Thesis Oral Defense - Dravyansh Sharma

— 12:00pm

Location:
In Person - Reddy Conference Room, Gates Hillman 4405

Speaker:
DRAVYANSH SHARMA , Ph.D. Candidate, Computer Science Department, Carnegie Mellon University
https://www.cs.cmu.edu/~dravyans/

Data-driven Algorithm Design and Principled Hyperparameter Tuning in Machine Learning

For any new machine learning technique, a large body of research often follows up in order to tune the technique to work suitably for each of the numerous application areas, requiring significant scientific and engineering efforts. Moreover, this typically involves unprincipled approaches for hyperparameter selection without any guarantee on global optimality. Inspired from the recently proposed paradigm of ‘data-driven algorithm design’, this thesis develops first principled hyperparameter tuning techniques for several core machine learning algorithms, including semi-supervised learning, regularized linear regression, robust nearest neighbors and decision trees, with formal near-optimality guarantees in statistical and online learning settings.  

Given multiple problem instances of a learning problem from some problem domain, we develop approaches to learn provably well-tuned parameters over the domain and answer questions related to the number of problem samples needed to learn a well-tuned learning algorithm. In addition, we also develop online learning techniques when the problem instances arrive in a potentially adversarial sequence. Our approaches apply to the following diverse scenarios:

  • selecting graph hyperparameters in semi-supervised learning,
  • setting regularization coefficients in linear regression,
  • controlling the robustness vs. abstention trade-off using parameterized nearest-neighbor algorithms,
  • Splitting and pruning nodes for accurate and interpretable decision trees,
  • meta-learning common parameters for similar tasks, and
  • learning adaptively in changing environments.

In addition to providing techniques for tuning fundamental learning algorithms, we expand the applicability of data driven algorithm design to algorithm families with multiple parameters by developing better online and more computationally efficient techniques. 

Thesis Committee: 

Maria-Florina Balcan (Chair)
Tom M. Mitchell
R. Ravi
Avrim Blum (Toyota Technological Institute at Chicago)
Tim Roughgarden (Columbia University)


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