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Related papers: Oblivious Deletion Codes

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Noisy channels are a powerful resource for cryptography as they can be used to obtain information-theoretically secure key agreement, commitment and oblivious transfer protocols, among others. Oblivious transfer (OT) is a fundamental…

Cryptography and Security · Computer Science 2014-10-13 Rafael Dowsley , Anderson C. A. Nascimento

We present error-correcting codes that achieve the information-theoretically best possible trade-off between the rate and error-correction radius. Specifically, for every $0 < R < 1$ and $\eps> 0$, we present an explicit construction of…

Information Theory · Computer Science 2007-10-08 Venkatesan Guruswami , Atri Rudra

In this paper, we discuss a construction method of quantum deletion error-correcting codes. First of all, we define deletion errors for quantum states, an encoder, a decoder, and two conditions which is expressed by only the combinatorial…

Quantum Physics · Physics 2020-04-03 Ayumu Nakayama , Manabu Hagiwara

We introduce and initiate the study of a new model of reductions called the random noise model. In this model, the truth table $T_f$ of the function $f$ is corrupted on a randomly chosen $\delta$-fraction of instances. A randomized…

Computational Complexity · Computer Science 2025-09-09 Tejas Nareddy , Abhishek Mishra

Concept erasure aims to remove unwanted attributes, such as social or demographic factors, from learned representations, while preserving their task-relevant utility. While the goal of concept erasure is protection against all adversaries,…

Machine Learning · Computer Science 2026-04-30 Ramin Akbari , Milad Afshari , Vishnu Naresh Boddeti

The problem of correcting deletions and insertions has recently received significantly increased attention due to the DNA-based data storage technology, which suffers from deletions and insertions with extremely high probability. In this…

Information Theory · Computer Science 2023-11-09 Tuan Thanh Nguyen , Kui Cai , Paul H. Siegel

Neural networks have demonstrated state-of-the-art performance in various machine learning fields. However, the introduction of malicious perturbations in input data, known as adversarial examples, has been shown to deceive neural network…

Machine Learning · Computer Science 2023-02-06 Dennis Y. Menn , Tzu-hsun Feng , Hung-yi Lee

Recent work by Smagloy et al. (ISIT 2020) shows that the redundancy of a single-deletion $s$-substitution correcting code is asymptotically at least $(s+1)\log n+o(\log n)$, where $n$ is the length of the codes. They also provide a…

Information Theory · Computer Science 2020-11-24 Wentu Song , Nikita Polyanskii , Kui Cai , Xuan He

Convolutional codes are constructed, designed and analysed using row and/or block structures of unit algebraic schemes. Infinite series of such codes and of codes with specific properties are derived. Properties are shown algebraically and…

Rings and Algebras · Mathematics 2018-04-04 Ted Hurley

Recently, DNA storage has surfaced as a promising alternative for data storage, presenting notable benefits in terms of storage capacity, cost-effectiveness in maintenance, and the capability for parallel replication. Mathematically, the…

Information Theory · Computer Science 2024-01-31 Yi Lu , Yun Ma , Chenghao Li , Xin Zhang , Guangxiang Si

We consider the problem of constructing codes that can correct $\delta$ deletions occurring in an arbitrary binary string of length $n$ bits. Varshamov-Tenengolts (VT) codes can correct all possible single deletions $(\delta=1)$ with an…

Information Theory · Computer Science 2017-05-01 Serge Kas Hanna , Salim El Rouayheb

Finding deletion-correcting codes of maximum size has been an open problem for over 70 years, even for a single deletion. In this paper, we propose a novel approach for constructing deletion-correcting codes. A code is a set of sequences…

Artificial Intelligence · Computer Science 2025-04-02 Franziska Weindel , Reinhard Heckel

Imperfect measurement can degrade a quantum error correction scheme. A solution that restores fault tolerance is to add redundancy to the process of syndrome extraction. In this work, we show how to optimize this process for an arbitrary…

Quantum Physics · Physics 2019-07-12 Vickram N. Premakumar , Hele Sha , Daniel Crow , Eric Bach , Robert Joynt

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 training algorithm, which is robust to missing or ambiguous…

Machine Learning · Statistics 2019-06-11 Kiran Koshy Thekumparampil , Sewoong Oh , Ashish Khetan

In distributed optimization problems, a technique called gradient coding, which involves replicating data points, has been used to mitigate the effect of straggling machines. Recent work has studied approximate gradient coding, which…

Machine Learning · Statistics 2021-08-09 Margalit Glasgow , Mary Wootters

We consider the problem of constructing deletion correcting codes over a binary alphabet and take a graph theoretic view. An $n$-bit $s$-deletion correcting code is an independent set in a particular graph. We propose constructing such a…

Information Theory · Computer Science 2012-11-20 Daniel Cullina , Ankur A. Kulkarni , Negar Kiyavash

Trustworthiness in artificial intelligence depends not only on what a model decides, but also on how it handles and explains cases in which a reliable decision cannot be made. In critical domains such as healthcare and finance, a reject…

Machine Learning · Computer Science 2026-03-17 Gleilson Pedro Fernandes , Thiago Alves Rocha

Constructing an efficient and robust quantum memory is central to the challenge of engineering feasible quantum computer architectures. Quantum error correction codes can solve this problem in theory, but without careful design it can…

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into…

Machine Learning · Computer Science 2020-04-28 Jan Philip Göpfert , André Artelt , Heiko Wersing , Barbara Hammer

Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex…

Machine Learning · Computer Science 2021-06-09 Varun Gupta , Christopher Jung , Seth Neel , Aaron Roth , Saeed Sharifi-Malvajerdi , Chris Waites