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Found 13 papers, 4 papers with code
Practical Differentially Private Hyperparameter Tuning with Subsampling
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values.
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustworthy.
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Differentially Private Bayesian Inference for Generalized Linear Models
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets.
Private Protocols for U-Statistics in the Local Model and Beyond
In this paper, we study the problem of computing $U$-statistics of degree $2$, i. e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP).
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804. 01118).
Unsupervised Learning of Object Keypoints for Perception and Control
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
Exploration in environments with sparse rewards is a key challenge for reinforcement learning.
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.
Synthesizing Programs for Images using Reinforced Adversarial Learning
Advances in deep generative networks have led to impressive results in recent years.
Understanding Visual Concepts with Continuation Learning
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations.
Language Understanding for Text-based Games Using Deep Reinforcement Learning
We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations.
