no code implementations • 1 Nov 2022 • Yifei Wang, Tavor Baharav, Yanjun Han, Jiantao Jiao, David Tse
In the infinite-armed bandit problem, each arm's average reward is sampled from an unknown distribution, and each arm can be sampled further to obtain noisy estimates of the average reward of that arm.
no code implementations • 18 Mar 2022 • Tavor Z. Baharav, Gary Cheng, Mert Pilanci, David Tse
We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$.
no code implementations • 18 Jun 2021 • Farzan Farnia, Amirali Aghazadeh, James Zou, David Tse
Robust training methods against perturbations to the input data have received great attention in the machine learning literature.
2 code implementations • 10 Sep 2020 • Joachim Neu, Ertem Nusret Tas, David Tse
To resolve this availability-finality dilemma, we formulate a new class of flexible consensus protocols, ebb-and-flow protocols, which support a full dynamically available ledger in conjunction with a finalized prefix ledger.
Cryptography and Security Distributed, Parallel, and Cluster Computing
1 code implementation • NeurIPS 2019 • Tavor Baharav, David Tse
Four to five orders of magnitude gains over exact computation are obtained on real data, in terms of both number of distance computations needed and wall clock time.
1 code implementation • 4 Oct 2019 • Vivek Bagaria, Joachim Neu, David Tse
Funds are forwarded using Boomerang contracts, which allow Alice to revert the transfer iff she has learned Bob's secret.
Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Networking and Internet Architecture Information Theory
2 code implementations • 25 Sep 2019 • Lei Yang, Vivek Bagaria, Gerui Wang, Mohammad Alizadeh, David Tse, Giulia Fanti, Pramod Viswanath
Bitcoin is the first fully-decentralized permissionless blockchain protocol to achieve a high level of security, but at the expense of poor throughput and latency.
Distributed, Parallel, and Cluster Computing Cryptography and Security Networking and Internet Architecture
no code implementations • 27 Jan 2019 • Banghua Zhu, Jiantao Jiao, David Tse
Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance, to design GANs to meet the population requirement using finite samples.
no code implementations • NeurIPS 2018 • Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Neural networks have been used prominently in several machine learning and statistics applications.
1 code implementation • ICLR 2019 • Farzan Farnia, Jesse M. Zhang, David Tse
A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training.
no code implementations • NeurIPS 2018 • Farzan Farnia, David Tse
For a convex set $\mathcal{F}$, this duality framework interprets the original GAN formulation as finding the generative model with minimum JS-divergence to the distributions penalized to match the moments of the data distribution, with the moments specified by the discriminators in $\mathcal{F}$.
no code implementations • 15 Apr 2018 • Vivek Bagaria, Jian Ding, David Tse, Yihong Wu, Jiaming Xu
Represented as bicolored multi-graphs, these extreme points are further decomposed into simpler "blossom-type" structures for the large deviation analysis and counting arguments.
no code implementations • ICLR 2018 • Farzan Farnia, Jesse Zhang, David Tse
The recent success of deep neural networks stems from their ability to generalize well on real data; however, Zhang et al. have observed that neural networks can easily overfit random labels.
1 code implementation • NeurIPS 2017 • Soheil Feizi, Hamid Javadi, David Tse
Consider a dataset where data is collected on multiple features of multiple individuals over multiple times.
1 code implementation • NeurIPS 2017 • Fei Xia, Martin J. Zhang, James Zou, David Tse
For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait.
1 code implementation • 2 Nov 2017 • Vivek Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse
Computing the medoid of a large number of points in high-dimensional space is an increasingly common operation in many data science problems.
no code implementations • ICLR 2018 • Soheil Feizi, Farzan Farnia, Tony Ginart, David Tse
Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data.
1 code implementation • 5 Oct 2017 • Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Neural networks have been used prominently in several machine learning and statistics applications.
no code implementations • 28 Apr 2017 • Daniel Russo, David Tse, Benjamin Van Roy
We propose satisficing Thompson sampling -- a variation of Thompson sampling -- and establish a strong discounted regret bound for this new algorithm.
no code implementations • 17 Feb 2017 • Soheil Feizi, David Tse
For jointly Gaussian variables we show that the covariance matrix corresponding to the identity (or the negative of the identity) transformations majorizes covariance matrices of non-identity functions.
1 code implementation • NeurIPS 2016 • Farzan Farnia, David Tse
Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $\Gamma$ on $(X, Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $\Gamma$?
no code implementations • 11 Feb 2016 • Yuxin Chen, Govinda Kamath, Changho Suh, David Tse
Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality.
no code implementations • NeurIPS 2015 • Meisam Razaviyayn, Farzan Farnia, David Tse
We prove that for a given set of marginals, the minimum Hirschfeld-Gebelein-Renyi (HGR) correlation principle introduced in [1] leads to a randomized classification rule which is shown to have a misclassification rate no larger than twice the misclassification rate of the optimal classifier.
no code implementations • 15 Aug 2012 • Bin Li, Hui Shen, David Tse
In this letter, we propose an adaptive SC (Successive Cancellation)-List decoder for polar codes with CRC.
Information Theory Information Theory