Seminar in Distributed Computing (FS 2018)
As a seminar participant, you are invited to attend all the talks and make a presentation. Your presentation should be in English. The presentation should last 45 minutes plus about 15 minutes of discussion.
Presentation & Discussion
The seminar will be exciting if the presentations are exciting. Here is a 2 page guideline how to do a great scientific presentation. Here are some additional guidelines: 1, 2 and 3.
We further expect the presentation to motivate a lively discussion. We encourage discussions during and after the presentations as a main objective of this seminar. It may help discussions if you also try to be critical about the presented work. These are all scientific papers, but if you have been in science long enough...
Your grade will mostly depend on your presentation. In addition, we also grade how actively you participate in the discussions throughout the whole semester.
How To Sign Up
There will be one presentation per week, so there is a limited number of slots (topics) that can be presented (this year: 13). Therefore, we encourage you to contact Pankaj Khanchandani and the mentor corresponding to your favorite topic as early as possible to claim your presentation slot. Below we will have a series of suggested papers (or groups of papers) which will be assigned on a first-come-first-serve basis.
After You Signed UpWe established the following rules to ensure a high quality of the talks and hope that these will result in a good grade for you:
- At least 5 weeks before your talk: first meeting with your mentor (you need to read the assigned literature before this meeting).
- At least 3 weeks before your talk: meet your mentor to discuss the structure of your talk.
- At least 1 week before your talk: give the talk in front of your mentor who will provide feedback.
- At the presentation date we expect an electronic copy of your slides.
|20.02.2018||Cédric Stoll||FruitChains: A Fair Blockchain.||[pdf]|
|27.02.2018||Laurent Chuat||Consensus with Three Options.||[pdf]|
|06.03.2018||Simon Bienz||Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin.||[pdf]|
|13.03.2018||Jil Weber||A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities.||[pdf]|
|20.03.2018||Jakub Golinowski||How Effective Can Simple Ordinal Peer Grading Be?||[pdf]|
|27.03.2018||Ethan Rambacher||Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false).||[pdf]|
|10.04.2018||Linard Arquint||Acoustic Communication||[pdf]|
|17.04.2018||Mickey Vänskä||Recoverable FCFS Mutual Exclusion with Wait-Free Recovery.||[pdf]|
|08.05.2018||Damien Aymon||Election vs. Selection: How Much Advice is Needed to Find the Largest Node in a Graph?||[pdf]|
|15.05.2018||Matthias Lei||Computing Equilibrium in Matching Markets.||[pptx]|
|22.05.2018||Gian-Marco Hutter||Hierarchical Reinforcement Learning.|
|29.05.2018||André Baptista Aguas||Stable Matching with Evolving Preferences.||[pptx]|
Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false).
A. Backurs & P. Indyk. STOC 2015.
|Ethan Rambacher||Pál András Papp|
Election vs. Selection: How Much Advice is Needed to Find the Largest Node in a Graph?.
A. Miller and A. Pelc. SPAA 2016.
|Damien Aymon||Pál András Papp|
How Effective Can Simple Ordinal Peer Grading Be?
Ioannis Caragiannis, George A. Krimpas, Alexandros A. Voudouris. EC 2016.
|Jakub Golinowski||Darya Melnyk|
Efficient voting via the top-k elicitation scheme: a probabilistic approach.
Yuval Filmus, Joel Oren. EC 2014.
Consensus with three options:
Stabilizing consensus with many opinions.
L. Becchetti, A. Clementi, E. Natale, F. Pasquale, L. Trevisan. SODA 2016.
Fast plurality consensus in regular expanders.
Colin Cooper, Tomasz Radzik, Nicolas Rivera, Takeharu Shiraga. DISC 2017.
|Laurent Chuat||Darya Melnyk|
FruitChains: A Fair Blockchain.
Rafael Pass, Elain Shi. PODC 2017.
|Cédric Stoll||Georgia Avarikioti|
Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin.
Y. Kwon, D. Kim, Y. Son, E. Vasserman, Y. Kim. CCS 2017.
|Simon Bienz||Georgia Avarikioti|
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos.
Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras. NIPS 2017.
Stable Matching with Evolving Preferences.
Varun Kanade, Nikos Leonardos, Frédéric Magniez. APPROX/RANDOM 2016.
|André Baptista Aguas||Julian Steger|
A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities.
Michael Elkin. PODC 2017.
|Jil Weber||Pankaj Khanchandani|
Recoverable FCFS Mutual Exclusion with Wait-Free Recovery.
Prasad Jayanti and Anup Joshi. DISC 2017.
|Mickey Vänskä||Pankaj Khanchandani|
BackDoor: Making Microphones Hear Inaudible Sounds.
N. Roy, H. Hassanieh, R. Choudhury. MobiSys 2017.
Messages Behind the Sound: Real-Time Hidden Acoustic Signal Capture with Smartphones.
Q. Wang, K. Ren, m.Zhou, T.Lei, D. Koutsonikolas, L. Su. MobiCom 2016.
Dhwani: secure peer-to-peer acoustic NFC.
R. Nandakumar, K. Chintalapudi, V. Padmanabhan, R. Venkatesan. SIGCOMM 2013.
|Linard Arquint||Simon Tanner|
3D Through-Wall Imaging with Unmanned Aerial Vehicles Using WiFi.
C. Karanam, Y. Mostofi. IPSN 2017.
Position and Orientation Agnostic Gesture Recognition Using WiFi.
A. Virmani, M. Shahzad. MobiSys 2017.
Hierarchical Reinforcement Learning:
The Option-Critic Architecture.
P. Bacon, J. Harb, D. Precup. AAAI 2017.
Stochastic Neural Networks for Hierarchical Reinforcement Learning.
C. Florensa, Y. Duan, P. Abbeel. ICLR 2017.
Meta Learning Shared Hierarchies.
K. Frans, J. Ho, X. Chen, P. Abbeel, J. Schulman. ICLR 2018.
|Gian-Marco Hutter||Oliver Richter|
G. Hinton, A. Krizhevsky, S. Wang. ICANN 2011.
Dynamic Routings Between Capsules.
S. Sabour, N. Frosst, G. Hinton. NIPS 2017.
Matrix Capsules with EM Routing.
(under review). ICLR 2018.
Position Tracking for Virtual Reality Using Commodity WiFi
M. Kotaru, S. Katti. CVPR 2017.
Real-time Full-Body Motion Capture from Video and IMUs
C. Malleson et al. 3DV 2017.
A Low Power, Fully Event-Based Gesture Recognition System
A. Amir et al. CVPR 2017.
Composing only by thought: Novel application of the P300 brain-computer interface
A. Pinegger A, H. Hiebel, S.C. Wriessnegger, G.R. Müller-Putz. PLoS ONE 2017.
A cryptography-based approach for movement decoding
E.L. Dyer et al. Nature Biomedical Engineering 2017.
Generative Adversarial Networks (GANs) for Music Generation:
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford et al. ICLR 2016.
MidiNet: A convolutional generative adversarial network for symbolic-domain music generation.
Yang et al. ISMIR 2017.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment.
Dong et al. AAAI 2018.
Learning Disentangled Latent Features in Deep Generative Models:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
Chen et al. NIPS 2016.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.
Higgins et al. ICLR 2017.
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations.
Bouchacourt et al. AAAI 2018.
Computing Equilibrium in Matching Markets.
S. alaei et al. EC 2017.
|Matthias Lei||Yuyi Wang|
Dynamic Mechanisms with Martingale Utilities.
S. Balseiro. EC 2017.
Online Learning Without Prior Information.
A. Cutkosky and K. Boahen. COLT 2017.
Depth Separation for Neural Networks.
A. Daniely. COLT 2017.
Online Auctions and Multi-scale Online Learning.
S. Bubeck et al. EC 2017.
Matching while Learning.
R. Johari et al. EC 2017.