2,431 research outputs found
The Limitations of Optimization from Samples
In this paper we consider the following question: can we optimize objective
functions from the training data we use to learn them? We formalize this
question through a novel framework we call optimization from samples (OPS). In
OPS, we are given sampled values of a function drawn from some distribution and
the objective is to optimize the function under some constraint.
While there are interesting classes of functions that can be optimized from
samples, our main result is an impossibility. We show that there are classes of
functions which are statistically learnable and optimizable, but for which no
reasonable approximation for optimization from samples is achievable. In
particular, our main result shows that there is no constant factor
approximation for maximizing coverage functions under a cardinality constraint
using polynomially-many samples drawn from any distribution.
We also show tight approximation guarantees for maximization under a
cardinality constraint of several interesting classes of functions including
unit-demand, additive, and general monotone submodular functions, as well as a
constant factor approximation for monotone submodular functions with bounded
curvature
A disposable bio-nano-chip usuing agarose beads for protein analysis
This thesis reports on the fabrication of a disposable bio-nano-chip (BNC), a microfluidic device composed of polydimethylsiloxane (PDMS) and thiolene-based optical epoxy which is both cost-effective and suitable for high performance immunoassays. A novel room temperature (RT) bonding technique was utilized so as to achieve irreversible covalent bonding between PDMS and thiolene-based epoxy layers, while at the same time being compatible with the insertion of agarose bead sensors, selectively arranged in an array of pyramidal microcavities replicated in the thiolene thin film layer. In the sealed device, the bead-supporting epoxy film is sandwiched between two PDMS layers comprising of fluidic injection and drain channels. The agarose bead sensors used in the device are sensitized with anti-C-reactive protein (CRP) antibody, and a fluorescent sandwich-type immunoassay was run to characterize the performance of this device. Computational fluid dynamics (CFD) was used based on the device specifications to model the bead penetration. Experimental data revealed analyte penetration of the immunocomplex to 100μm into the 280μm diameter agarose beads, which correlated well with the simulation. A dose response curve was obtained and the linear dynamic range of the assay was established over 1ng/mL to 50ng/mL with a limit of detection less than 1ng/mL
Shaping Social Activity by Incentivizing Users
Events in an online social network can be categorized roughly into endogenous
events, where users just respond to the actions of their neighbors within the
network, or exogenous events, where users take actions due to drives external
to the network. How much external drive should be provided to each user, such
that the network activity can be steered towards a target state? In this paper,
we model social events using multivariate Hawkes processes, which can capture
both endogenous and exogenous event intensities, and derive a time dependent
linear relation between the intensity of exogenous events and the overall
network activity. Exploiting this connection, we develop a convex optimization
framework for determining the required level of external drive in order for the
network to reach a desired activity level. We experimented with event data
gathered from Twitter, and show that our method can steer the activity of the
network more accurately than alternatives
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