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### An Empirical Evaluation of the Effect of Adversarial Labels on Classifier Accuracy Estimation

This paper examines the effect of providing adversarial labels to several algorithms that use noisy labels from multiple experts to estimate classifier accuracy, referred to hereafter as "estimators." We propose four adversary labeling strategies and use experiments on synthetic data to gauge their impact on the estimators. Our results show that even a single adversary can considerably impact the effectiveness of an estimator. In addition, we find that estimators that weight the input of all experts equally tend to be much more affected by the inclusion of adversaries than those that can separately model each expert and that the impact of adversaries is lessened when the experts have higher accuracy.

### Reputation-based Worker Filtering in Crowdsourcing

In this paper, we study the problem of aggregating noisy labels from crowd workers to infer the underlying true labels of binary tasks. Unlike most prior work which has examined this problem under the random worker paradigm, we consider a much broader class of {\em adversarial} workers with no specific assumptions on their labeling strategy. Our key contribution is the design of a computationally efficient reputation algorithm to identify and filter out these adversarial workers in crowdsourcing systems. Our algorithm uses the concept of optimal semi-matchings in conjunction with worker penalties based on label disagreements, to assign a reputation score for every worker. We provide strong theoretical guarantees for deterministic adversarial strategies as well as the extreme case of {\em sophisticated} adversaries where we analyze the worst-case behavior of our algorithm.

### The Hidden Vulnerability of Distributed Learning in Byzantium

While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending poisoned gradients during the training phase. Some of these approaches have been proven Byzantine-resilient: they ensure the convergence of SGD despite the presence of a minority of adversarial workers. We show in this paper that convergence is not enough. In high dimension $d \gg 1$, an adver\-sary can build on the loss function's non--convexity to make SGD converge to ineffective models. More precisely, we bring to light that existing Byzantine--resilient schemes leave a margin of poisoning of $\Omega\left(f(d)\right)$, where $f(d)$ increases at least like $\sqrt[p]{d~}$. Based on this leeway, we build a simple attack, and experimentally show its strong to utmost effectivity on CIFAR--10 and MNIST. We introduce Bulyan, and prove it significantly reduces the attackers leeway to a narrow $O( \frac{1}{\sqrt{d~}})$ bound. We empirically show that Bulyan does not suffer the fragility of existing aggregation rules and, at a reasonable cost in terms of required batch size, achieves convergence as if only non--Byzantine gradients had been used to update the model.

### Sentiment Analysis via Deep Hybrid Textual-Crowd Learning Model

Crowdsourcing technique provides an efficient platform to employ human skills in sentiment analysis, which is a difficult task for automatic language models due to the large variations in context, writing style, view point and so on. However, the standard crowdsourcing aggregation models are incompetent when the number of crowd labels per worker is not sufficient to train parameters, or when it is not feasible to collect labels for each sample in a large dataset. In this paper, we propose a novel hybrid model to exploit both crowd and text data for sentiment analysis, consisting of a generative crowdsourcing aggregation model and a deep sentimental autoencoder. Combination of these two sub-models is obtained based on a probabilistic framework rather than a heuristic way. We introduce a unified objective function to incorporate the objectives of both sub-models, and derive an efficient optimization algorithm to jointly solve the corresponding problem. Experimental results indicate that our model achieves superior results in comparison with the state-of-the-art models, especially when the crowd labels are scarce.

We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system. Assuming a set of $n$ workers, up to $f$ being Byzantine, we ask how resilient can SGD be, without limiting the dimension, nor the size of the parameter space. We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure.