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We studied the problem of adaptive distance estimation where one is required to estimate the distance between a sequence of possibly adversarially chosen query points and the points in a dataset

NIPS 2020, (2020)

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Abstract

We provide a static data structure for distance estimation which supports {\it adaptive} queries. Concretely, given a dataset $X = \{x_i\}_{i = 1}^n$ of $n$ points in $\mathbb{R}^d$ and $0 < p \leq 2$, we construct a randomized data structure with low memory consumption and query time which, when later given any query point \$q \in \math...More

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Introduction
• Much research attention has been directed towards understanding the performance of machine learning algorithms in adaptive or adversarial environments.
• The authors' method is simple and likely to be applicable to other domains: the authors describe a generic approach for transforming randomized Monte Carlo data structures which do not support adaptive queries to ones that do, and show that for the problem at hand, it can be applied to standard nonadaptive solutions to p norm estimation with negligible overhead in query time and a factor d overhead in memory.
• The authors provide a new data structure for ADE in the adaptive setting, for p norms (0 < p ≤ 2) with memory consumption O ((n + d)d/ε2), slightly more than the O(nd) required to store X in memory explicitly, but with the benefit that the query time is only O (ε−2(n + d)) as opposed to the O(nd) query time of the trivial algorithm.
Highlights
• In recent years, much research attention has been directed towards understanding the performance of machine learning algorithms in adaptive or adversarial environments
• Our method is simple and likely to be applicable to other domains: we describe a generic approach for transforming randomized Monte Carlo data structures which do not support adaptive queries to ones that do, and show that for the problem at hand, it can be applied to standard nonadaptive solutions to p norm estimation with negligible overhead in query time and a factor d overhead in memory
• We study the problem of designing efficient data structures for distance estimation, a basic primitive in algorithms for nonparametric estimation and exploratory data analysis, in the adaptive setting where the sequence of queries made to the data structure may be adversarially chosen
• We studied the problem of adaptive distance estimation where one is required to estimate the distance between a sequence of possibly adversarially chosen query points and the points in a dataset
• The only previous result with comparable guarantees is an algorithm for the Euclidean case which only returns one near neighbor [Kle97] and does not estimate all distances
• Starting with the influential work of [Bre96, Bre01], ensemble methods have been a mainstay in practical machine learning techniques
Results
• Pre-processing time for the data structure can be improved by using fast algorithms for rectangular matrix multiplication (See Section 4 for further discussion).
• For the specific application of approximate nearest neighbor, the works of [Kle97, KOR00] provide non-trivial data structures supporting adaptive queries; a comparison with the results is given in Subsection 1.2.
• The work of [Kle97] presents another algorithm with memory and query/pre-processing times similar to the ADE data structure though for Euclidean space.
• While both of these works provide algorithms with runtimes sublinear in n, they are for finding the approximate single nearest neighbor (“1-NN”) and do not provide distance estimates to all points in the same query time.
• For any 0 < δ < 1 and any 0 < p < 2, there is a data structure for the ADE problem in p space that succeeds on any query with probability at least 1 − δ, even in a sequence of adaptively chosen queries.
• Algorithm 2 when given as input any query point q ∈ Rd , D = {Πj, {Πjxi}in=1}lj=1 where {Πj}lj=1 are (ε, p)-representative, ε and δ, outputs distance estimates {di}in=1 satisfying:
• The query time follows from the time required to compute Πjk q for k ∈ [r] with r = O(log n/δ), the n median computations in Algorithm 2 and the setting of m.
Conclusion
• The proof of Theorem 4.1 follows by using Algorithm 1 to construct the adaptive data structure, D, and Algorithm 2 to answer any query, q.
• The only previous result with comparable guarantees is an algorithm for the Euclidean case which only returns one near neighbor [Kle97] and does not estimate all distances.
• Are there other machine learning tasks for which such trade-offs can be quantified?
Summary
• Much research attention has been directed towards understanding the performance of machine learning algorithms in adaptive or adversarial environments.
• The authors' method is simple and likely to be applicable to other domains: the authors describe a generic approach for transforming randomized Monte Carlo data structures which do not support adaptive queries to ones that do, and show that for the problem at hand, it can be applied to standard nonadaptive solutions to p norm estimation with negligible overhead in query time and a factor d overhead in memory.
• The authors provide a new data structure for ADE in the adaptive setting, for p norms (0 < p ≤ 2) with memory consumption O ((n + d)d/ε2), slightly more than the O(nd) required to store X in memory explicitly, but with the benefit that the query time is only O (ε−2(n + d)) as opposed to the O(nd) query time of the trivial algorithm.
• Pre-processing time for the data structure can be improved by using fast algorithms for rectangular matrix multiplication (See Section 4 for further discussion).
• For the specific application of approximate nearest neighbor, the works of [Kle97, KOR00] provide non-trivial data structures supporting adaptive queries; a comparison with the results is given in Subsection 1.2.
• The work of [Kle97] presents another algorithm with memory and query/pre-processing times similar to the ADE data structure though for Euclidean space.
• While both of these works provide algorithms with runtimes sublinear in n, they are for finding the approximate single nearest neighbor (“1-NN”) and do not provide distance estimates to all points in the same query time.
• For any 0 < δ < 1 and any 0 < p < 2, there is a data structure for the ADE problem in p space that succeeds on any query with probability at least 1 − δ, even in a sequence of adaptively chosen queries.
• Algorithm 2 when given as input any query point q ∈ Rd , D = {Πj, {Πjxi}in=1}lj=1 where {Πj}lj=1 are (ε, p)-representative, ε and δ, outputs distance estimates {di}in=1 satisfying:
• The query time follows from the time required to compute Πjk q for k ∈ [r] with r = O(log n/δ), the n median computations in Algorithm 2 and the setting of m.
• The proof of Theorem 4.1 follows by using Algorithm 1 to construct the adaptive data structure, D, and Algorithm 2 to answer any query, q.
• The only previous result with comparable guarantees is an algorithm for the Euclidean case which only returns one near neighbor [Kle97] and does not estimate all distances.
• Are there other machine learning tasks for which such trade-offs can be quantified?
Related work
• As previously discussed, there has been growing interest in understanding risks posed by the deployment of algorithms in potentially adversarial settings ([BCM+17, HMPW16, GSS15, YHZL19, LCLS17, PMG16]). In addition, the problem of preserving statistical validity in exploratory data analysis has been well explored [DFH+15a, BNS+16, DFH+15b, DFH+15c, DSSU17] where the goal is to maintain coherence with an unknown distribution from which one obtains data samples. There has also been previous work studying linear sketches in adversarial scenarios quite different from those appearing here ([MNS11, GHR+12, GHS+12]).

Specifically on data structures, it is, of course, the case that deterministic data structures provide correctness guarantees for adaptive queries automatically, though we are unaware of any non-trivial deterministic solutions for ADE. For the specific application of approximate nearest neighbor, the works of [Kle97, KOR00] provide non-trivial data structures supporting adaptive queries; a comparison with our results is given in Subsection 1.2. In the context of streaming algorithms (i.e. sublinear memory), the very recent work of Ben-Eliezer et al [BEJWY20] considers streaming algorithms with both adaptive queries and updates. One key difference is they considered the insertion-only model of streaming, which does not allow one to model computing some function of the difference of two vectors (e.g. the norm of q − xi).
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• B2(0, 1 + ε/2, d). From the fact that the sets B2(x, ε/2, d) and B2(y, ε/2, d) are disjoint for distinct x, y ∈ T, we have: Vol (Tε) = |T| Vol (B2(0, ε/2, d)) ≤ Vol (B2(0, 1 + ε/2, d)). By dividing both sides and by using that fact that Vol (B2(0, l, d)) = ld Vol (B2(0, 1, d)), we get:
• 1. Through a similar manipulation, we get Z − Z ≤ 1 and this concludes the proof of the lemma.
Author
Yeshwanth Cherapanamjeri
Jelani Nelson