Upcoming Events

Princeton University is actively monitoring the situation around coronavirus (COVID-19) and the evolving guidance from government and health authorities. The latest guidance for Princeton members and visitors is available on the University’s Emergency Management website

JARROD MCCLEAN - Google Quantum Artificial Intelligence Lab

Thu, Sep 30, 2021, 4:00 pm

Google Quantum Artificial Intelligence Lab

Website Jarrod McClean Website

Thursday, Sep. 30, 2021

Zoom Meeting

Host - Haw Yang

More information and abstract forthcoming.

Location: Virtual Seminar

CSML Poster Session Event

Mon, May 2, 2022, 8:00 am

The annual CSML Poster Session event will be held in person or virtually. Watch this space for further details.

Published date of event is subject to change.

Due date for independent work posters and papers TBA. Please check your email for details.

Events Archive

Variational models and gradient flows for graph clustering

Discrete graph-based variants of the Allen--Cahn and total variation variational models have proven to be successful tools for clustering and classification on graphs. In this talk we will study these models and the gradient flows that are derived from them. We will see deep connections between the various discrete gradient flows as well as...

Real-Time Remote Sensing and Fusion Plasma Control: A Reservoir Computing Approach

Nuclear fusion power is a potential source of safe, non-carbon-emitting and virtually limitless energy. The tokamak is a promising approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. However, plasma instabilities pose an existential threat to a reactor, which has not yet...
Location: Virtual

Convergence of Stochastic Gradient Descent for analytic target functions

In this talk we discuss almost sure convergence of Stochastic Gradient Descent in discrete and continuous time for a given twice continuously-differentiable target function F. In a first step we give assumptions on the step-sizes and perturbation size to ensure convergence of the target value F and gradient f=DF assuming that f is locally Hölder-...

Accelerate Your Code at the Princeton GPU Hackathon, June 2, 8-10, 2021

Graphics Processing Units (GPUs) offer high performance and massive parallelization, but learning how to program GPUs for scientific applications can be daunting.

Location: Virtual Seminarl

Smooth bilevel programming for sparse regularisation

Nonsmooth regularisers are widely used in machine learning for enforcing solution structures (such as the l1 norm for sparsity or the nuclear norm for low rank). State of the art solvers are typically first order methods or coordinate descent methods which handle nonsmoothness by careful smooth approximations and support pruning. In this work, we...

Transport information Bregman divergences

In this talk, we talk about a joint intersection between optimal transport and information geometry. We study Bregman divergences in probability density space embedded with the Wasserstein-2 metric. Several properties and dualities of transport Bregman divergences are provided. In particular, we derive the transport Kullback-Leibler (KL)...

Princeton Research Day 2021

Princeton’s celebration of early-career research and creative work is back in an all-online format.

The efficiency of kernel methods on structured datasets

Inspired by the proposal of tangent kernels of neural networks (NNs), a recent research line aims to design kernels with a better generalization performance on standard datasets. Indeed, a few recent works showed that certain kernel machines perform as well as NNs on certain datasets, despite their separations in specific cases implied by...

CSML Poster Session Event

The annual CSML Poster Session event will be held in person or virtually. Watch this space for further details.

Due date for independent work posters and papers TBA. Please check your email for details.

CSML/Princeton Data Science Grant Presentation

This event is meant to highlight independent projects that Princeton Data Science (PDS) Data Science Grant recipients have been working on throughout this past semester. The grant recipients will each be giving a short presentation detailing their projects, which range from creating a dynamic digital 3D model of Streicker Bridge on Princeton’s...