Machine Learning Seminar (Summer Term 2024)

Overview

Seminar (0/2/0) in GÖR/0127/U on Fridays, 9:20 - 10:50
Co-located with the Computer Vision Seminar. Talks are marked as belonging to one or the other seminar.
Lecturer: Bjoern Andres
Teaching Assistants: Jannik Irmai, David Stein, Holger Heidrich

Schedule

In this seminar, participating students will read, understand, prepare and present the contents of a research article or book chapter on a topic from the field of machine learning. The article or book chapter will be chosen by the student from the list below, or suggested by the student for approval in the beginning of the term. The preparation will include relevant foundational and related work. By attending at least seven presentations of their peers, students will get an overview of diverse topics in the field of machine learning.

Prerequisites

Prerequisites for taking this course are a solid foundational education in mathematics (esp. linear algebra and analysis) and theoretical computer science, as well as basics of machine learning, comparable to the contents of the course Machine Learning 1. For some of the suggested articles and book chapters, additional knowledge from the field of mathematical optimization is required.

Requirements

Requirements for passing this course are:

A 30-minute oral presentation by the student
A written report of about six pages to be submitted by the student before their presentation → Template with instructions
Active participation in at least seven presentations by other students
Additional forms of examination if required by the module toward which the course is to be credited

Suggested Research Articles

Clustering

Philip N. Klein, Claire Mathieu, Hang Zhou. Correlation Clustering and Two-Edge-Connected Augmentation for Planar Graphs. Algorithmica 85, 3024–3057 (2023)
Raka Jovanovic, Antonio P. Sanfilippo, Stefan Voß. Fixed set search applied to the clique partitioning problem. European Journal of Operational Research 309(1):65-81 (2023)
Ahmed Abbas, Paul Swoboda. ClusterFuG: Clustering Fully connected Graphs by Multicut. ICML 2023
Erik D. Demaine, Dotan Emanuel, Amos Fiat, and Nicole Immorlica. Correlation Clustering in General Weighted Graphs. Theoretical Computer Science 361(2-3):172-187 (2006)
Nikhil Bansal, Avrim Blum and Shuchi Chawla. Correlation Clustering. Machine Learning 56:89-113 (2004)

Ordering

Christoph Buchheim, Angelika Wiegele and Lanbo Zheng. Exact Algorithms for the Quadratic Linear Ordering Problem. INFORMS J. Comput. 22(1):168-177 (2010)

Unsupervised Learning

Vivien Cabannes, Alessandro Rudi and Francis R. Bach. Structured Prediction with Partial Labelling through the Infimum Loss. ICML 2020

Structured Learning

Jan-Hendrik Lange, Paul Swoboda. Efficient Message Passing for 0-1 ILPs with Binary Decision Diagrams. ICML 2021
Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter. Efficient semidefinite-programming-based inference for binary and multi-class MRFs. NeurIPS 2020
Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy. Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020
Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy. Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 40(7): 1668-1682 (2018)
Stefan Haller, Paul Swoboda, Bogdan Savchynskyy. Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation. AAAI 2018
Paul Swoboda, Jan Kuske, Bogdan Savchynskyy. A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems. CVPR 2017
Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes, Christoph Schnörr, Bogdan Savchynskyy. Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 38(7): 1370-1382 (2016)

Embedding

George C. Linderman and Stefan Steinerberger. Clustering with t-SNE, provably. SIAM Journal on Mathematics of Data Science 1(2):313-332 (2019)

Deep Learning

Siddharth Tourani, Muhammad Haris Khan, Carsten Rother, Bogdan Savchynskyy. Discrete Cycle-Consistency Based Unsupervised Deep Graph Matching. AAAI 2024
Ahmed Abbas, Paul Swoboda. DOGE-Train: Discrete Optimization on GPU with End-to-End Training. AAAI 2024
Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, Sean Welleck. Llemma: An Open Language Model For Mathematics. ICLR 2024
Marin Vlastelica Pogancic, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolínek. Differentiation of Blackbox Combinatorial Solvers. ICLR 2020
Michal Rolínek, Vit Musil, Anselm Paulus, Marin Vlastelica, Claudio Michaelis and Georg Martius. Optimizing Rank-Based Metrics With Blackbox Differentiation. CVPR 2020
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. Attention is All you Need. NeurIPS 2017