[Colloquium] REMINDER: 2/9 Talks at TTIC: Steve Hanneke

[Mary Marre via Colloquium]

 When:     Friday, February 9th at *10:30am*

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Steve Hanneke


Title: Principles of Active Learning

Abstract:
In many machine learning applications, the effort required to manually
label the massive data sets necessary to train machine learning systems to
a high accuracy presents a major hurdle.  One promising approach to
reducing the required training sample size is active learning, a technique
in which the learning algorithm participates in interactively selecting
examples to be labeled for training, in order to focus the human expert's
efforts on labeling only informative and non-redundant examples.  Active
learning holds great potential for dramatically reducing the number of
labeled training examples needed for learning.  However, despite decades of
research on the subject, the most popular active learning algorithms in the
applications literature are known to be unreliable and sensitive to
violations of modeling assumptions, which has held back the widespread
applicability of active learning in practice.  At the root of this problem,
it seems we have lacked a complete understanding of the basic principles
that should underly the design of good active learning algorithms.  Such a
situation calls for a careful theoretical approach to the problem.

In this talk, I will articulate essential principles for the design of
effective active learning algorithms, distilled from over a decade of
research on the theory of active learning.  Moreover, I will describe a
general active learning strategy based on these principles, which is
provably near-optimal, in the sense that the number of labeled training
examples sufficient to achieve a given accuracy guarantee cannot be
significantly reduced by any other active learning algorithm.  In the
process, I will discuss the fundamental trade-offs and general complexity
measures intrinsic to the active learning setting, and present formulas
expressing the minimum number of labeled examples sufficient and necessary
for an optimal active learning algorithm to achieve a given accuracy
guarantee.


Host: Avrim Blum <avrim at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Fri, Feb 2, 2018 at 3:23 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Friday, February 9th at *10:30am*
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Steve Hanneke
>
>
> Title: Principles of Active Learning
>
> Abstract:
> In many machine learning applications, the effort required to manually
> label the massive data sets necessary to train machine learning systems to
> a high accuracy presents a major hurdle.  One promising approach to
> reducing the required training sample size is active learning, a technique
> in which the learning algorithm participates in interactively selecting
> examples to be labeled for training, in order to focus the human expert's
> efforts on labeling only informative and non-redundant examples.  Active
> learning holds great potential for dramatically reducing the number of
> labeled training examples needed for learning.  However, despite decades of
> research on the subject, the most popular active learning algorithms in the
> applications literature are known to be unreliable and sensitive to
> violations of modeling assumptions, which has held back the widespread
> applicability of active learning in practice.  At the root of this problem,
> it seems we have lacked a complete understanding of the basic principles
> that should underly the design of good active learning algorithms.  Such a
> situation calls for a careful theoretical approach to the problem.
>
> In this talk, I will articulate essential principles for the design of
> effective active learning algorithms, distilled from over a decade of
> research on the theory of active learning.  Moreover, I will describe a
> general active learning strategy based on these principles, which is
> provably near-optimal, in the sense that the number of labeled training
> examples sufficient to achieve a given accuracy guarantee cannot be
> significantly reduced by any other active learning algorithm.  In the
> process, I will discuss the fundamental trade-offs and general complexity
> measures intrinsic to the active learning setting, and present formulas
> expressing the minimum number of labeled examples sufficient and necessary
> for an optimal active learning algorithm to achieve a given accuracy
> guarantee.
>
>
> Host: Avrim Blum <avrim at ttic.edu>
>
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
>
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