Thesis Defense - May 3, 2023 - Kin Gutierrez Olivares - Applied Mathematics of the Future

[Artur Dubrawski]

After an intellectual feast we were provided by Chirag today in his thesis
defense, we are going to double the load tomorrow with Kin's defense!

Just a reminder...

See you all there tomorrow (on zoom only).

Cheers,
Artur

PS This major event frequency matches the previous record of the Auton Lab:
producing two doctors in one week :)

On Mon, Apr 17, 2023 at 4:33 PM Artur Dubrawski <awd at cs.cmu.edu> wrote:

> Team, please mark your calendars for this intellectual feast brought to us
> generously by Kin.
>
> Cheers,
> Artur
>
> PS The first week of May will be loaded with events for us. Expect at
> least one more phd thesis defense plus at least two new thesis proposals
> coming out of our Lab that week!
>
>
> ---------- Forwarded message ---------
> From: Diane Stidle <stidle at andrew.cmu.edu>
> Date: Mon, Apr 17, 2023, 3:41 PM
> Subject: Thesis Defense - May 3, 2023 - Kin Gutierrez Olivares - Applied
> Mathematics of the Future
> To: ml-seminar at cs.cmu.edu <ML-SEMINAR at cs.cmu.edu>, <
> stine at wharton.upenn.edu>, <robertastine at icloud.com>
>
>
> *Thesis Defense*
>
> Date: May 3, 2023
> Time: 12:30pm (EST) - Remote only presentation
> PhD Candidate: Kin Gutierrez Olivares
>
> *Title: **Applied Mathematics of the Future*
>
> Abstract:
> Novel learning algorithms have enhanced our ability to acquire knowledge
> solely from past observations of single events to learn from the
> observations of several related events. This ability to leverage shared
> useful information across time series is causing a paradigm shift in the
> time-series forecasting practice. Still, machine learning-based forecasting
> faces pressing challenges that limit its usability, usefulness, and
> attainable real-world impact, including human interpretability, the ability
> to leverage structured information, generalization capabilities, and
> computational costs. This thesis tackles these challenges by bridging the
> gap between machine learning and classic statistical forecasting methods.
>
> We organized the thesis as follows. We introduce the time-series
> forecasting task, accompanied by a short review of modern forecasting
> models, their optimization, and forecast evaluation methods. In the
> following chapters, we present our approach with three case studies. First,
> we augment state-of-the-art neural forecasting algorithms with
> interpretability capabilities inspired by time series decomposition
> analysis; we illustrate its application in the short-term electricity price
> forecasting task. Second, we improve neural forecasting generalization and
> computational efficiency in the long-horizon setting through a novel
> wavelet-inspired algorithm that assembles its predictions sequentially,
> emphasizing components with different frequencies and scales. Third, we
> tackle the hierarchical forecasting task, a regression problem with linear
> aggregation constraints, by augmenting neural forecasting architectures
> with a specialized probability mixture capable of incorporating the
> aggregation constraints in its construction. Our approach improves upon the
> current state-of-the-art in each of the considered domains.
>
> *Thesis Committee:*
> Artur Dubrawski (Chair)
> Barnabas Poczos
> Russ Salakhutdinov
> Robert A. Stine (University of Pennsylvania)
>
> Link to the draft document:
>
> https://drive.google.com/drive/folders/1PZX-1cFVy83M5McOL16WPHIUJYeHEcxn?usp=share_link
>
> Zoom meeting link:
> https://cmu.zoom.us/j/96712150727?pwd=UXJIOGorbXZjbEtKdWVPdEFTV3ZPUT09
>
> --
> Diane Stidle
> PhD Program Manager
> Machine Learning Department
> Carnegie Mellon Universitystidle at andrew.cmu.edu
>
>
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