51 research outputs found
Hyperprofile-based Computation Offloading for Mobile Edge Networks
In recent studies, researchers have developed various computation offloading
frameworks for bringing cloud services closer to the user via edge networks.
Specifically, an edge device needs to offload computationally intensive tasks
because of energy and processing constraints. These constraints present the
challenge of identifying which edge nodes should receive tasks to reduce
overall resource consumption. We propose a unique solution to this problem
which incorporates elements from Knowledge-Defined Networking (KDN) to make
intelligent predictions about offloading costs based on historical data. Each
server instance can be represented in a multidimensional feature space where
each dimension corresponds to a predicted metric. We compute features for a
"hyperprofile" and position nodes based on the predicted costs of offloading a
particular task. We then perform a k-Nearest Neighbor (kNN) query within the
hyperprofile to select nodes for offloading computation. This paper formalizes
our hyperprofile-based solution and explores the viability of using machine
learning (ML) techniques to predict metrics useful for computation offloading.
We also investigate the effects of using different distance metrics for the
queries. Our results show various network metrics can be modeled accurately
with regression, and there are circumstances where kNN queries using Euclidean
distance as opposed to rectilinear distance is more favorable.Comment: 5 pages, NSF REU Site publicatio
Investigating the Use of Recurrent Neural Networks in Modeling Guitar Distortion Effects
Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects such as overdrive, distortion, and compression. This research aims to analyze the inner workings of these neural networks and how they can replicate audio effects to such a high caliber. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) will also be used to model a distortion effect unit and compare the results they yield with the original audio device modeled
Using Neural Networks to Model Guitar Distortion
Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to produce audio effects that yield strikingly similar results to their analog counterparts. Recurrent Neural Networks and Temporal Convolutional Networks have proven to be exceptional at modeling audio effects such as overdrive, distortion, and compression. The goal of this research is to analyze the inner workings of these neural networks and how they can replicate audio effects to such a high caliber. Some of these networks will also be used to model a distortion effect and compare the results they yield with the original audio device modeled
\u27Follow the Data\u27 — What Data Says About Real-world Behavior in Commons Problems
We test the game-theoretic foundations of common-pool resources using an individual-level dataset of groundwater usage that accounts for 3% of US irrigated agriculture. Using necessary and sufficient revealed preference tests for dynamic games, we find: (i) a rejection of the standard game-theoretic arguments based on strategic substitutes, and instead (ii) support for models building on reciprocity-like behavior and strategic complements. By estimating strategic interactions directly, we find that reciprocity-like interactions drive behavior more than market and climate trends. Taken together, we take a step toward developing more realistic models to understand groundwater usage, and related issues pertaining to tragedy of the commons and commons governance
Properly Learning Decision Trees with Queries Is NP-Hard
We prove that it is NP-hard to properly PAC learn decision trees with
queries, resolving a longstanding open problem in learning theory (Bshouty
1993; Guijarro-Lavin-Raghavan 1999; Mehta-Raghavan 2002; Feldman 2016). While
there has been a long line of work, dating back to (Pitt-Valiant 1988),
establishing the hardness of properly learning decision trees from random
examples, the more challenging setting of query learners necessitates different
techniques and there were no previous lower bounds. En route to our main
result, we simplify and strengthen the best known lower bounds for a different
problem of Decision Tree Minimization (Zantema-Bodlaender 2000; Sieling 2003).
On a technical level, we introduce the notion of hardness distillation, which
we study for decision tree complexity but can be considered for any complexity
measure: for a function that requires large decision trees, we give a general
method for identifying a small set of inputs that is responsible for its
complexity. Our technique even rules out query learners that are allowed
constant error. This contrasts with existing lower bounds for the setting of
random examples which only hold for inverse-polynomial error.
Our result, taken together with a recent almost-polynomial time query
algorithm for properly learning decision trees under the uniform distribution
(Blanc-Lange-Qiao-Tan 2022), demonstrates the dramatic impact of distributional
assumptions on the problem.Comment: 41 pages, 10 figures, FOCS 202
Bostonia
Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs
A Query-Optimal Algorithm for Finding Counterfactuals
We design an algorithm for finding counterfactuals with strong theoretical
guarantees on its performance. For any monotone model and
instance , our algorithm makes queries to and returns {an {\sl optimal}} counterfactual for
: a nearest instance to for which . Here is the sensitivity of , a discrete analogue of the
Lipschitz constant, and is the distance from to
its nearest counterfactuals. The previous best known query complexity was
, achievable by brute-force local search. We
further prove a lower bound of on the query complexity of any algorithm, thereby showing that the
guarantees of our algorithm are essentially optimal.Comment: 22 pages, ICML 202
A Strong Composition Theorem for Junta Complexity and the Boosting of Property Testers
We prove a strong composition theorem for junta complexity and show how such
theorems can be used to generically boost the performance of property testers.
The -approximate junta complexity of a function is the
smallest integer such that is -close to a function that
depends only on variables. A strong composition theorem states that if
has large -approximate junta complexity, then has even
larger -approximate junta complexity, even for . We develop a fairly complete understanding of this behavior,
proving that the junta complexity of is characterized by that of
along with the multivariate noise sensitivity of . For the important
case of symmetric functions , we relate their multivariate noise sensitivity
to the simpler and well-studied case of univariate noise sensitivity.
We then show how strong composition theorems yield boosting algorithms for
property testers: with a strong composition theorem for any class of functions,
a large-distance tester for that class is immediately upgraded into one for
small distances. Combining our contributions yields a booster for junta
testers, and with it new implications for junta testing. This is the first
boosting-type result in property testing, and we hope that the connection to
composition theorems adds compelling motivation to the study of both topics.Comment: 44 pages, 1 figure, FOCS 202
Certification with an NP Oracle
In the certification problem, the algorithm is given a function with
certificate complexity and an input , and the goal is to find a
certificate of size for 's value at . This
problem is in , and assuming , is not in . Prior works, dating back to Valiant in
1984, have therefore sought to design efficient algorithms by imposing
assumptions on such as monotonicity.
Our first result is a algorithm for the general
problem. The key ingredient is a new notion of the balanced influence of
variables, a natural variant of influence that corrects for the bias of the
function. Balanced influences can be accurately estimated via uniform
generation, and classic algorithms are known for
the latter task.
We then consider certification with stricter instance-wise guarantees: for
each , find a certificate whose size scales with that of the smallest
certificate for . In sharp contrast with our first result, we show
that this problem is -hard even to approximate. We
obtain an optimal inapproximability ratio, adding to a small handful of
problems in the higher levels of the polynomial hierarchy for which optimal
inapproximability is known. Our proof involves the novel use of bit-fixing
dispersers for gap amplification.Comment: 25 pages, 2 figures, ITCS 202
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