Upcoming Events

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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.

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Events Archive

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...
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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)...
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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...
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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...

Barriers to Deploying Deep Learning Models During the COVID-19 Pandemic

A promising application for deep learning models is in assisting clinicians with interpreting X-ray and CT scans, especially when treating respiratory diseases. At the onset of the COVID-19 pandemic, radiologists had to quickly learn how to identify a new disease on chest X-rays and CT scans, and use this information to decide how to allocate...
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CITP Reading Group: Recommender Systems (RS)

The goal of the recommender systems (RS) reading group is to gain deeper understanding both of seminal work as well as emerging ideas in the field. Papers will include research on RS algorithm development and evaluation; user-centered design and user studies for RS; fairness, accountability, and explainability in recommendations; and societal...

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

VisualAI lab focuses on bringing together the fields of computer vision, machine learning, human-machine interaction as well as fairness, accountability and transparency. In this talk, we will introduce the general goal of the lab, and how to build an agent that can understand and follow human’s language to perform tasks.
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Machine Learning and Dynamical Systems meet in Reproducing Kernel Hilbert Spaces

Since its inception in the 19th century through the efforts of Poincaré and Lyapunov, the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. From this perspective, the modeling of dynamical processes in applications requires a detailed understanding of the processes to be analyzed. This...
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