<div dir="auto"><div>Team, please mark your calendars for this intellectual feast brought to us generously by Kin.<div dir="auto"><br></div><div dir="auto">Cheers,</div><div dir="auto">Artur</div><div dir="auto"><br></div><div dir="auto">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!</div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Diane Stidle</strong> <span dir="auto"><<a href="mailto:stidle@andrew.cmu.edu">stidle@andrew.cmu.edu</a>></span><br>Date: Mon, Apr 17, 2023, 3:41 PM<br>Subject: Thesis Defense - May 3, 2023 - Kin Gutierrez Olivares - Applied Mathematics of the Future<br>To: <a href="mailto:ml-seminar@cs.cmu.edu">ml-seminar@cs.cmu.edu</a> <<a href="mailto:ML-SEMINAR@cs.cmu.edu">ML-SEMINAR@cs.cmu.edu</a>>, <<a href="mailto:stine@wharton.upenn.edu">stine@wharton.upenn.edu</a>>, <<a href="mailto:robertastine@icloud.com">robertastine@icloud.com</a>><br></div><br><br>
<div>
<p><i><b>Thesis Defense</b></i></p>
<p>Date: May 3, 2023<br>
Time: 12:30pm (EST) - Remote only presentation<br>
PhD Candidate: Kin Gutierrez Olivares</p>
<p><b>Title: </b><b>Applied Mathematics of the Future</b></p>
<p>Abstract:<br>
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.<br>
<br>
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.</p>
<p><b>Thesis Committee:</b><br>
Artur Dubrawski (Chair)<br>
Barnabas Poczos<br>
Russ Salakhutdinov<br>
Robert A. Stine (University of Pennsylvania)</p>
<p>Link to the draft document:<br>
<a href="https://drive.google.com/drive/folders/1PZX-1cFVy83M5McOL16WPHIUJYeHEcxn?usp=share_link" target="_blank" rel="noreferrer">https://drive.google.com/drive/folders/1PZX-1cFVy83M5McOL16WPHIUJYeHEcxn?usp=share_link</a></p>
<p>Zoom meeting link:<br>
<a href="https://cmu.zoom.us/j/96712150727?pwd=UXJIOGorbXZjbEtKdWVPdEFTV3ZPUT09" target="_blank" rel="noreferrer">https://cmu.zoom.us/j/96712150727?pwd=UXJIOGorbXZjbEtKdWVPdEFTV3ZPUT09</a></p>
<pre cols="72">--
Diane Stidle
PhD Program Manager
Machine Learning Department
Carnegie Mellon University
<a href="mailto:stidle@andrew.cmu.edu" target="_blank" rel="noreferrer">stidle@andrew.cmu.edu</a></pre>
</div>
</div></div></div>