In this paper, we consider the task of learning control policies for text-based games.In these games, all interactions in virtual world are through text and underlying state is not observed.The resulting language barrier makes such environments challenging automatic game players.We employ a deep reinforcement framework to jointly learn representations action using rewards as feedback.This enables us map descriptions into vector that capture semantics states.We evaluate our approach on two...

Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft. This tutorial aims introduce key technical underpinnings these deployed systems, survey current research that addresses related problems within LDP model, identify relevant open directions community.

Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for data subjects. Applications these range from finding correlations in to fitting sophisticated prediction models. In this paper, we provide a set algorithms materializing under model local differential privacy. We prove first tight theoretical bounds accuracy marginals compiled each approach, perform empirical evaluation confirm...

Advances in deep generative networks have led to impressive results recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due weak inductive biases decoders. This is where graphics engines may come handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine learning renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts supervision,...

Counting the fraction of a population having an input within specified interval i.e. range query, is fundamental data analysis primitive. Range queries can also be used to compute other core statistics such as quantiles, and build prediction models. However, frequently subject privacy concerns when it drawn from individuals, relates for example their financial, health, religious or political status. In this paper, we introduce analyze methods support under local variant differential [23],...

The study of object representations in computer vision has primarily focused on developing that are useful for image classification, detection, or semantic segmentation as downstream tasks. In this work we aim to learn control and reinforcement learning (RL). To end, introduce Transporter, a neural network architecture discovering concise geometric terms keypoints image-space coordinates. Our method learns from raw video frames fully unsupervised manner, by transporting learnt features...

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present unsupervised algorithm train agents achieve perceptually-specified goals using only a stream observations actions. Our agent simultaneously learns goal-conditioned policy goal achievement reward function that measures how similar state state. This dual optimization leads co-operative game, giving rise learned reflects...

We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A agent controls a simulated painting environment, and is trained with rewards provided by discriminator network simultaneously to assess the realism agent's samples, either unconditional or reconstructions. Compared prior work, we make number improvements architectures discriminators that lead intriguing at times surprising results. find when sufficiently constrained, can learn...

Counting the fraction of a population having an input within specified interval i.e. range count query is fundamental database operation. Range queries can also be used to compute other interesting statistics such as quantiles. The framework differential privacy [6] (DP) becoming standard for privacy-preserving data analysis [1]. While many works address problem counting in trusted aggregation model, surprisingly, this has not been addressed specifically under untrusted (local DP [10]). In...

We introduce a neural network architecture and learning algorithm to produce factorized symbolic representations. propose learn these concepts by observing consecutive frames, letting all the components of hidden representation except small discrete set (gating units) be predicted from previous frame, let factors variation in next frame represented entirely gated units (corresponding representations). demonstrate efficacy our approach on datasets faces undergoing 3D transformations Atari 2600 games.

For a considerable time, individuals have been searching for the most effective way to secretly transmit information, making it challenging anyone without authorization decode it. Steganography is method that conceals confidential information from unauthorized users. The message encrypted, allowing only intended recipient access message. In case of image steganography, hidden within cover image. One technique involves modifying image, so receiver can still even though not visible human eye....

We present an evaluation of text entry methods for tabletop displays given small display space allocations, increasingly important design constraint as tabletops become collaborative platforms. Small is already a requirement mobile methods, and these can often be easily ported to settings. The purpose this work determine whether are equally useful displays, or there unique aspects on large, horizontal surfaces that influence design. Our consists two studies designed elicit differences...

We investigate the problem of electing a leader in sparse but well-connected synchronous dynamic network which up to fraction nodes chosen adversarially can leave/join per time step. At this churn rate, all be replaced by new constant number rounds. Moreover, adversary shield (which may include leader) repeatedly churning their neighbourhood and thus hinder communication with rest network. However, empirical studies peer-to-peer networks have shown that significant are usually stable well...

We consider the problem of electing a leader among nodes in highly dynamic network where adversary has unbounded capacity to insert and remove (including leader) from change connectivity at will. present randomized Las Vegas algorithm that (re)elects O(D\log n) rounds with high probability, D is bound on diameter n maximum number any point time. assume model broadcast-based communication node can send only 1 message O(\log bits per round not aware receivers advance. Thus, our results also...

Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier greater availability of data. Differential has emerged as an accepted model release information while giving statistical guarantee for privacy. Many different algorithms are possible address target functions. We focus on the core problem count queries, and seek design mechanisms associated with group n individuals. Prior work focused designing by raw optimization loss function,...

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important understand how the will used by agent and what properties good should have. paper we systematically evaluate number of common learnt hand-engineered context three robotics...

Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier greater availability of data. Differential has emerged as an accepted model release information while giving statistical guarantee for privacy. Many different algorithms are possible address target functions. We focus on the core problem count queries, and seek design mechanisms associated with group n individuals. Prior work focused designing by raw optimization loss function,...

In this paper, we consider the task of learning control policies for text-based games. these games, all interactions in virtual world are through text and underlying state is not observed. The resulting language barrier makes such environments challenging automatic game players. We employ a deep reinforcement framework to jointly learn representations action using rewards as feedback. This enables us map descriptions into vector that capture semantics states. evaluate our approach on two...