281 research outputs found
Optimal Unateness Testers for Real-Valued Functions: Adaptivity Helps
We study the problem of testing unateness of functions f:{0,1}^d -> R. We give an O(d/epsilon . log(d/epsilon))-query nonadaptive tester and an O(d/epsilon)-query adaptive tester and show that both testers are optimal for a fixed distance parameter epsilon. Previously known unateness testers worked only for Boolean functions, and their query complexity had worse dependence on the dimension both for the adaptive and the nonadaptive case. Moreover, no lower bounds for testing unateness were known. We generalize our results to obtain optimal unateness testers for functions f:[n]^d -> R.
Our results establish that adaptivity helps with testing unateness of real-valued functions on domains of the form {0,1}^d and, more generally, [n]^d. This stands in contrast to the situation for monotonicity testing where there is no adaptivity gap for functions f:[n]^d -> R
Local Lipschitz Filters for Bounded-Range Functions with Applications to Arbitrary Real-Valued Functions
We study local filters for the Lipschitz property of real-valued functions
, where the Lipschitz property is defined with respect to an
arbitrary undirected graph . We give nearly optimal local Lipschitz
filters both with respect to -distance and -distance. Previous
work only considered unbounded-range functions over . Jha and
Raskhodnikova (SICOMP `13) gave an algorithm for such functions with lookup
complexity exponential in , which Awasthi et al. (ACM Trans. Comput. Theory)
showed was necessary in this setting. We demonstrate that important
applications of local Lipschitz filters can be accomplished with filters for
functions with bounded-range. For functions , we circumvent
the lower bound and achieve running time for the
-respecting filter and for the
-respecting filter. Our local filters provide a novel Lipschitz
extension that can be implemented locally. Furthermore, we show that our
algorithms have nearly optimal dependence on for the domain . In
addition, our lower bound resolves an open question of Awasthi et al., removing
one of the conditions necessary for their lower bound for general range. We
prove our lower bound via a reduction from distribution-free Lipschitz testing
and a new technique for proving hardness for {\em adaptive} algorithms. We
provide two applications of our local filters to arbitrary real-valued
functions. In the first application, we use them in conjunction with the
Laplace mechanism for differential privacy and noisy binary search to provide
mechanisms for privately releasing outputs of black-box functions, even in the
presence of malicious clients. In the second application, we use our local
filters to obtain the first nontrivial tolerant tester for the Lipschitz
property
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
Differential privacy is concerned about the prediction quality while
measuring the privacy impact on individuals whose information is contained in
the data. We consider differentially private risk minimization problems with
regularizers that induce structured sparsity. These regularizers are known to
be convex but they are often non-differentiable. We analyze the standard
differentially private algorithms, such as output perturbation, Frank-Wolfe and
objective perturbation. Output perturbation is a differentially private
algorithm that is known to perform well for minimizing risks that are strongly
convex. Previous works have derived excess risk bounds that are independent of
the dimensionality. In this paper, we assume a particular class of convex but
non-smooth regularizers that induce structured sparsity and loss functions for
generalized linear models. We also consider differentially private Frank-Wolfe
algorithms to optimize the dual of the risk minimization problem. We derive
excess risk bounds for both these algorithms. Both the bounds depend on the
Gaussian width of the unit ball of the dual norm. We also show that objective
perturbation of the risk minimization problems is equivalent to the output
perturbation of a dual optimization problem. This is the first work that
analyzes the dual optimization problems of risk minimization problems in the
context of differential privacy
Parameterized Property Testing of Functions
We investigate the parameters in terms of which the complexity of sublinear-time algorithms should be expressed. Our goal is to find input parameters that are tailored to the combinatorics of the specific problem being studied and design algorithms that run faster when these parameters are small. This direction enables us to surpass the (worst-case) lower bounds, expressed in terms of the input size, for several problems. Our aim is to develop a similar level of understanding of the complexity of sublinear-time algorithms to the one that was enabled by research in parameterized complexity for classical algorithms.
Specifically, we focus on testing properties of functions. By parameterizing the query complexity in terms of the size r of the image of the input function, we obtain testers for monotonicity and convexity of functions of the form f:[n]to mathbb{R} with query complexity O(log r), with no dependence on n. The result for monotonicity circumvents the Omega(log n) lower bound by Fischer (Inf. Comput., 2004) for this problem. We present several other parameterized testers, providing compelling evidence that expressing the query complexity of property testers in terms of the input size is not always the best choice
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