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Found 13 papers, 2 papers with code
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
To reduce the need for training data with simulated solutions, we pretrain neural operators on unlabeled PDE data using reconstruction-based proxy tasks.
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods.
MLNav: Learning to Safely Navigate on Martian Terrains
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.
Learning Pseudo-Backdoors for Mixed Integer Programs
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
Learning to Make Decisions via Submodular Regularization
In particular, we focus on a class of combinatorial problems that can be solved via submodular maximization (either directly on the objective function or via submodular surrogates).
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe.
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways.
Policy Optimization by Local Improvement through Search
On the other end of the spectrum, approaches rooted in Policy Iteration, such as Dual Policy Iteration do not choose next step actions based on an expert, but instead use planning or search over the policy to choose an action distribution to train towards.
Co-training for Policy Learning
We study the problem of learning sequential decision-making policies in settings with multiple state-action representations.
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach.
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs?
Learning to Search via Retrospective Imitation
We study the problem of learning a good search policy for combinatorial search spaces.
Onsets and Frames: Dual-Objective Piano Transcription
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames.
