Rising Stars in AI Symposium 2022
The Rising Stars in AI Symposium is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be tens of brief presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR.
All speakers will be encouraged to start with an intro for non-AI experts. Normal talks: 20 min per talk: 5 for broader intro, 15 for deeper stuff. There will be plenty of time for discussions and for social activities.
The symposium will be held in KAUST with (limited) in-person attendance. To register for the event, please fill out this registration form. Due to HSE restrictions, there is a limited number of available seats for in-person attendees in the Auditorium between Building 4 and 5. We will send confirmation emails for in-person attendance.
Agenda
Arabian Standard Time (UTC+3)
Day 1 - Sunday, March 13th, 2022
| Time | Speaker | Presentation |
|---|---|---|
| 09:00 - 09:20 | Prof. Juergen Schmidhuber, Dr. Tony Chan, Prof. Lawrence Carin |
Opening Remarks |
| 09:20 – 09:40 | Konstantin Burlachenko | FL_PyTorch: optimization research simulator for federated learning |
| 09:40 – 10:00 | Yazeed A. Alharbi | Towards interpretable and disentangled GANs |
| 10:00 – 10:20 | Juan Camilo Perez Santamaria | Certifying the Robustness of Face Recognition Against Semantic Perturbations |
| 10:20 – 11:00 | Coffee Networking Break | |
| 11:00 – 11:20 | Eduard Gorbunov | Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity |
| 11:20 – 11:40 | Guohao Li | Training Graph Neural Networks with 1000 Layers |
| 11:40 – 12:00 | Deyao Zhu | Motion Forecasting with Unlikelihood Training in Continuous Space |
| 12:00 – 13:20 | Lunch Break | |
| 13:20 – 13:40 | Dmitry Kovalev | Lower bounds and optimal algorithms for smooth and strongly convex decentralized optimization over time-varying networks |
| 13:40 – 14:00 | Mengmeng Xu | Low-Fidelity Video Encoder Pre-training for Temporal Action Localization |
| 14:00 – 14:20 | Mikhail Andronov | Overview of the methods for chemical reaction prediction |
| 14:20 – 15:00 | Coffee Networking Break | |
| 15:00 – 15:20 | Grigory Malinovsky | ProxSkip: Breaking the Communication Complexity Barrier of Local Gradient Methods |
| 15:20 – 15:40 | Merey Ramazanova | Ego4D: Around the World in 3,000 Hours of Egocentric Video |
| 15:40 – 16:00 | Michael Wand | Challenges in Real-world Applications of Neural Networks |
| 16:00 – 16:20 | Coffee Networking Break | |
| 16:20 – 17:00 | Panel Discussion: Current trends in AI | |
Day 2 - Monday, March 14th, 2022
| Time | Speaker | Presentation |
|---|---|---|
| 09:20 – 10:00 | Sebastian Stich | Keynote: Algorithms for Efficient Federated and Decentralized Learning |
| 10:00 – 10:20 | Motasem Alfarra | Network robustness from pixel perturbations to input transformations. |
| 10:20 – 10:40 | Alexander Tyurin | Permutation Compressors for Provably Faster Distributed Nonconvex Optimization |
| 10:40 – 11:00 | Coffee Networking Break | |
| 11:00 – 11:20 | Anastasia Koloskova | Decentralized optimization with heterogeneous data |
| 11:20 – 11:40 | Elnur Gasanov | FLIX: A simple and communication-efficient alternative to local methods in federated learning |
| 11:40 – 12:00 | Juan Carlos Alcazar | Active Speakers in Context |
| 12:00 – 13:20 | Lunch Break | |
| 13:20 – 13:40 | Mher Safaryan | FedNL: Making Newton-Type Methods Applicable to Federated Learning |
| 13:40 – 14:00 | Francesco Faccio | Parameter-based Value Functions |
| 14:00 – 14:20 | Robert Csordas | Principles of Compositionality Improve Systematic Generalization of Neural Networks |
| 14:20 – 15:00 | Coffee Networking Break | |
| 15:00 – 15:20 | Imanol Schlag | Fast Weight Architectures for Language Modeling and Associative Inference |
| 15:20 – 15:40 | Kazuki Irie | Training a Weight Matrix to Train/Modify/Adapt Itself |
| 15:40 – 16:00 | Louis Kirsch | Meta learning general-purpose learning algorithms |
| 16:00 – 16:20 | Coffee Networking Break | |
| 16:20 – 17:00 | Panel Discussion: Future trends in AI | |
Day 3 - Tuesday, March 15th, 2022
| Time | Speaker | Presentation |
|---|---|---|
| 09:00 – 09:40 | Bernard Ghanem | The AI Initiative at KAUST |
| 09:40 – 10:00 | Egor Shulgin | Shifted Compression Framework for Distributed Learning |
| 10:00 – 10:20 | Guocheng Qian | Make PointNet++ Faster and More Accurate |
| 10:20 – 11:00 | Coffee Networking Break | |
| 11:00 – 11:20 | Rameen Abdal | Extracting Semantics, Geometry, and Appearance using GANs |
| 11:20 – 11:40 | Samuel Horvath | FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout |
| 11:40 – 12:00 | Mattia Soldan | MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions |
| 12:00 – 13:20 | Lunch Break | |
| 13:20 – 13:40 | Huda A. Alamri | End-to-end joint training for video question-answering |
| 13:40 – 14:00 | Zhize Li | CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression |
| 14:00 – 14:20 | Dylan Ashley | Iterated Supervised Learning Methods for Solving Reinforcement Learning Problems |
| 14:20 – 15:00 | Coffee Networking Break | |
| 15:00 – 15:20 | Anna Fruehstueck | Improving Image Synthesis through specialized InsetGANs |
| 15:20 – 15:40 | Humam Alwassel | Self-Supervised Learning by Cross-Modal Audio-Video Clustering |
| 15:40 – 16:00 | Vincent Herrmann | Information theoretic concepts for the perception and creation of music |
| 16:00 – 16:20 | Concluding Remarks | |