84 citations · 247 across the 23 of their papers we have counts for
9 papers · 1 filter
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically…
Proof-of-Stake Longest Chain Protocols: Security vs Predictability
Vivek Bagaria, Amir Dembo, Sreeram Kannan +5
The Nakamoto longest chain protocol is remarkably simple and has been proven to provide security against any adversary with less than 50% of the total hashing power. Proof-of-stake…
Barracuda: The Power of -polling in Proof-of-Stake Blockchains
Giulia Fanti, Jiantao Jiao, Ashok Makkuva +3
A blockchain is a database of sequential events that is maintained by a distributed group of nodes. A key consensus problem in blockchains is that of determining the next block (da…
Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Weizhao Tang, Weina Wang, Giulia Fanti +1
Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a us…
Optimal transport mapping via input convex neural networks
Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh +1
In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn t…
Robust conditional GANs under missing or uncertain labels
Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan
Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains. We design a new trai…
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti +1
Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct…
Learning in Gated Neural Networks
Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan +1
Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural networks. The backbone of such gated networks is a mixture-of-experts layer,…
Minimax Rates of Estimating Approximate Differential Privacy
Xiyang Liu, Sewoong Oh
Differential privacy has become a widely accepted notion of privacy, leading to the introduction and deployment of numerous privatization mechanisms. However, ensuring the privacy…