[Theory] Re: [Theory Lunch] Subhodh Kotekal

[Alec Sun via Theory]

Please join us for the fourth theory lunch of the quarter today!

*Time: *Wednesday, April 16, 2025, 12pm-1pm in JCL 390

*Speaker:* Andrzej Kaczmarczyk

*Title: *Learning Real-Life Approval Elections

*Abstract*: We study how to learn an approval election, i.e., an election
in which each voter selects which candidates they approve. Specifically, we
focus on the independent approval model (IAM), where each candidate has its
own approval probability and is approved independently of the other ones.
We propose algorithms for learning IAMs and their mixtures from data, using
either maximum likelihood estimation or Bayesian learning. We then apply
these algorithms to a large set of real-life elections. In particular, we
find that single-component models are rarely sufficient to capture the
complexity of real-life data, whereas their mixtures perform well.

On Tue, Apr 8, 2025 at 1:05 PM Alec Sun <alecsun at uchicago.edu> wrote:

> Please join us for the third theory lunch of the quarter tomorrow!
>
> *Time: *Wednesday, April 9, 2025, 12pm-1pm in JCL 390
>
> *Speaker: *Subhodh Kotekal
>
> *Title: *Variance estimation in compound decision theory under boundedness
>
> *Abstract*: The normal means model is often studied under the assumption
> of a known variance. However, ignorance of the variance is a frequent issue
> in applications and basic theoretical questions still remain open in this
> setting. This article establishes that the sharp minimax rate of variance
> estimation in square error is $(\log\log n/\log n)^2$ under arguably the
> most mild assumption imposed for identifiability: bounded means. The
> rate-optimal estimator proposed in this article achieves the optimal rate
> by estimating $O\left(\log n/\log\log n\right)$ cumulants and leveraging a
> variational representation of the noise variance in terms of the cumulants
> of the data distribution. The minimax lower bound involves a moment
> matching construction.
>
> On Tue, Apr 1, 2025 at 1:38 PM Alec Sun <alecsun at uchicago.edu> wrote:
>
>> Please join us for the second theory lunch of the quarter tomorrow!
>>
>> *Time: *Wednesday, April 2, 2025, 12pm-1pm in JCL 390
>>
>> *Speaker: *Dravy Sharma
>>
>> *Title: *Provable tuning of deep learning model hyperparameters
>>
>> *Abstract*: Modern machine learning algorithms, especially deep
>> learning-based techniques, typically involve careful hyperparameter tuning
>> to achieve the best performance. Despite the surge of intense interest in
>> practical techniques like Bayesian optimization and random search-based
>> approaches to automating this laborious and compute-intensive task, the
>> fundamental learning-theoretic complexity of tuning hyperparameters for
>> deep neural networks is poorly understood. Inspired by this glaring gap, we
>> initiate the formal study of hyperparameter tuning complexity in deep
>> learning under a powerful data-driven paradigm. A major difficulty is that
>> the utility function as a function of the hyperparameter is very volatile
>> and furthermore, it is given implicitly by an optimization problem over the
>> model parameters. To tackle this challenge, we employ subtle concepts from
>> differential/algebraic geometry and constrained optimization to show that
>> the learning-theoretic complexity of the corresponding family of utility
>> functions is bounded. We instantiate our results and provide sample
>> complexity bounds for concrete applications—tuning a hyperparameter that
>> interpolates neural activation functions and setting the kernel parameter
>> in graph neural networks. The talk is based on joint work with Nina Balcan
>> and Anh Nguyen.
>>
>> On Tue, Mar 25, 2025, 4:52 PM Alec Sun <alecsun at uchicago.edu> wrote:
>>
>>> Please join us for the first theory lunch of the quarter tomorrow!
>>>
>>> *Time: *Wednesday, March 26, 2025, 12pm-1pm in JCL 390
>>>
>>> *Speaker: *Olga Medrano
>>>
>>> *Title: *Short Visit to Regularity Lemmas
>>>
>>> *Abstract: *In this expository talk, we will overview Szémerédi’s
>>> regularity lemma, a result from extremal graph theory with different
>>> applications. We will then mention some considerations about this result,
>>> including the existence of irregular pairs in the partitions obtained, as
>>> well as the large size of those partitions. Time permitting, we very
>>> briefly reflect on how the proof of Szémerédi's regularity lemma is not
>>> algorithmic and mention a few lines of work that were focused on finding
>>> algorithms to output regular partitions. In the second half of this talk,
>>> we will describe versions of this lemma over certain classes of graphs. In
>>> particular, we state both the Ultra-strong regularity lemma, which works
>>> for the class of graphs of bounded VC dimension, and the Stable regularity
>>> lemma, which works for the class of k-edge stable graphs (namely, those not
>>> containing a bi-induced bipartite graph). We conclude by acknowledging that
>>> algorithmic questions for both results remain open.
>>>
>>
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