research
∙
07/25/2023
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
We study Bayesian optimization (BO) in high-dimensional and non-stationa...
share
research
∙
03/17/2023
Conformal Generative Modeling on Triangulated Surfaces
We propose conformal generative modeling, a framework for generative mod...
research
∙
03/03/2023
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Linear temporal logic (LTL) offers a simplified way of specifying tasks ...
research
∙
12/14/2022
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Quantifying motion in 3D is important for studying the behavior of human...
research
∙
10/30/2022
FI-ODE: Certified and Robust Forward Invariance in Neural ODEs
We study how to certifiably enforce forward invariance properties in neu...
research
∙
10/10/2022
Neurosymbolic Programming for Science
Neurosymbolic Programming (NP) techniques have the potential to accelera...
research
∙
08/08/2022
POLAR: Preference Optimization and Learning Algorithms for Robotics
Parameter tuning for robotic systems is a time-consuming and challenging...
research
∙
07/21/2022
The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior
Real-world behavior is often shaped by complex interactions between mult...
research
∙
07/12/2022
Compactly Restrictable Metric Policy Optimization Problems
We study policy optimization problems for deterministic Markov decision ...
research
∙
06/20/2022
Policy Optimization with Linear Temporal Logic Constraints
We study the problem of policy optimization (PO) with linear temporal lo...
research
∙
06/16/2022
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Neuroscientists and neuroengineers have long relied on multielectrode ne...
research
∙
05/13/2022
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Executing safe and precise flight maneuvers in dynamic high-speed winds ...
research
∙
04/18/2022
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
This work presents Neural Gaits, a method for learning dynamic walking g...
research
∙
03/22/2022
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Control Barrier Functions (CBFs) have been demonstrated to be a powerful...
research
∙
03/09/2022
MLNav: Learning to Safely Navigate on Martian Terrains
We present MLNav, a learning-enhanced path planning framework for safety...
research
∙
03/02/2022
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
With the increasing prevalence of complex vision-based sensing methods f...
research
∙
02/05/2022
LyaNet: A Lyapunov Framework for Training Neural ODEs
We propose a method for training ordinary differential equations by usin...
research
∙
12/15/2021
Safety-Aware Preference-Based Learning for Safety-Critical Control
Bringing dynamic robots into the wild requires a tenuous balance between...
research
∙
12/09/2021
Self-Supervised Keypoint Discovery in Behavioral Videos
We propose a method for learning the posture and structure of agents fro...
research
∙
11/30/2021
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
Obtaining annotations for large training sets is expensive, especially i...
research
∙
10/08/2021
On the Implicit Biases of Architecture Gradient Descent
Do neural networks generalise because of bias in the functions returned ...
research
∙
09/10/2021
Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics
Generating provably stable walking gaits that yield natural locomotion w...
research
∙
07/28/2021
Unsupervised Learning of Neurosymbolic Encoders
We present a framework for the unsupervised learning of neurosymbolic en...
research
∙
06/11/2021
Interpreting Expert Annotation Differences in Animal Behavior
Hand-annotated data can vary due to factors such as subjective differenc...
research
∙
06/11/2021
Meta-Adaptive Nonlinear Control: Theory and Algorithms
We present an online multi-task learning approach for adaptive nonlinear...
research
∙
06/09/2021
Fine-Grained System Identification of Nonlinear Neural Circuits
We study the problem of sparse nonlinear model recovery of high dimensio...
research
∙
06/09/2021
Learning Pseudo-Backdoors for Mixed Integer Programs
We propose a machine learning approach for quickly solving Mixed Integer...
research
∙
05/13/2021
End-to-End Sequential Sampling and Reconstruction for MR Imaging
Accelerated MRI shortens acquisition time by subsampling in the measurem...
research
∙
04/06/2021
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Multi-agent behavior modeling aims to understand the interactions that o...
research
∙
03/08/2021
Learning Unstable Dynamics with One Minute of Data: A Differentiation-based Gaussian Process Approach
We present a straightforward and efficient way to estimate dynamics mode...
research
∙
03/02/2021
Minimax Model Learning
We present a novel off-policy loss function for learning a transition mo...
research
∙
03/01/2021
Computing the Information Content of Trained Neural Networks
How much information does a learning algorithm extract from the training...
research
∙
02/18/2021
Learning Invariant Representation of Tasks for Robust Surgical State Estimation
Surgical state estimators in robot-assisted surgery (RAS) - especially t...
research
∙
01/17/2021
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Discovering the complete set of causal relations among a group of variab...
research
∙
12/10/2020
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
We present Neural-Swarm2, a learning-based method for motion planning an...
research
∙
11/27/2020
Task Programming: Learning Data Efficient Behavior Representations
Specialized domain knowledge is often necessary to accurately annotate t...
research
∙
11/21/2020
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
Modern nonlinear control theory seeks to endow systems with properties s...
research
∙
11/14/2020
On the Benefits of Early Fusion in Multimodal Representation Learning
Intelligently reasoning about the world often requires integrating data ...
research
∙
11/11/2020
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Enhanced AutoNav (ENav), the baseline surface navigation software for NA...
research
∙
11/09/2020
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
Characterizing what types of exoskeleton gaits are comfortable for users...
research
∙
11/05/2020
Architecture Agnostic Neural Networks
In this paper, we explore an alternate method for synthesizing neural ne...
research
∙
10/20/2020
Iterative Amortized Policy Optimization
Policy networks are a central feature of deep reinforcement learning (RL...
research
∙
10/08/2020
Distributionally Robust Learning for Unsupervised Domain Adaptation
We propose a distributionally robust learning (DRL) method for unsupervi...
research
∙
07/23/2020
Learning Differentiable Programs with Admissible Neural Heuristics
We study the problem of learning differentiable functions expressed as p...
research
∙
07/16/2020
Active Learning under Label Shift
Distribution shift poses a challenge for active data collection in the r...
research
∙
07/08/2020
Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs)...
research
∙
06/28/2020
Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
research
∙
06/25/2020
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
We examine the task of locating a target region among those induced by i...
research
∙
06/25/2020
Learning compositional functions via multiplicative weight updates
Compositionality is a basic structural feature of both biological and ar...
research
∙
06/18/2020
