Jeff Schneider

research

∙ 09/12/2023

Reasoning with Latent Diffusion in Offline Reinforcement Learning

Offline reinforcement learning (RL) holds promise as a means to learn hi...

0 Siddarth Venkatraman, et al. ∙

research

∙ 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...

0 Ian Char, et al. ∙

research

∙ 06/16/2023

Enhancing Visual Domain Adaptation with Source Preparation

Robotic Perception in diverse domains such as low-light scenarios, where...

0 Anirudha Ramesh, et al. ∙

research

∙ 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...

0 Nikhil Angad Bakshi, et al. ∙

research

∙ 10/05/2022

Cost Aware Asynchronous Multi-Agent Active Search

Multi-agent active search requires autonomous agents to choose sensing a...

7 Arundhati Banerjee, et al. ∙

research

∙ 07/21/2022

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

Impressive results in natural language processing (NLP) based on the Tra...

0 Adam Villaflor, et al. ∙

research

∙ 05/20/2022

How Useful are Gradients for OOD Detection Really?

One critical challenge in deploying highly performant machine learning m...

0 Conor Igoe, et al. ∙

research

∙ 04/26/2022

BATS: Best Action Trajectory Stitching

The problem of offline reinforcement learning focuses on learning a good...

0 Ian Char, et al. ∙

research

∙ 03/09/2022

Multi-Agent Active Search using Detection and Location Uncertainty

Active search refers to the task of autonomous robots (agents) detecting...

11 Arundhati Banerjee, et al. ∙

research

∙ 02/17/2022

Robust Reinforcement Learning via Genetic Curriculum

Achieving robust performance is crucial when applying deep reinforcement...

0 Yeeho Song, et al. ∙

research

∙ 01/14/2022

UGV-UAV Object Geolocation in Unstructured Environments

A robotic system of multiple unmanned ground vehicles (UGVs) and unmanne...

0 David Guttendorf, et al. ∙

research

∙ 12/23/2021

Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling

One of the preeminent obstacles to scaling multi-agent reinforcement lea...

8 Benjamin Freed, et al. ∙

research

∙ 05/25/2021

SBEVNet: End-to-End Deep Stereo Layout Estimation

Accurate layout estimation is crucial for planning and navigation in rob...

8 Divam Gupta, et al. ∙

research

∙ 01/15/2021

Affordance-based Reinforcement Learning for Urban Driving

Traditional autonomous vehicle pipelines that follow a modular approach ...

0 Tanmay Agarwal, et al. ∙

research

∙ 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...

0 Ramina Ghods, et al. ∙

research

∙ 11/09/2020

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

For autonomous vehicles, effective behavior planning is crucial to ensur...

0 Zhiqian Qiao, et al. ∙

research

∙ 08/14/2020

Interactive Visualization for Debugging RL

Visualization tools for supervised learning allow users to interpret, in...

0 Shuby Deshpande, et al. ∙

research

∙ 07/10/2020

Vizarel: A System to Help Better Understand RL Agents

Visualization tools for supervised learning have allowed users to interp...

0 Shuby Deshpande, et al. ∙

research

∙ 06/25/2020

Asynchronous Multi Agent Active Search

Active search refers to the problem of efficiently locating targets in a...

0 Ramina Ghods, et al. ∙

research

∙ 11/09/2019

Human Driver Behavior Prediction based on UrbanFlow

How autonomous vehicles and human drivers share public transportation sy...

0 Zhiqian Qiao, et al. ∙

research

∙ 11/09/2019

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning

In this work, we propose a hierarchical reinforcement learning (HRL) str...

0 Zhiqian Qiao, et al. ∙

research

∙ 08/05/2019

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...

0 Ksenia Korovina, et al. ∙

research

∙ 08/01/2019

Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories

Self-driving vehicles (SDVs) hold great potential for improving traffic ...

3 Henggang Cui, et al. ∙

research

∙ 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 ...

6 Fang-Chieh Chou, et al. ∙

research

∙ 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...

14 Kirthevasan Kandasamy, et al. ∙

research

∙ 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...

18 Willie Neiswanger, et al. ∙

research

∙ 11/08/2018

Labeling Bias in Galaxy Morphologies

We present a metric to quantify systematic labeling bias in galaxy morph...

0 Guillermo Cabrera-Vives, et al. ∙

research

∙ 09/18/2018

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

Autonomous driving presents one of the largest problems that the robotic...

0 Henggang Cui, et al. ∙

research

∙ 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...

0 Nemanja Djuric, et al. ∙

research

∙ 08/17/2018

Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks

Recent algorithmic improvements and hardware breakthroughs resulted in a...

0 Nemanja Djuric, et al. ∙

research

∙ 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...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 02/11/2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Bayesian Optimisation (BO) refers to a class of methods for global optim...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 01/30/2018

Transformation Autoregressive Networks

The fundamental task of general density estimation has been of keen inte...

0 Junier B. Oliva, et al. ∙

research

∙ 11/06/2017

Estimating Cosmological Parameters from the Dark Matter Distribution

A grand challenge of the 21st century cosmology is to accurately estimat...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 05/30/2017

Recurrent Estimation of Distributions

This paper presents the recurrent estimation of distributions (RED) for ...

0 Junier B. Oliva, et al. ∙

research

∙ 05/25/2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

We design and analyse variations of the classical Thompson sampling (TS)...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 04/30/2017

Scaling Active Search using Linear Similarity Functions

Active Search has become an increasingly useful tool in information retr...

0 Sibi Venkatesan, et al. ∙

research

∙ 03/18/2017

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Bandit methods for black-box optimisation, such as Bayesian optimisation...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 03/01/2017

The Statistical Recurrent Unit

Sophisticated gated recurrent neural network architectures like LSTMs an...

0 Junier B. Oliva, et al. ∙

research

∙ 02/27/2017

Equivariance Through Parameter-Sharing

We propose to study equivariance in deep neural networks through paramet...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 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...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 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...

0 Yifei Ma, et al. ∙

research

∙ 11/14/2016

Deep Learning with Sets and Point Clouds

We introduce a simple permutation equivariant layer for deep learning wi...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 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...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 03/20/2016

Multi-fidelity Gaussian Process Bandit Optimisation

In many scientific and engineering applications, we are tasked with the ...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 01/01/2016

Stochastic Neural Networks with Monotonic Activation Functions

We propose a Laplace approximation that creates a stochastic unit from a...

0 Siamak Ravanbakhsh, et al. ∙

research

∙ 11/13/2015

Deep Mean Maps

The use of distributions and high-level features from deep architecture ...

0 Junier B. Oliva, et al. ∙

research

∙ 09/24/2015

Linear-time Learning on Distributions with Approximate Kernel Embeddings

Many interesting machine learning problems are best posed by considering...

0 Dougal J. Sutherland, et al. ∙

research

∙ 06/29/2015

Bayesian Nonparametric Kernel-Learning

Kernel methods are ubiquitous tools in machine learning. They have prove...

0 Junier Oliva, et al. ∙

research

∙ 06/09/2015

On the Error of Random Fourier Features

Kernel methods give powerful, flexible, and theoretically grounded appro...

0 Dougal J. Sutherland, et al. ∙

Jeff Schneider

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