ADSI Working Group
Welcome to the page of the ADSI Working Group. If you wish to present at the group, please contact Vincent Roulet.
Meeting time: Fridays at 1:30pm
Meeting location: Variable (location is announced on the mailing list)
Organizers: Zaid Harchaoui,
Sham Kakade, Maryam Fazel, Kevin Jamieson, John Thickstun
Mailing List: https://mailman.cs.washington.edu/mailman/listinfo/ml-opt
Quarter: Fall 2019
11/15/2019
Mitas Ray, UW
Welfare maximization with production costs: A primal dual approach
11/8/2019
Yue Sun, UW
Escaping saddle on manifold
11/1/2019
Aravind Rajeswaran, UW
Meta Learning with Implicit Gradients. FTML iMAML
10/25/2019
Alec Greaves-Tunnell, UW
Optimal spectral transportation with applications to music transcription
10/11/2019
Zhaoqi Li, UW
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
10/4/2019
Dmitriy Drusvyatskiy, UW
Robust Stochastic Optimization with the Proximal Point Method
9/27/2019
John Thickstun, UW
Deep Learning with Logged Bandit Feedback
Quarter: Spring 2019
6/7/2019
Omid Sadeghi, UW
Controlling Discrimination in Online Auctions
5/31/2019
John Thickstun, UW
Transformer Networks
5/17/2019
Andrew Wagenmaker, UW
Temporal Difference Learning
5/10/2019
Alec Greaves-Tunnell, UW
Long Memory Processes
5/3/2019
Vincent Roulet, UW
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
4/26/2019
Ross Boczar, Berkeley
Learning to Control in Finite Time
4/19/2019
Greg Yang, Microsoft
Batch Normalization Causes Gradient Explosion
Quarter: Winter 2019
3/15/2019
Omid Sadeghi, UW
Online convex optimization for cumulative constraints
3/8/2019
Lillian Ratliff, UW
Learning in Non-Stationary Environments: Near-Optimal Guarantees
3/1/2019
Corinne Jones, UW
Deriving Neural Architectures from Kernels
2/22/2019
Ramya Vinayak, UW
MLE for Learning Populations of Parameters
2/15/2019
Lang Liu, UW
Optimization Guarantees for Overparameterized Nets
2/1/2019
Krishna Pillutla, UW
Localized Structured Prediction
Quarter: Fall 2018
11/30/2018
Gang Cheng, UW
Certificates for approximately correct clustering
11/16/2018
John Thickstun, UW
Generative Modeling with Sinkhorn Divergences
11/2/2018
Yue Sun, UW
Learning Linear Dynamical Systems
10/26/2018
Aravind Rajeswaran, UW
TBA
10/12/2018
Ashok Vardhan, UIUC
Globally Consistent Algorithms for Mixture of Experts
10/5/2018
Romain Camilleri, UW
Imitation Learning and DAGGER
Quarter: Summer 2018
7/20/2018
Weihao Kong, Stanford
Estimating unexplained variance and learnability in the sublinear data regime
Quarter: Spring 2018
6/8/2018
Vincent Roulet, UW
Grouping Features for Prediction
6/1/2018
Krishna Pillutla, UW
Regularization for Structured Prediction
5/25/2018
Fabian Pedregosa, Berkeley
Asynchronous Stochastic Optimization
5/11/2018
Mathurin Massias, Télécom ParisTech
Celer: dual extrapolation for faster Lasso solvers
5/4/2018
Omid Sadeghi, UW
Fast Algorithms for Online Stochastic Convex Programming
4/27/2018
Rahul Kidambi, UW
Evaluating predictions on selectively labeled data
4/20/2018
Nadav Cohen, Princeton
Implicit Acceleration by Overparameterization
4/6/2018
Dmitriy Drusvyatskiy, UW
SGD rate for weakly convex functions
3/30/2018
Chi Jin, Berkeley
Nonconvex optimization: acceleration, saddle points, and shallow minima
Quarter: Winter 2018
1/26/2018
Jennifer Rogers, Aravind Rajeswaran, UW
Adversarial Examples and Robust Optimization
2/16/2018
Krishna Pillutla, UW
Consistency for Structured Prediction
1/26/2018
Ramya Vinayak, UW
Learning Populations of Parameters
1/19/2018
Alec Greaves-Tunnell, UW
Learning Overcomplete HMMs
1/12/2018
Yue Sun, UW
Accelerated gradient methods on non-convex loss surfaces
1/5/2018
Jerry Li, MIT
Robust Estimation
Quarter: Fall 2017
11/17/2017
Swati Padmanabhan, UW
Deterministic Approximate Caratheodory
11/3/2017
Krishna Pillutla, UW
Dualing GANs
10/27/2017
Jennifer Rogers, UW
Intelligible Models (paper1 ,
paper2)
10/13/2017
Aravind Rajeswaran, UW
Model Agnostic Meta Learning
10/6/2017
Chris Xie, UW
Visual Object Tracking