Probability Seminar: Difference between revisions
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[[Probability | Back to Probability Group]] | [[Probability | Back to Probability Group]] | ||
* '''When''': Thursdays at 2:30 pm | |||
* '''Where''': 901 Van Vleck Hall | |||
* '''Organizers''': Hanbaek Lyu, Tatyana Shcherbyna, David Clancy | |||
* '''To join the probability seminar mailing list:''' email probsem+subscribe@g-groups.wisc.edu. | |||
* '''To subscribe seminar lunch announcements:''' email lunchwithprobsemspeaker+subscribe@g-groups.wisc.edu | |||
[[Past Seminars]] | [[Past Seminars]] | ||
= | |||
= Spring 2025 = | |||
<b>Thursdays at 2:30 PM either in 901 Van Vleck Hall or on Zoom</b> | <b>Thursdays at 2:30 PM either in 901 Van Vleck Hall or on Zoom</b> | ||
We usually end for questions at 3:20 PM. | We usually end for questions at 3:20 PM. | ||
== | == January 23, 2025: == | ||
''' | No seminar | ||
== January 30, 2025: Promit Ghosal (UChicago) == | |||
'''Bridging Theory and Practice in Stein Variational Gradient Descent: Gaussian Approximations, Finite-Particle Rates, and Beyond''' | |||
Stein Variational Gradient Descent (SVGD) has emerged as a powerful interacting particle-based algorithm for nonparametric sampling, yet its theoretical properties remain challenging to unravel. This talk delves into two complementary perspectives about SVGD. First, we explore Gaussian-SVGD, a framework that projects SVGD onto the family of Gaussian distributions via a bilinear kernel. We establish rigorous convergence results for both mean-field dynamics and finite-particle systems, demonstrating linear convergence to equilibrium in strongly log-concave settings and unifying recent algorithms for Gaussian variational inference (GVI) under a single framework. Second, we analyze the finite-particle convergence rates of SVGD in Kernelized Stein Discrepancy (KSD) and Wasserstein-2 metrics. Leveraging a novel decomposition of the relative entropy time derivative, we achieve near-optimal rates with polynomial dimensional dependence and extend these results to bilinear-enhanced kernels. | |||
== February 6, 2025: Subhabrata Sen (Harvard) == | |||
TBD | |||
== February 13, 2025: == | |||
TBD | |||
== February 20, 2025: Mustafa Alper Gunes (Princeton) == | |||
TBD | |||
== | == February 27, 2025: Souvik Dhara (Purdue) == | ||
TBD | |||
== | == March 6, 2025: Alexander Meehan (UW-Madison, Department of Philosophy) == | ||
''' | '''What conditional probability could (probably) be''' | ||
According to orthodox probability theory, when B has probability zero, the conditional probability of A given B can depend on the partition or sub-sigma-field that B is relativized to. This relativization to sub-sigma-fields, a hallmark of Kolmogorov's theory of conditional expectation, is traditionally seen as appropriate in a treatment of conditioning with continuous variables, and it is what allows the theory to preserve Total Disintegrability, a generalization of the Law of Total Probability to uncountable partitions. In this talk, I will argue that although the relativization of conditional probability to sub-sigma-fields has advantages, it also has an underrecognized cost: it leads to puzzles for the treatment of ''iterated conditioning''. I will discuss these puzzles and some possible implications for the foundations of conditional probability. | |||
This talk is based on joint work with Snow Zhang (UC Berkeley). | |||
== | == March 13, 2025: Klara Courteaut (Courant) == | ||
TBD | |||
== | == March 20, 2025: Ewain Gwynne (UChicago) == | ||
TBD | |||
== | == March 27, 2025: SPRING BREAK == | ||
No seminar | |||
== | == April 3, 2025: Jimme He (OSU) == | ||
TBD | |||
== | == April 10, 2025: Evan Sorensen (Columbia) == | ||
TBD | |||
== | == April 17, 2025: == | ||
TBD | |||
== | == April 24, 2025: William Leep (University of Minnesota, Twin Cities) == | ||
TBD | |||
== | == May 1, 2025: == | ||
No seminar | |||
Latest revision as of 23:09, 22 January 2025
- When: Thursdays at 2:30 pm
- Where: 901 Van Vleck Hall
- Organizers: Hanbaek Lyu, Tatyana Shcherbyna, David Clancy
- To join the probability seminar mailing list: email probsem+subscribe@g-groups.wisc.edu.
- To subscribe seminar lunch announcements: email lunchwithprobsemspeaker+subscribe@g-groups.wisc.edu
Spring 2025
Thursdays at 2:30 PM either in 901 Van Vleck Hall or on Zoom
We usually end for questions at 3:20 PM.
January 23, 2025:
No seminar
January 30, 2025: Promit Ghosal (UChicago)
Bridging Theory and Practice in Stein Variational Gradient Descent: Gaussian Approximations, Finite-Particle Rates, and Beyond
Stein Variational Gradient Descent (SVGD) has emerged as a powerful interacting particle-based algorithm for nonparametric sampling, yet its theoretical properties remain challenging to unravel. This talk delves into two complementary perspectives about SVGD. First, we explore Gaussian-SVGD, a framework that projects SVGD onto the family of Gaussian distributions via a bilinear kernel. We establish rigorous convergence results for both mean-field dynamics and finite-particle systems, demonstrating linear convergence to equilibrium in strongly log-concave settings and unifying recent algorithms for Gaussian variational inference (GVI) under a single framework. Second, we analyze the finite-particle convergence rates of SVGD in Kernelized Stein Discrepancy (KSD) and Wasserstein-2 metrics. Leveraging a novel decomposition of the relative entropy time derivative, we achieve near-optimal rates with polynomial dimensional dependence and extend these results to bilinear-enhanced kernels.
February 6, 2025: Subhabrata Sen (Harvard)
TBD
February 13, 2025:
TBD
February 20, 2025: Mustafa Alper Gunes (Princeton)
TBD
February 27, 2025: Souvik Dhara (Purdue)
TBD
March 6, 2025: Alexander Meehan (UW-Madison, Department of Philosophy)
What conditional probability could (probably) be
According to orthodox probability theory, when B has probability zero, the conditional probability of A given B can depend on the partition or sub-sigma-field that B is relativized to. This relativization to sub-sigma-fields, a hallmark of Kolmogorov's theory of conditional expectation, is traditionally seen as appropriate in a treatment of conditioning with continuous variables, and it is what allows the theory to preserve Total Disintegrability, a generalization of the Law of Total Probability to uncountable partitions. In this talk, I will argue that although the relativization of conditional probability to sub-sigma-fields has advantages, it also has an underrecognized cost: it leads to puzzles for the treatment of iterated conditioning. I will discuss these puzzles and some possible implications for the foundations of conditional probability.
This talk is based on joint work with Snow Zhang (UC Berkeley).
March 13, 2025: Klara Courteaut (Courant)
TBD
March 20, 2025: Ewain Gwynne (UChicago)
TBD
March 27, 2025: SPRING BREAK
No seminar
April 3, 2025: Jimme He (OSU)
TBD
April 10, 2025: Evan Sorensen (Columbia)
TBD
April 17, 2025:
TBD
April 24, 2025: William Leep (University of Minnesota, Twin Cities)
TBD
May 1, 2025:
No seminar