Schedule at a Glance
The schedule will continue to be updated as we get closer to the workshop.
Day 1 - Tuesday, October 5th (PST)
8:30 AM - Welcome/Intro + UR overview - Vahab Mirrokni
8:40 AM - Keynonte - Sanjoy Dasgupta
Some Directions in Explainable Unsupervised Learning
9:02 AM - Talk - Badih Ghazi
Differentially Private Clustering: Tight Approximation Ratios
9:14 AM - Talk - Vincent Cohen-Addad
A New Framework For K-means Coresets
9:26 AM - Talk - Laxman Dhulipala
ParHAC: Single-Machine Parallel Hierarchical Clustering of Billion-Edge Graphsactice
9:38 AM - Talk - David Woodruff
Optimal Stochastic Trace Estimation
9:50 AM - Talk - Umar Syed
Label differential privacy via clustering
10:02 AM - Talk - Corinna Cortes
10:07 AM - Break
10:17 AM - Keynote - Ronitt Rubinfeld
Locality in computation
10:40 AM - Talk - Andreas Krause
Data Summarization via Bilevel Coresets
10:52 AM - Talk - Amin Karbasi
Batch Active Learning
11:04 AM - Talk - Piotr Indyk
Learning-Based Sampling for Distinct Elements Counting
11:16 AM - Talk - MohammadHosein Bateni
Submodular Optimization for Machine Learning
11:28 AM - Talk - Nicholas Carlini
Data Poisoning in {Semi,Self}-Supervised Learning
11:40 AM - Closing Remarks - Alessandro Epasto
11:50 AM - Break
12:00 PM - Poster Session
12:30 PM - END
Day 2 - Wednesday, October 6th (PST)
8:30 AM - Welcome - Vahab Mirrokni
8:40 AM - Keynonte - Maria-Florina Balcan
Data driven algorithm design
9:02 AM - Talk - Christian Schulz
Recent Advances in Scalable Graph Decomposition
9:14 AM - Talk - Richard Peng
Fully Dynamic Effective Resistance
9:26 AM - Talk - Piotr Sankowski
Walking Randomly, Massively, and Efficiently
9:38 AM - Talk - Jakub "Kuba" Łącki
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice
9:50 AM - Talk - Peilin Zhong
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering
10:02 AM - Break
10:17 AM - Keynote - Julian Shun
Parallel Index-Based Structural Graph Clustering and Its Approximation
10:40 AM - Talk - David Gleich
Recent insights from flow and diffusion-based semi-supervised learning problems
10:52 AM - Talk - Silvio Lattanzi
Semi-supervised Clustering
11:04 AM - Talk - Ali Sinop
Robust Routing Using Electrical Flows
11:16 AM - Talk - Aneesh Sharma
Graph Embeddings and Graph Structure
11:28 AM - Talk - Jean Pouget-Abadi
Graphs and Causal Inference
11:40 AM - Closing Remarks - Alessandro Epasto
11:50 AM - Break
11:55 AM - Poster Session
12:30 PM - END
Day 3 - Thursday, October 7th (PST)
8:30 AM - Welcome - Alessandro Epasto
8:40 AM - Keynonte - Stefanie Jegelka
Extrapolation in Graph Neural Networks
9:02 AM - Talk - Leman Akoglu
Distributed Outlier Detection at Scale
9:14 AM - Talk - Danai Koutra
The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability
9:26 AM - Talk - Andreas Loukas
Erdős goes neural: solving combinatorial optimization problems with neural networks and no supervision
9:38 AM - Talk - Rina Panigrahy
Sketch based Nerual Memory for Deep Networks
9:50 AM - Talk - Marinka Zitnik
Few-Shot Learning for Network Biomedicine
10:02 AM - Break
10:17 AM - Keynote - Zoubin Ghahramani
Semi-supervised learning: graph-based methods, probabilistic approaches and deep learning
10:40 AM - Talk - Bryan Perozzi
Graph Neural Networks at Google
10:52 AM - Talk - Prateek Jain
Node-Level Differentially Private Graph Neural Networks
11:04 AM - Talk - Dilip Krishnan
Contrastive Representation Learning
11:16 AM - Talk - Srinadh Bhojanapalli
Efficient transformers with less redundancy in attention computation
11:28 AM - Talk - Pranjal Awasthi
Beyond GNNs : A Sample-Efficient Architecture for Graph Problems
11:40 AM - Closing Remarks - Vahab Mirrokni
11:50 AM - Break
11:55 AM - Poster Session
12:30 PM - END