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09/12/2023
Reasoning with Latent Diffusion in Offline Reinforcement Learning
Offline reinforcement learning (RL) holds promise as a means to learn hi...
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07/12/2023
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
Deep reinforcement learning (RL) has shown immense potential for learnin...
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06/16/2023
Enhancing Visual Domain Adaptation with Source Preparation
Robotic Perception in diverse domains such as low-light scenarios, where...
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04/04/2023
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
Robotic solutions for quick disaster response are essential to ensure mi...
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10/05/2022
Cost Aware Asynchronous Multi-Agent Active Search
Multi-agent active search requires autonomous agents to choose sensing a...
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07/21/2022
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Impressive results in natural language processing (NLP) based on the Tra...
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05/20/2022
How Useful are Gradients for OOD Detection Really?
One critical challenge in deploying highly performant machine learning m...
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04/26/2022
BATS: Best Action Trajectory Stitching
The problem of offline reinforcement learning focuses on learning a good...
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03/09/2022
Multi-Agent Active Search using Detection and Location Uncertainty
Active search refers to the task of autonomous robots (agents) detecting...
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02/17/2022
Robust Reinforcement Learning via Genetic Curriculum
Achieving robust performance is crucial when applying deep reinforcement...
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01/14/2022
UGV-UAV Object Geolocation in Unstructured Environments
A robotic system of multiple unmanned ground vehicles (UGVs) and unmanne...
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12/23/2021
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling
One of the preeminent obstacles to scaling multi-agent reinforcement lea...
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05/25/2021
SBEVNet: End-to-End Deep Stereo Layout Estimation
Accurate layout estimation is crucial for planning and navigation in rob...
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01/15/2021
Affordance-based Reinforcement Learning for Urban Driving
Traditional autonomous vehicle pipelines that follow a modular approach ...
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11/09/2020
Multi-Agent Active Search using Realistic Depth-Aware Noise Model
The search for objects of interest in an unknown environment by making d...
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11/09/2020
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
For autonomous vehicles, effective behavior planning is crucial to ensur...
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08/14/2020
Interactive Visualization for Debugging RL
Visualization tools for supervised learning allow users to interpret, in...
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07/10/2020
Vizarel: A System to Help Better Understand RL Agents
Visualization tools for supervised learning have allowed users to interp...
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06/25/2020
Asynchronous Multi Agent Active Search
Active search refers to the problem of efficiently locating targets in a...
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11/09/2019
Human Driver Behavior Prediction based on UrbanFlow
How autonomous vehicles and human drivers share public transportation sy...
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11/09/2019
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
In this work, we propose a hierarchical reinforcement learning (HRL) str...
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08/05/2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
We describe ChemBO, a Bayesian Optimization framework for generating and...
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08/01/2019
Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories
Self-driving vehicles (SDVs) hold great potential for improving traffic ...
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06/20/2019
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
Following detection and tracking of traffic actors, prediction of their ...
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03/15/2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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01/31/2019
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensive-to-query function is a common task in science an...
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11/08/2018
Labeling Bias in Galaxy Morphologies
We present a metric to quantify systematic labeling bias in galaxy morph...
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09/18/2018
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Autonomous driving presents one of the largest problems that the robotic...
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08/17/2018
Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks
Despite its ubiquity in our daily lives, AI is only just starting to mak...
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08/17/2018
Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks
Recent algorithmic improvements and hardware breakthroughs resulted in a...
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05/25/2018
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of...
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02/11/2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optim...
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01/30/2018
Transformation Autoregressive Networks
The fundamental task of general density estimation has been of keen inte...
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11/06/2017
Estimating Cosmological Parameters from the Dark Matter Distribution
A grand challenge of the 21st century cosmology is to accurately estimat...
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05/30/2017
Recurrent Estimation of Distributions
This paper presents the recurrent estimation of distributions (RED) for ...
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05/25/2017
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
We design and analyse variations of the classical Thompson sampling (TS)...
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04/30/2017
Scaling Active Search using Linear Similarity Functions
Active Search has become an increasingly useful tool in information retr...
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03/18/2017
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Bandit methods for black-box optimisation, such as Bayesian optimisation...
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03/01/2017
The Statistical Recurrent Unit
Sophisticated gated recurrent neural network architectures like LSTMs an...
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02/27/2017
Equivariance Through Parameter-Sharing
We propose to study equivariance in deep neural networks through paramet...
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02/03/2017
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
A common problem in disciplines of applied Statistics research such as A...
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12/02/2016
Active Search for Sparse Signals with Region Sensing
Autonomous systems can be used to search for sparse signals in a large s...
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11/14/2016
Deep Learning with Sets and Point Clouds
We introduce a simple permutation equivariant layer for deep learning wi...
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09/19/2016
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
Understanding the nature of dark energy, the mysterious force driving th...
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03/20/2016
Multi-fidelity Gaussian Process Bandit Optimisation
In many scientific and engineering applications, we are tasked with the ...
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01/01/2016
Stochastic Neural Networks with Monotonic Activation Functions
We propose a Laplace approximation that creates a stochastic unit from a...
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11/13/2015
Deep Mean Maps
The use of distributions and high-level features from deep architecture ...
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09/24/2015
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Many interesting machine learning problems are best posed by considering...
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06/29/2015
Bayesian Nonparametric Kernel-Learning
Kernel methods are ubiquitous tools in machine learning. They have prove...
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06/09/2015