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Following [OW16], we continue our analysis of: (1) "Quantum tomography", i.e., learning a quantum state, i.e., the quantum generalization of learning a discrete probability distribution; (2) The distribution of Young diagrams output by the…

Quantum Physics · Physics 2016-12-02 Ryan O'Donnell , John Wright

Spinal codes are a type of capacity-achieving rateless codes that have been proved to approach the Shannon capacity over the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC). In this paper, we aim to…

Information Theory · Computer Science 2022-04-05 Aimin Li , Shaohua Wu , Jian Jiao , Ning Zhang , Qinyu Zhang

The capacity under strong asynchronism was recently shown to be essentially unaffected by the imposed output sampling rate $\rho$ and decoding delay $d$---the elapsed time between when information is available at the transmitter and when it…

Information Theory · Computer Science 2017-10-20 Longguang Li , Aslan Tchamkerten

We develop Second Order Asymptotical Regularization (SOAR) methods for solving inverse source problems in elliptic partial differential equations with both Dirichlet and Neumann boundary data. We show the convergence results of SOAR with…

Numerical Analysis · Mathematics 2019-01-23 Ye Zhang , Rongfang Gong

In this work we develop a scalable computational framework for the solution of PDE-constrained optimal control under high-dimensional uncertainty. Specifically, we consider a mean-variance formulation of the control objective and employ a…

Optimization and Control · Mathematics 2019-03-27 Peng Chen , Umberto Villa , Omar Ghattas

The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs,…

Information Theory · Computer Science 2020-07-15 Lampros Gavalakis , Ioannis Kontoyiannis

Can we analyze data without decompressing it? As our data keeps growing, understanding the time complexity of problems on compressed inputs, rather than in convenient uncompressed forms, becomes more and more relevant. Suppose we are given…

Computational Complexity · Computer Science 2018-03-05 Amir Abboud , Arturs Backurs , Karl Bringmann , Marvin Künnemann

This paper establishes the exact strong converse exponent of the soft covering problem in the classical setting. This exponent characterizes the slowest achievable convergence speed of the total variation to one when a code of rate below…

Information Theory · Computer Science 2026-04-01 Xingyi He , S. Sandeep Pradhan , Andreas Winter

Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum likelihood rule, which is NP-hard due to the curse of…

Information Theory · Computer Science 2021-02-22 Nir Raviv , Avi Caciularu , Tomer Raviv , Jacob Goldberger , Yair Be'ery

We revisit the outlier hypothesis testing framework of Li \emph{et al.} (TIT 2014) and derive fundamental limits for the optimal test under the generalized Neyman-Pearson criterion. In outlier hypothesis testing, one is given multiple…

Information Theory · Computer Science 2022-02-15 Lin Zhou , Yun Wei , Alfred Hero

This paper studies the secrecy rate maximization problem of a secure wireless communication system, in the presence of multiple eavesdroppers. The security of the communication link is enhanced through cooperative jamming, with the help of…

Information Theory · Computer Science 2017-01-13 Kanapathippillai Cumanan , George C. Alexandropoulos , Zhgiuo Ding , George K. Karagiannidis

This work analyzes the asymptotic performances of fully distributed sequential hypothesis testing procedures as the type-I and type-II error rates approach zero, in the context of a sensor network without a fusion center. In particular, the…

Applications · Statistics 2018-04-17 Shang Li , Xiaodong Wang

The paper derives the optimal second-order coding rate for the continuous-time Poisson channel. We also obtain bounds on the third-order coding rate. This is the first instance of a second-order result for a continuous-time channel. The…

Information Theory · Computer Science 2020-08-18 Yuta Sakai , Vincent Y. F. Tan , Mladen Kovačević

This paper studies distributionally robust chance constrained programs (DRCCPs), where the uncertain constraints must be satisfied with at least a probability of a prespecified threshold for all probability distributions from the…

Optimization and Control · Mathematics 2023-02-06 Nan Jiang , Weijun Xie

In this paper, we establish an initial theory regarding the Second Order Asymptotical Regularization (SOAR) method for the stable approximate solution of ill-posed linear operator equations in Hilbert spaces, which are models for linear…

Numerical Analysis · Mathematics 2018-08-28 Ye Zhang , Bernd Hofmann

Consider a receiver in a multi-user network that wishes to decode several messages. Simultaneous joint typicality decoding is one of the most powerful techniques for determining the fundamental limits at which reliable decoding is possible.…

Information Theory · Computer Science 2019-01-11 Sung Hoon Lim , Chen Feng , Adriano Pastore , Bobak Nazer , Michael Gastpar

Shannon's analysis of the fundamental capacity limits for memoryless communication channels has been refined over time. In this paper, the maximum volume $M_\avg^*(n,\epsilon)$ of length-$n$ codes subject to an average decoding error…

Information Theory · Computer Science 2016-12-28 Pierre Moulin

When information is to be transmitted over an unknown, possibly unreliable channel, an erasure option at the decoder is desirable. Using constant-composition random codes, we propose a generalization of Csiszar and Korner's Maximum Mutual…

Information Theory · Computer Science 2016-11-17 Pierre Moulin

We consider the problem of slotted asynchronous coded communication, where in each time frame (slot), the transmitter is either silent or transmits a codeword from a given (randomly selected) codebook. The task of the decoder is to decide…

Information Theory · Computer Science 2013-08-22 Neri Merhav

We consider the following basic learning task: given independent draws from an unknown distribution over a discrete support, output an approximation of the distribution that is as accurate as possible in $\ell_1$ distance (i.e. total…

Machine Learning · Computer Science 2015-11-12 Gregory Valiant , Paul Valiant