uncertain. The goal is to reduce the labelling effort. This is useful in applications where unlabelled data is abundant, yet labels are scarce, such as node classification in social networks, drug discovery, and autonomous driving.

**Overview**:

**Papers and topics**:

- Petersen,
*Learning with Differentiable Algorithms*, Doctoral Thesis at the University of Konstanz, arXiv, (2022)

**Motivation:** Classical algorithms cannot be combined with neural networks as training neural networks would require the computation of a gradient which is not possible for classical algorithms. Thus, we are interested in making classical algorithms differentiable.

**Papers & Projects:**

The above thesis gives an introduction to the field of differentiable algorithms and is built upon many conference publications by the author at strong ML conferences. We recommend picking either one of the chapters or one of the underlying publications from ICML, ICLR, NeurIPS or CVPR as a basis.

- What is a good representation? (Bengio, et al., “Representation Learning: A Review and New Perspectives”, 2013)
- Two common architectures used for disentanglement:
- Variational Auto-Encoders (Kingma & Welling, “Auto-Encoding Variational Bayes”, 2013, and “An Introduction to Variational Autoencoders”, 2019)
- Generative Adversarial Networks (Goodfellow, et al., “Generative Adversarial Nets”, 2014)
- survey on useful Metrics (Carbonneau, et al., “Measuring Disentanglement: A Review of Metrics”, 2022; and Eastwood & Williams, “A Framework for the Quantitative Evaluation of Disentangled Representations”, 2018; and Do & Tran, “Theory and Evaluation Metrics for Learning Disentangled Representations”, 2019)
- fairness (Creager, et al., “Flexibly Fair Representation Learning by Disentanglement”, 2019)
- contrastive Learning (Cao, et al., “An Empirical Study on Disentanglement of Negative-free Contrastive Learning”, 2022)
- recommender Systems (Ma, et al., “Learning Disentangled Representations for Recommendation”, 2019)
- weakly-Supervised (Locatello, et al., “Weakly-Supervised Disentanglement Without Compromises”, 2020)
- semi-supervised (Nie, et al., “Semi-Supervised StyleGAN for Disentanglement Learning”, 2020)

**Motivation**: Computing a disentangled representation is a very desirable property for modern deep learning architectures. Having access to individual, disentangled factors is expected to provide significant improvements for generalisation, interpretability and explainability.

**Overview**:

**Papers and topics**:

- chapter 8 “equivariant neural networks” of “Deep learning for molecules and materials” by Andrew D. White, 2021. (pdf).
- introduction to equivariance: Taco Cohen and Risi Kondor - Neurips 2020 Tutorial (first half) (slideslive-link)
- neural network that can learn on sets (Zaheer, et al. “Deep sets.” NeurIPS 2017)
- learning equivariance from data (Zhou, et al. “Meta-learning symmetries by reparameterization.” ICLR 2021)

**Motivation**: Many datastructures have an innate structure that our neural networks should respect. For example the output of a graph neural networks should not change if we permute the vertices (permutation equivariance/invariance).

**Overview**:

**Papers and topics**:

- notes on generalisation (Prof. Roger Grosse) (link)
- generalisation and overfitting (youtube-link)
- memorisation (Arpit, et al. “A closer look at memorization in deep networks.” ICML 2017)
- double-descent (Belkin, et al. “Reconciling modern machine-learning practice and the classical bias–variance trade-off.” Proceedings of the National Academy of Sciences 2019)
- generalisation gap (Keskar, et al. “On large-batch training for deep learning: Generalization gap and sharp minima.” ICLR 2017)
- loss landscape (Fort and Jastrzebski. “Large scale structure of neural network loss landscapes.” NeurIPS 2019
**and**Li, et al. “Visualizing the loss landscape of neural nets.” NeurIPS 2018)

**Motivation**: The ability of a model to adapt and perform well on new data is crucial. A model which generalises not only performs well on the training set, but on unseen data as well. Understanding and characterising why and how deep learning can generalise well is still an open question.

**Overview**:

**Papers and topics**:

- Veličković,
*Everything is connected: Graph neural networks*, Current Opinion in Structural Biology, 2023 - Sanchez-Lengeling et al.,
*A Gentle Introduction to Graph Neural Networks*, distill.pub 2021 - Veličković,
*Intro to graph neural networks (ML Tech Talks)*, YouTube, 2021 - Expressiveness of GNNs
- Zhang et al.,
*Rethinking the Expressive Power of GNNs via Graph Biconnectivity*, ICLR, 2023 - Lim et al.,
*Sign and Basis Invariant Networks for Spectral Graph Representation Learning*, ICLR, 2023 - Hwang et al.,
*An Analysis of Virtual Nodes in Graph Neural Networks for Link Prediction*, LoG, 2022

- Zhang et al.,
- Oversmoothing & Over-Squashing
- Keriven,
*Not too little, not too much: a theoretical analysis of graph (over)smoothing*, NeurIPS, 2023 - Abboud et al.,
*Shortest Path Networks for Graph Property Prediction*, LoG, 2022 - Huang et al.,
*You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets*, LoG, 2022

- Keriven,
- Neural Algorithmic Reasoning / Algorithm Representation Learning
- Numeroso et al.,
*Dual Algorithmic Reasoning*, ICLR, 2023

- Numeroso et al.,
- Learning from Graph Data with MLPs
- Tian et al.,
*Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency*, ICLR, 2023

- Tian et al.,

**Motivation:** Graphs are a very general structure and can be applied to many areas: molecules and developing medicine, geographical maps, spread of diseases. They can be used to model physical systems and solve partial differential equations. Even images and text can be seen as a special case of graphs. Thus it makes sense to develop neural networks that can work with graphs. GNNs have strong connections to many classical computer science topics (algorithmics, logic, …) while also making use of neural networks. This means that work on GNN can be very theoretical, applied or anything in between.

**Overview:**

**Papers & Projects:**

*Note:* For very long papers we do not expect you to read the entire appendix.

**Motivation**:
Large language models such as ChatGPT are seeing a huge research interest. Some companies are releasing more or less free models, and open-source initiatives have sprung up.

**Overview**:
In this seminar paper, an overview of the latest large language models that are available in various forms is given. This includes, in particular, an investigation of their performance, and an explanation how performance can be evaluated objectively at all.

**Advisor**: Prof. Clemens Heitzinger

**Motivation:**

Large language models such as ChatGPT are seeing a huge research interest. Some companies are releasing more or less free models, and open-source initiatives have sprung up.

**Overview:**

In this seminar paper, an overview of the latest large language models that are available in various forms is given. This includes, in particular, an investigation of their performance, and an explanation how performance can be evaluated objectively at all.

- Renormalization Group Flow as Optimal Transport by Jordan Cotler and Semon Rezchikov, https://arxiv.org/abs/2202.11737
- Learning Interacting Theories from Data by Claudia Merger et al., https://arxiv.org/abs/2304.00599

**Motivation:**
Exploration of new perspectives opened by ML methods for complex quantum systems and/or improving machine learning methods from the point of view as a physical system of interacting elements

**Overview:**
Statistical field theory for neural networks (Lecture notes)
by Moritz Helias and David Dahmen, https://arxiv.org/abs/1901.10416

**Related Works:**

**Advisor**: Prof. Sabine Andergassen

- P. Mazza et al. Machine learning time-local generators of open quantum dynamics, 2021
- F. Carnazza et al. Inferring Markovian quantum master equations of few-body observables in interacting spin chains https://github.com/fcarnazza/NN_Lindblad

**Task**: Inferring Markovian quantum master equations of few-body observables in interacting spin chains

**Motivation** Full information about a many-body quantum system is usually out-of-reach due to the exponential growth – with the size of the system – of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phenomenology that can be observed in these systems, it is often sufficient to consider dynamical or stationary properties of local observables or, at most, of few-body correlation functions of a subsystem. One can formulate the problem of finding the generator of the subsystem dynamics as a variational problem, and solve them using the standard toolbox of machine learning for optimization.

**Overview**
H.-P. Breuer, F. Petruccione, The theory of open quantum systems Chapter 3.1 and 3.2

**Papers and topics**

**Advisor**: Francesco Carnazza

- Renormalization Group Flow as Optimal Transport by Jordan Cotler and Semon Rezchikov, https://arxiv.org/abs/2202.11737
- Learning Interacting Theories from Data by Claudia Merger et al., https://arxiv.org/abs/2304.00599

**Motivation**:

Exploration of new perspectives opened by ML methods for complex quantum systems and/or improving machine learning methods from the point of view as a physical system of interacting elements

**Overview**:

Statistical field theory for neural networks (Lecture notes) by Moritz Helias and David Dahmen, https://arxiv.org/abs/1901.10416

**Papers**:

- Chapter 2 “Spaceborne Synthetic Aperture Radar: Principles, Data Access, and Basic Processing Techniques” of Franz Meyer’s “SAR Handbook” book, 2019.
- “A Tutorial on Synthetic Aperture Radar” (IEEE Geoscience and remote sensing magazine 2013) by A. Moreira, et al.
- SAR Classification (Miller, et al. “Graph-based Active Learning for Semi-supervised Classification of SAR Data.” 2022)
- SAR Features (Zhang, et al. “Sparse Feature Clustering Network for Unsupervised SAR Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 2022)
- SAR Despeckling (Gu, et al. “A Two-Component Deep Learning Network for SAR Image Denoising.” IEEE Access 2021)
- SAR Despeckling (Yuan, et al. “Blind SAR Image Despeckling Using Self-Supervised Dense Dilated Convolutional Neural Network.” 2019)
- SAR Features (Jiang ,et al. “Unsupervised Deep Sparse Features Extraction for SAR Image Segmentation.” IEEE Transactions on Geoscience and Remote Sensing 2022)

**Motivation**: Synthetic Aperture Radar (SAR) is an active microwave imaging system that provides high-resolution images day and night under all weather conditions. It has been widely used in many practical applications, such as environment, crop monitoring, and disaster detection. Using best-suited machine learning algorithms to derive useful information from these data is essential.

**Overview**:

**Papers and topics**:

**Motivation:**

Transformers have revolutionized natural-language processing, and research has exploded since 2017.

**Overview:**

In this seminar paper, the functioning of transformers is explained and an overview of the latest developments, regarding large language models, time-series predction, etc., is given.

**Motivation**:
Transformers have revolutionized natural-language processing, and research has exploded since 2017.

**Overview**:
In this seminar paper, the functioning of transformers is explained and an overview of the latest developments, regarding large language models, time-series predction, etc., is given.

**Advisor**: Prof. Clemens Heitzinger

- General: What does it mean for ML to be trustworthy? (youtube-link)
- General: Trustworthy ML (Kush R. Varshney) (link)
- Differential privacy: Chapter 2 of: Dwork, Cynthia, and Aaron Roth. “The algorithmic foundations of differential privacy.” Found. Trends Theor. Comput. Sci. 9.3-4 2014
- Explainability: Samek, Wojciech, and Klaus-Robert Müller. “Towards explainable artificial intelligence.” Explainable AI: interpreting, explaining and visualizing deep learning.” Springer, Cham, 2019
- interpreting model predictions
- Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. ““Why should i trust you?” Explaining the predictions of any classifier.” ACM SIGKDD 2016
- Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions.” NeurIPS 2017

- reliability of explanation methods
- Kumar, I. Elizabeth, et al. “Problems with Shapley-value-based explanations as feature importance measures.” ICML, 2020.

- robustness against attacks and adversaries
- Jagielski, Matthew, et al. “Manipulating machine learning: Poisoning attacks and countermeasures for regression learning.” 2018 IEEE Symposium on Security and Privacy (SP). IEEE, 2018.
- Carmon, Yair, et al. “Unlabeled data improves adversarial robustness.” NeurIPS 2019.

- differential privacy
- Abadi, Martin, et al. “Deep learning with differential privacy.” Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.
- Patel, Neel, Reza Shokri, and Yair Zick. “Model explanations with differential privacy.” 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022.

**Motivation**: Machine learning systems are ubiquitous and it is necessary to make sure they behave as intended. In particular, trustworthiness can be achieved by means of privacy-preserving, robust, and explainable algorithms.

**Overview**:

**Papers and topics**: