Recent & Upcoming Talks

2021

Willie Neiswanger: Going Beyond Global Optima with Bayesian Algorithm Execution

In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function …

Florin Gogianu and Tudor Berariu: TBD

Steve James: Learning Portable Hierarchies for Task-Level Planning

In this work, we tackle the problem of learning symbolic representations of low-level and continuous environments. We present a …

Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes

Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given …

Ruha Benjamin: Reimagining the Default Settings of Technology & Society

From everyday apps to complex algorithms, technology has the potential to hide, speed, and deepen discrimination, while appearing …

César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations

Probabilistic modeling is a core component of modern machine learning. Nevertheless, at least from our local perspective, educational …

Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Que​​​​​​​er Communities

Recent advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore the concerns of the …

Dhruva Tirumala: Using behavior priors for data efficiency in Reinforcement Learning

While reinforcement learning has shown great promise as a viable solution to many problems such as Go and Atari, it’s application …

2020

Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry

When companies, whether start-ups or big corporations, talk about machine learning they usually mean some kind of neural network model. …

Thu Nguyen Phuoc: Neural rendering and inverse rendering using physical inductive biases

Computer graphics focuses on rendering high-quality 2D images from 3D scenes, with most research focusing on simulating elements of the …

Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks

Our streets shape us in more ways than we can imagine. In this talk, I offer a unique view into the promising applications of machine …

Tim G. J. Rudner: Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP …

Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions

As robots move from labs and factories into human-centric spaces, it becomes progressively harder to predetermine the environments, …

Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus?

In their seminal paper in 2002, Joe Cartwright and Mads Huuse referred to 3D seismic reflection data as the ‘Geological Hubble’, …

Yasemin Bekiroglu: Learning and multi-modal sensing for robotic grasping and manipulation

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics

The Earth’s system dynamics has an intrinsically multi-scale and nonlinear nature, which fundamentally affects the ability to …

Laura Mansfield: Can we use machine learning to predict global patterns of climate change?

To achieve long-term climate change goals, such as limiting global warming to 1.5 or 2°C, there must be a global effort to decide and …

So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure

Way too often, observations from weather stations fall outside of the ensemble generated by initial uncertainty in weather models. This …

Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

Keshi He: Toward AI-powered Robots Served for IndustrialProductions and Healthcare

Recent advancement of AI technologies like deep learning or deep reinforcement learning greatly enhance robot’s …

Vincent Adam: Variational inference for Gaussian Process models: multi-GP regression and time-series models

Variational inference (VI) is a versatile framework to perform approximate inference in probabilistic models. Recent progress made it …

Rituraj Kaushik: Data-efficient adaptation in robotics using priors from the simulator

In the real world, a robot might face any unanticipated situation, such as damaged motors, unseen terrain conditions, faults in the …

Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization

This talk will describe a framework for constructing simulation-based kernels for ultra data-efficient Bayesian optimization (BO). We …

Michael Lutter: Inductive Biases for Learning Robot Control

In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be …

2019

Vincent Adam: Doubly Sparse Variational Gaussian Processes

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their …

Samuel Kaski: Probabilistic Modelling with Experts

I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based …

Christian Walder: New Tricks for Estimating Gradients of Expectations

We derive a family of Monte Carlo estimators for gradients of expectations, which is related to the log-derivative trick, but involves …

Emtiyaz Khan: Learning-Algorithms from Bayesian Principle

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive …