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Found 7 papers, 4 papers with code
Optimal Differentially Private Model Training with Public Data
We show that the optimal error rates can be attained (up to log factors) by either discarding private data and training a public model, or treating public data like it is private and using an optimal DP algorithm.
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
Attribution methods address the issue of explainability by quantifying the importance of an input feature for a model prediction.
Robustness through Data Augmentation Loss Consistency
Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.
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Multi-domain Dialogue State Tracking
on MULTIWOZ 2.2
Multi-domain Dialogue State Tracking
Visual Question Answering
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs).
Alternating Direction Method of Multipliers for Quantization
For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for Quantization ($\texttt{ADMM-Q}$) algorithm, which is a variant of the widely-used ADMM method applied to our discrete optimization problem.
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games.
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
In this paper, we study the problem in the non-convex regime and show that an \varepsilon--first order stationary point of the game can be computed when one of the player's objective can be optimized to global optimality efficiently.
