Bachelor and Master thesis topics:
( methodological focus, technical focus)

Master Thesis at ISMLL

• How to Start a Master Thesis
• Available Thesis Topics
• Current Master Theses
• Past Master Theses

Available Supervisors

Name	Available Slots
Dr. Josif Grabocka	2
Mohsan Jameel, M.Sc.	From June 2019
Rafael Rêgo Drumond, M.Sc.	From September 2020
Ahmed Rashed, M.Sc.	From December 2019
Hadi Samer Jomaa, M.Eng.	1
Randolf Scholz, M.Sc.	0
Jonas Falkner, M.Sc.	0
Vijaya Krishna Yalavarthi, M.Eng.	From April 2020
Mofassir ul Islam Arif	1
Lukas Brinkmeyer	From July 2020
Eya Boumaiza	From April 2020
Shayan Jawed	From February 2020

Available Master Thesis topics at ISMLL

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The notion of attention in machine learning approaches proved successful in domain adaptation, time series forecasting, and image classification. However, all attention techniques are designed on an instance level. In this thesis, you will investigate the a new form of attention, by means of dataset meta-feature similarity. The idea in a nutshell is that, the performance of a target model for a given hyper-parameter configuration depends on the similarity between the target dataset itself and the support dataset, as well as the corrsesponding surrogate value of support dataset .

References: -

• [1] Vinyals, O., Blundell, C., Lillicrap, T., and Wierstra, D. (2016). Matching networks for one shot learning. In Advances in neural information processing systems (pp. 3630-3638).
• [2] Jomaa, H. S., Schmidt-Thieme, L., and Grabocka, J. (2019). Dataset2vec: Learning dataset meta-features. arXiv preprint arXiv:1905.11063.

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Recent advances in Gradient based Hyperparameter Optimization allow the efficient optmization of large amounts of hyperparameters. Yet many questions are unexplored: How useful is it to optimize many hyperparameters? How to void overfitting on the validation data? What are interesting new model architectures to experiment with? In this thesis, you will perform exploratory work with gradient based hyperparameter optimization. A strong background in linear algebra and differential calculus is required.

References:

1. Lorraine, Jonathan, Paul Vicol, and David Duvenaud. "Optimizing Millions of Hyperparameters by Implicit Differentiation." arXiv preprint arXiv:1911.02590 (2019)

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Traditional recommender systems assume that the user profile and item attributes are static factors. This assumption doesn't consider the temporal dynamics that affect the user profile or the items e.g. user interest in an item may change based on the season of the year like watching Christmas movies in summer and winter. This thesis will identify the potential approaches to build a dynamic recommender system model that can predict users interest in items based on their historical interactions and behaviors.

References: -

• 1) Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent Recommender Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM '17). ACM, New York, NY, USA, 495-503. DOI: https://doi.org/10.1145/3018661.3018689
• 2) Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, and Shaoping Ma. 2015. Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis. In Proceedings of the 24th International Conference on World Wide Web (WWW '15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1373-1383. DOI: https://doi.org/10.1145/2736277.2741087

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The task of classifying multi-relational data spans a widerange of domains such as document classification in cita-tion networks, classification of emails and protein labeling inproteins interaction graphs. Current state-of-art classifica-tion models rely on learning per-entity latent representationsby mining the whole structure of the relations graph, how-ever, they still face two major problems. Firstly, it is verychallenging to generate expressive latent representations insparse multi-relational settings with implicit feedback rela-tions as there is very little information per-entity. Secondly,for entities with structured properties such as titles and ab-stracts (text) in documents, models have to be modified ad-hoc. This thesis will identify the potential deep learning approaches to predict entities labels based on their relations and interaction information such as predicting document's category in citation networks or prediciting user interest in social networks.

References: -

• Cai, Hongyun, Vincent W. Zheng, and Kevin Chang. "A comprehensive survey of graph embedding: problems, techniques and applications." IEEE Transactions on Knowledge and Data Engineering (2018).
• Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016).

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Siamese networks have grown quite popular in the field of Machine Learning specially with research about object tracking and text equivalence, since they are able to compare samples and estimate their similarity. The hypothesis is that this architecture can be proven as a useful tool if you want to compare similarities between features from different data-sets becoming an asset for performing Model Agnostic Meta-Learning across tasks with different schemas.

In this thesis you will implement a siamese network to improve the performance of Model Agnostic Meta-Learning Methods that work across tasks with different schemas.

References:

• 1. Zhang, Zhipeng, and Houwen Peng. "Deeper and wider siamese networks for real-time visual tracking." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
• 2. Mueller, J., Thyagarajan, A. (2016, March). Siamese recurrent architectures for learning sentence similarity. In thirtieth AAAI conference on artificial intelligence.
• 3. Brinkmeyer, L., Drumond, R. R., Scholz, R., Grabocka, J., Schmidt-Thieme, L. (2019). Chameleon: Learning model initializations across tasks with different schemas.
• 4. Drumond, Rafael Rego, et al. "HIDRA: Head Initialization across Dynamic targets for Robust Architectures." Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020.

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The classical vehicle routing problem (VRP) designs optimal delivery routes where each vehicle only travels one route, each vehicle has the same characteristics and there is only one central depot. The goal of the VRP is to find a set of least-cost vehicle routes such that each customer is visited exactly once by one vehicle, each vehicle starts and ends its route at the depot, and the capacity of the vehicles is not exceeded. This classical VRP has been extended in many ways by introducing additional real-life aspects or characteristics, resulting in a large number of variants of the VRP. In the standard periodic vehicle routing problem (PVRP), customers require visits on one or more days within a planning period, and there are a set of feasible visit options for each customer. Customers must be assigned to a feasible visit option and a VRP is solved for each day in the planning period. The typical objective is to minimize the total distance traveled over the planning period. The PVRP arises in a diverse array of applications, from the collection of recyclables, to the routing of home healthcare nurses, to the collection of data in wireless networks. The wide applicability and versatility of the problem, coupled with the problem’s difficulty, has led to continuing interest and research efforts.

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science.

References:

• 1. Braekers, Kris, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. “The Vehicle Routing Problem: State of the Art Classification and Review.” Computers and Industrial Engineering 99 (September 1, 2016): 300–313. https://doi.org/10.1016/j.cie.2015.12.007.
• 2. Campbell, Ann Melissa, and Jill Hardin Wilson. “Forty Years of Periodic Vehicle Routing.” Networks 63, no. 1 (January 2014): 2–15. https://doi.org/10.1002/net.21527.
• 3. Zhou, Aimin, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. “Multiobjective Evolutionary Algorithms: A Survey of the State of the Art.” Swarm and Evolutionary Computation 1, no. 1 (March 1, 2011): 32–49. https://doi.org/10.1016/j.swevo.2011.03.001.
• 4. Ombuki, Beatrice, Brian J. Ross, and Franklin Hanshar. “Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows.” Applied Intelligence 24, no. 1 (February 1, 2006): 17–30. https://doi.org/10.1007/s10489-006-6926-z.

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The classical vehicle routing problem (VRP) designs optimal delivery routes where each vehicle only travels one route, each vehicle has the same characteristics and there is only one central depot. The goal of the VRP is to find a set of least-cost vehicle routes such that each customer is visited exactly once by one vehicle, each vehicle starts and ends its route at the depot, and the capacity of the vehicles is not exceeded. This classical VRP has been extended in many ways by introducing additional real-life aspects or characteristics, resulting in a large number of variants of the VRP. In the standard periodic vehicle routing problem (PVRP), customers require visits on one or more days within a planning period, and there are a set of feasible visit options for each customer. Customers must be assigned to a feasible visit option and a VRP is solved for each day in the planning period. The typical objective is to minimize the total distance traveled over the planning period. The PVRP arises in a diverse array of applications, from the collection of recyclables, to the routing of home healthcare nurses, to the collection of data in wireless networks. The wide applicability and versatility of the problem, coupled with the problem’s difficulty, has led to continuing interest and research efforts.

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming (NDP). It describes a recent methodology that deals with the approximate solution of large and complex dynamic programming (DP) problems. Accordingly NDP combines simulation, learning, approximation architectures (e.g., neural networks) and the central ideas of DP to break the curse of dimensionality that is typical of DP. Therefore NDP could be a promising approach to solve large-scale and complex VRP.

References:

• 1. Braekers, Kris, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. “The Vehicle Routing Problem: State of the Art Classification and Review.” Computers and Industrial Engineering 99 (September 1, 2016): 300–313. https://doi.org/10.1016/j.cie.2015.12.007.
• 2. Campbell, Ann Melissa, and Jill Hardin Wilson. “Forty Years of Periodic Vehicle Routing.” Networks 63, no. 1 (January 2014): 2–15. https://doi.org/10.1002/net.21527.
• 3. Sutton, Richard S, and Andrew G Barto. “Reinforcement Learning: An Introduction,”; https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
• 4. Secomandi, Nicola. “Comparing Neuro-Dynamic Programming Algorithms for the Vehicle Routing Problem with Stochastic Demands.” Computers and Operations Research 27, no. 11 (September 1, 2000): 1201–25. https://doi.org/10.1016/S0305-0548(99)00146-X.

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In general reinforcement learning, action space is pre-defined (number of actions are fixed throughout the task). But, in real world situations, we may need to use new actions to accomplish the tasks. It can also be observed in the video games where the number of actions may increase with increase in level of the game.

As an example, consider a well known DAVE game. In level one, there are the actions {up}, {left}, {right}. Once, we enter level three, we will get an additional action of {shooting}. If we do not know that in future we may get new action, one has to learn new action, and may need to train the reinforcement learning algorithm from scratch with the additional action. This will take lot of time and the knowledge gained in previous levels goes waste.

In this thesis, a novel learning technique that can adapt the new action incrementally not from the scratch. The algorithm shall not lose the existing knowledge (ex: knowledge of previous levels in video games).

References:

• 1. Venkatesan, Rajasekar, and Meng Joo Er. "A novel progressive learning technique for multi-class classification." Neurocomputing 207 (2016): 310-321.
• 2. Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529.

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In general, while training a classification algorithm, we have true labels (Y) and predicted predicted labels (Y'). General trend of updating the parameters of the algorithm is by back propogating the loss which is average of absolute difference (or RMS loss). However, recent works shows that considereing top-k loss [1], average top-k loss [2] provide better results compared with traditional method.

In this work, one would analyse the results using above methods and find a better distribution over the losses for increasing the accuracy of the algorithm.

For information:

Top-k loss: It is the k^{th} maximum value in |Y - Y'| (similarly in RMS loss)

Average Top-k loss: It is the average of top-k values in |Y - Y'| (similarly in RMS loss)

References:

• 1. S. Shalev-Shwartz and Y. Wexler. Minimizing the maximal loss: How and why. In ICML, 2016
• 2. Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu. Learning with Average Top-k Loss. In NeruIPS, 2017
• 3. Leonard Berrada, Andrew Zisserman, M. Pawan Kumar. Smooth Loss Functions for Deep Top-k Classification. in ICLR 2018

If you are interested in other topics, please ask one of us directly.

Past Master theses at University of Hildesheim
Past Master theses at University of Freiburg