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Showing 1 to 8 of 8 for “"adversarial noise"”.

  1. Using adversarial noise for privacy protection: an evaluation of hybrid attacks across application targets

    Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01

    uiuc Repository record for Using adversarial noise for privacy protection: an evaluation of hybrid attacks across application targets (opens in a new tab)

  2. Multishot Capacity of Adversarial Networks

    Adversarial network coding studies the transmission of data over networks affected by adversarial noise. In this realm, the noise is modeled by an omniscient adversary who is restricted to corrupting a proper subset of the network edges. In 2018, Ravagnani and Kschischang established a …

    vt Repository record for Multishot Capacity of Adversarial Networks (opens in a new tab)

  3. Cryptographic error correction

    … correction in which the source of errors is adversarial, but limited to feasible computation. In this model, we construct appealingly simple, general, and efficient cryptographic coding schemes which can recover from much larger error rates than schemes for classical models of adversarial

    mit Repository record for Cryptographic error correction (opens in a new tab)

  4. Distributed computation and inference

    … distributed computations in the presence of adversarial noise. This work falls into the framework of interactive coding, which is an extension of error correcting codes to interactive settings commonly found in theoretical computer science. On the inference side, we model social and …

    mit Repository record for Distributed computation and inference (opens in a new tab)

  5. Fundamental Limits of Learning for Generalizability, Data Resilience, and Resource Efficiency

    … 3), supervised learning with arbitrary or adversarial noise (Chapter 4); partial-feedback in standard contextual bandits (Chapter 5) and, as a first step towards more complex reinforcement learning settings, contextual bandits with non-stationary or adversarial rewards (Chapter 6). We …

    mit Repository record for Fundamental Limits of Learning for Generalizability, Data Resilience, and Resource Efficiency (opens in a new tab)

  6. Intractability Results for some Computational Problems

    … to learning parities with random classification noise, commonly referred to as the noisy parity problem. Together with the parity learning algorithm of Blum et al, this gives the first nontrivial algorithm for agnostic learning of parities. We use similar techniques to reduce learning of two …

    gatech Repository record for Intractability Results for some Computational Problems (opens in a new tab)

  7. Balanced allocations under incomplete information: New settings and techniques

    … - We analyse the Two-Choice process with random, adversarial and delay noise, proving tight bounds for various settings. In the adversarial setting, the adversary can decide in which of the two sampled bins the ball is allocated to, only when the two loads differ by at most 𝑔. The analysis of this …

    cambridge Repository record for Balanced allocations under incomplete information: New settings and techniques (opens in a new tab)

  8. Active and Semi-Supervised Learning for Speech Recognition

    … the novel algorithm cosine-distance virtual adversarial training (CD-VAT) was developed. Whilst not directed at speech recognition, this technique was inspired by initial work towards using consistency-regularisation for speech recognition. CD-VAT allows for semi-supervised training of …

    cambridge Repository record for Active and Semi-Supervised Learning for Speech Recognition (opens in a new tab)