Search Results for author:
Found 14 papers, 5 papers with code
Gaussian Differential Privacy on Riemannian Manifolds
We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds.
Class Interference of Deep Neural Networks
Recognizing and telling similar objects apart is even hard for human beings.
Conformalized Fairness via Quantile Regression
To fulfill great needs and advocate the significance of quantile fairness, we propose a novel framework to learn a real-valued quantile function under the fairness requirement of Demographic Parity with respect to sensitive attributes, such as race or gender, and thereby derive a reliable fair prediction interval.
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?
We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL.
Distributional Reinforcement Learning
reinforcement-learning
+1
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space.
Distributional Reinforcement Learning by Sinkhorn Divergence
The empirical success of distributional reinforcement learning~(RL) highly depends on the distribution representation and the choice of distribution divergence.
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings.
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning
The theoretical advantages of distributional reinforcement learning~(RL) over classical RL remain elusive despite its remarkable empirical performance.
Gaussian Differential Privacy Transformation: from identification to application
Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of individuals from machine learning models.
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm
Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation.
Meta-HAR: Federated Representation Learning for Human Activity Recognition
However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type.
Similarity Embedding Networks for Robust Human Activity Recognition
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently.
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification
In deep neural network, the cross-entropy loss function is commonly used for classification.
