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Found 12 papers, 4 papers with code
Optimistic and Adaptive Lagrangian Hedging
The generality of this framework includes problems that are not adversarial, for example offline optimization, or saddle point problems (i. e. min max optimization).
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
Maximum Entropy Monte-Carlo Planning
We then extend this approach to general sequential decision making by developing a general MCTS algorithm, Maximum Entropy for Tree Search (MENTS).
On the Sensitivity of Adversarial Robustness to Input Data Distributions
Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
We demonstrate the superiority of our method to the previous ones in two different continual learning settings on popular benchmarks, as well as a new continual learning problem where tasks are designed to be more dissimilar.
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view.
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs).
Structured Best Arm Identification with Fixed Confidence
We study the problem of identifying the best action among a set of possible options when the value of each action is given by a mapping from a number of noisy micro-observables in the so-called fixed confidence setting.
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
The follow the leader (FTL) algorithm, perhaps the simplest of all online learning algorithms, is known to perform well when the loss functions it is used on are convex and positively curved.
Revise Saturated Activation Functions
In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh).
Learning with a Strong Adversary
The robustness of neural networks to intended perturbations has recently attracted significant attention.
