Upcoming Events

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Events Archive

Can learning theory resist deep learning?

Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world...
Location: CS 105
Speaker(s):

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Stochastic iterative methods lie at the core of large-scale optimization and its modern applications to data science. Though such algorithms are routinely and successfully used in practice on highly irregular problems (e.g. deep neural networks), few performance guarantees are available outside of smooth or convex settings. In this talk, I will...
Location: B205 Engineering Quadrangle
Speaker(s):

Exploration by Optimization in Partial Monitoring

In many real-world problems learners cannot directly observe their own rewards but can still infer whether some particular action is successful. How should a learner take actions to balance its need of information while maximizing their reward in this setting? Partial monitoring is a framework introduced a few decades ago to model learning...

Location: Sherrerd 101
Speaker(s):

Randomized Methods for Low-Rank Tensor Decomposition in Unsupervised Learning

Tensor decomposition discovers latent structure in higher-order data sets and is the higher-order analogue of the matrix decomposition.
Location: 214 Fine Hall
Speaker(s):

Algorithm and Statistical Inference for Recovery of Discrete Structure

Discrete structure recovery is an important topic in modern high-dimensional inference. Examples of discrete structure include clustering labels, ranks of players, and signs of variables in a regression model.
Location: Sherrerd 101
Speaker(s):

Optimizing for Fairness in ML

Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be.
Location: Sherrerd 101
Speaker(s):

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research

We consider the properties and performance of word embeddings techniques in the context of political science research. In particular, we explore key parameter choices—including context window length, embedding vector dimensions and the use of pre-trained vs locally fit variants—with respect to efficiency and quality of inferences possible with...
Location: Corwin Hall, Room 127
Speaker(s):

Machine Learning for the Sciences

Taking place every other Friday. Lunch will be provided.

Location: 26 Prospect Ave, Auditorium 103

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