72 research outputs found
Pairwise Independent Random Walks Can Be Slightly Unbounded
A family of problems that have been studied in the context of various streaming algorithms are generalizations of the fact that the expected maximum distance of a 4-wise independent random walk on a line over n steps is O(sqrt{n}). For small values of k, there exist k-wise independent random walks that can be stored in much less space than storing n random bits, so these properties are often useful for lowering space bounds. In this paper, we show that for all of these examples, 4-wise independence is required by demonstrating a pairwise independent random walk with steps uniform in +/- 1 and expected maximum distance Omega(sqrt{n} lg n) from the origin. We also show that this bound is tight for the first and second moment, i.e. the expected maximum square distance of a 2-wise independent random walk is always O(n lg^2 n). Also, for any even k >= 4, we show that the kth moment of the maximum distance of any k-wise independent random walk is O(n^{k/2}). The previous two results generalize to random walks tracking insertion-only streams, and provide higher moment bounds than currently known. We also prove a generalization of Kolmogorov\u27s maximal inequality by showing an asymptotically equivalent statement that requires only 4-wise independent random variables with bounded second moments, which also generalizes a result of Blasiok
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation
We provide improved lower bounds for two well-known high-dimensional private
estimation tasks. First, we prove that for estimating the covariance of a
Gaussian up to spectral error with approximate differential privacy,
one needs samples for any , which is tight up
to logarithmic factors. This improves over previous work which established this
for , and is also simpler than
previous work. Next, we prove that for estimating the mean of a heavy-tailed
distribution with bounded th moments with approximate differential privacy,
one needs samples. This matches known upper bounds and
improves over the best known lower bound for this problem, which only hold for
pure differential privacy, or when . Our techniques follow the method of
fingerprinting and are generally quite simple. Our lower bound for heavy-tailed
estimation is based on a black-box reduction from privately estimating
identity-covariance Gaussians. Our lower bound for covariance estimation
utilizes a Bayesian approach to show that, under an Inverse Wishart prior
distribution for the covariance matrix, no private estimator can be accurate
even in expectation, without sufficiently many samples.Comment: 23 page
Optimal Time-Backlog Tradeoffs for the Variable-Processor Cup Game
The \emph{-processor cup game} is a classic and widely studied scheduling
problem that captures the setting in which a -processor machine must assign
tasks to processors over time in order to ensure that no individual task ever
falls too far behind. The problem is formalized as a multi-round game in which
two players, a filler (who assigns work to tasks) and an emptier (who schedules
tasks) compete. The emptier's goal is to minimize backlog, which is the maximum
amount of outstanding work for any task.
Recently, Kuszmaul and Westover (ITCS, 2021) proposed the
\emph{variable-processor cup game}, which considers the same problem, except
that the amount of resources available to the players (i.e., the number of
processors) fluctuates between rounds of the game. They showed that this
seemingly small modification fundamentally changes the dynamics of the game:
whereas the optimal backlog in the fixed -processor game is , independent of , the optimal backlog in the variable-processor game is
. The latter result was only known to apply to games with
\emph{exponentially many} rounds, however, and it has remained an open question
what the optimal tradeoff between time and backlog is for shorter games.
This paper establishes a tight trade-off curve between time and backlog in
the variable-processor cup game. Importantly, we prove that for a game
consisting of rounds, the optimal backlog is if and only if . Our techniques also allow for us to resolve several other
open questions concerning how the variable-processor cup game behaves in
beyond-worst-case-analysis settings.Comment: 40 pages, published in International Conference on Automata,
Languages, and Programming (ICALP), 2022. Abstract abridged for arXiv
submission: see paper for full abstract. Updated to acknowledge additional
fundin
A faster and simpler algorithm for learning shallow networks
We revisit the well-studied problem of learning a linear combination of
ReLU activations given labeled examples drawn from the standard -dimensional
Gaussian measure. Chen et al. [CDG+23] recently gave the first algorithm for
this problem to run in time when ,
where is the target error. More precisely, their algorithm runs
in time and learns over multiple
stages. Here we show that a much simpler one-stage version of their algorithm
suffices, and moreover its runtime is only .Comment: 14 page
Improved Diversity Maximization Algorithms for Matching and Pseudoforest
In this work we consider the diversity maximization problem, where given a
data set of elements, and a parameter , the goal is to pick a subset
of of size maximizing a certain diversity measure. [CH01] defined a
variety of diversity measures based on pairwise distances between the points. A
constant factor approximation algorithm was known for all those diversity
measures except ``remote-matching'', where only an approximation
was known. In this work we present an approximation for this remaining
notion. Further, we consider these notions from the perpective of composable
coresets. [IMMM14] provided composable coresets with a constant factor
approximation for all but ``remote-pseudoforest'' and ``remote-matching'',
which again they only obtained a approximation. Here we also close
the gap up to constants and present a constant factor composable coreset
algorithm for these two notions. For remote-matching, our coreset has size only
, and for remote-pseudoforest, our coreset has size
for any , for an
-approximate coreset.Comment: 27 pages, 1 table. Accepted to APPROX, 202
POSE ESTIMATION FOR ROBOTIC DISASSEMBLY USING RANSAC WITH LINE FEATURES
In this thesis, a new technique to recognize and estimate the pose of a given 3-D object from a single real image provided known prior knowledge of its approximate structure is proposed. Metrics to evaluate the correctness of a calculated pose are presented and analyzed. The traditional and the more recent approaches used in solving this problem are explored and the various methodologies adopted are discussed. The first step in disassembling a given assembly from its image is to recognize the attitude and translation of each of its constituent components - a fundamental problem which is being addressed in this work. The proposed algorithm does not depend on uniquely identifiable 3D model surface features for its operation - this makes it ideally suited for object recognition for assemblies. The algorithm works well even for low-resolution occluded object images taken under variable illumination conditions and heavy shadows and performs markedly better when these factors are removed. The algorithm uses a combination of various computer vision concepts such as segmentation, corner detection and camera calibration, and subsequently adopts a line-based object pose estimation technique (originally based on the RANSAC algorithm) to settle on the best pose estimate. The novelty of the proposed technique lies in the specific way in which the poses are evaluated in the RANSAC-like algorithm. In particular, line-based pose evaluation is adopted where the line chamfer image is used to evaluate the error distance between the projected model line and the image edges. The correctness of the computed pose is determined based on the number of line matches computed using this error distance. As opposed to the RANSAC algorithm where the search process is pseudo-random, we do an exhaustive pose search instead. Techniques to reduce the search space by a large amount are discussed and implemented. The algorithm was used to estimate the pose of 28 objects in 22 images, where some images contain multiple objects. The algorithm has been found to work with a 3-D mismatch error of less than 2.5cm in 90% of the cases and less than 1cm error in 53% of the cases in the dataset used
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