135 research outputs found
Lower bounds to randomized algorithms for graph properties
AbstractFor any property P on n-vertex graphs, let C(P) be the minimum number of edges needed to be examined by any decision tree algorithm for determining P. In 1975 Rivest and Vuillemin settled the Aanderra-Rosenberg Conjecture, proving that C(P)=Ω(n2) for every nontrivial monotone graph property P. An intriguing open question is whether the theorem remains true when randomized algorithms are allowed. In this paper we show that Ω(n(log n)112 edges need to be examined by any randomized algorithm for determining any nontrivial monotone graph property
Quantum replication at the Heisenberg limit
No process in nature can perfectly clone an arbitrary quantum state. But is
it possible to engineer processes that replicate quantum information with
vanishingly small error? Here we demonstrate the possibility of probabilistic
super-replication phenomena where N equally prepared quantum clocks are
transformed into a much larger number of M nearly perfect replicas, with an
error that rapidly vanishes whenever M is small compared to the square of N.
The quadratic replication rate is the ultimate limit imposed by Quantum
Mechanics to the proliferation of information and is fundamentally linked with
the Heisenberg limit of quantum metrology.Comment: 9 + 16 pages, 2 figures, published versio
Credible, Truthful, and Two-Round (Optimal) Auctions via Cryptographic Commitments
We consider the sale of a single item to multiple buyers by a
revenue-maximizing seller. Recent work of Akbarpour and Li formalizes
\emph{credibility} as an auction desideratum, and prove that the only optimal,
credible, strategyproof auction is the ascending price auction with reserves
(Akbarpour and Li, 2019).
In contrast, when buyers' valuations are MHR, we show that the mild
additional assumption of a cryptographically secure commitment scheme suffices
for a simple \emph{two-round} auction which is optimal, strategyproof, and
credible (even when the number of bidders is only known by the auctioneer).
We extend our analysis to the case when buyer valuations are
-strongly regular for any , up to arbitrary
in credibility. Interestingly, we also prove that this construction cannot be
extended to regular distributions, nor can the be removed with
multiple bidders
When do Models Generalize? A Perspective from Data-Algorithm Compatibility
One of the major open problems in machine learning is to characterize
generalization in the overparameterized regime, where most traditional
generalization bounds become inconsistent (Nagarajan and Kolter, 2019). In many
scenarios, their failure can be attributed to obscuring the crucial interplay
between the training algorithm and the underlying data distribution. To address
this issue, we propose a concept named compatibility, which quantitatively
characterizes generalization in a both data-relevant and algorithm-relevant
manner. By considering the entire training trajectory and focusing on
early-stopping iterates, compatibility exploits the data and the algorithm
information and is therefore a more suitable notion for generalization. We
validate this by theoretically studying compatibility under the setting of
solving overparameterized linear regression with gradient descent.
Specifically, we perform a data-dependent trajectory analysis and derive a
sufficient condition for compatibility in such a setting. Our theoretical
results demonstrate that in the sense of compatibility, generalization holds
with significantly weaker restrictions on the problem instance than the
previous last iterate analysis
PrivacyFL: A simulator for privacy-preserving and secure federated learning
Federated learning is a technique that enables distributed clients to
collaboratively learn a shared machine learning model while keeping their
training data localized. This reduces data privacy risks, however, privacy
concerns still exist since it is possible to leak information about the
training dataset from the trained model's weights or parameters. Setting up a
federated learning environment, especially with security and privacy
guarantees, is a time-consuming process with numerous configurations and
parameters that can be manipulated. In order to help clients ensure that
collaboration is feasible and to check that it improves their model accuracy, a
real-world simulator for privacy-preserving and secure federated learning is
required. In this paper, we introduce PrivacyFL, which is an extensible, easily
configurable and scalable simulator for federated learning environments. Its
key features include latency simulation, robustness to client departure,
support for both centralized and decentralized learning, and configurable
privacy and security mechanisms based on differential privacy and secure
multiparty computation. In this paper, we motivate our research, describe the
architecture of the simulator and associated protocols, and discuss its
evaluation in numerous scenarios that highlight its wide range of functionality
and its advantages. Our paper addresses a significant real-world problem:
checking the feasibility of participating in a federated learning environment
under a variety of circumstances. It also has a strong practical impact because
organizations such as hospitals, banks, and research institutes, which have
large amounts of sensitive data and would like to collaborate, would greatly
benefit from having a system that enables them to do so in a privacy-preserving
and secure manner
On Computational Power of Quantum Read-Once Branching Programs
In this paper we review our current results concerning the computational
power of quantum read-once branching programs. First of all, based on the
circuit presentation of quantum branching programs and our variant of quantum
fingerprinting technique, we show that any Boolean function with linear
polynomial presentation can be computed by a quantum read-once branching
program using a relatively small (usually logarithmic in the size of input)
number of qubits. Then we show that the described class of Boolean functions is
closed under the polynomial projections.Comment: In Proceedings HPC 2010, arXiv:1103.226
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