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[theory-seminar] Theory Lunch 12/10: Ellen Vitercik

David Wajc wajc at stanford.edu
Thu Dec 10 10:01:22 PST 2020


Reminder: this is happening in two hours.

Cheers,
David

On Wed, 9 Dec 2020 at 09:01, David Wajc <wajc at stanford.edu> wrote:

> Hi all,
>
> The last Theory lunch of the quarter will be happening tomorrow at noon
> (PDT), at our gather space:
> https://gather.town/app/lR6jRBPK44nZ7V68/StanfordTheory (*password:*
>  SongComplexity).
> Ellen will tell us about *How much data is sufficient to learn
> high-performing algorithms?*
>
> *Abstract:*
> Algorithms often have tunable parameters that impact runtime and solution
> quality. For many algorithms used in practice, no parameter setting admits
> a meaningful worst-case guarantee, so the parameters are explicitly made
> available for the user to tune. Alternatively, the parameters may be tuned
> implicitly within the proof of a worst-case approximation ratio or runtime
> bound. Worst-case instances, however, may be rare or nonexistent in
> practice. A growing body of research has demonstrated that data-driven
> algorithm design can lead to significant gains in runtime and solution
> quality. Data-driven algorithm design uses a training set of problem
> instances sampled from an unknown, application-specific distribution and
> returns a parameter setting with strong average performance on the training
> set.
>
> We provide a broadly applicable theory for deriving generalization
> guarantees for data-driven algorithm design, which bound the difference
> between the algorithm's expected performance and its average performance
> over the training set. The challenge is that for many combinatorial
> algorithms, performance is a volatile function of the parameters: slightly
> perturbing the parameters can cause a cascade of changes in the algorithm’s
> behavior. Prior research has proved generalization bounds by employing
> case-by-case analyses of parameterized greedy algorithms, clustering
> algorithms, integer programming algorithms, and selling mechanisms. We
> uncover a unifying structure which we use to prove extremely general
> guarantees, yet we recover the bounds from prior research. Our guarantees
> apply whenever an algorithm's performance is a piecewise-constant, -linear,
> or—more generally—piecewise-structured function of its parameters. As we
> demonstrate, our theory also implies novel bounds for dynamic programming
> algorithms used in computational biology.
>
> This talk is based on joint work with Nina Balcan, Dan DeBlasio, Travis
> Dick, Carl Kingsford, and Tuomas Sandholm.
>
> Cheers,
> David
>
> PS
> *Pro tip:* To join the zoom talk:
> (1) go to the lecture hall,
> (2) grab a seat, and
> (3)* press X to join the zoom lecture*.
>
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