Search Results for author:
Found 15 papers, 14 papers with code
Structured World Representations in Maze-Solving Transformers
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers.
A Configurable Library for Generating and Manipulating Maze Datasets
Understanding how machine learning models respond to distributional shifts is a key research challenge.
Learning to Solve Integer Linear Programs with Davis-Yin Splitting
In many applications, a combinatorial problem must be repeatedly solved with similar, but distinct parameters.
Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
Instead of tuning $\alpha$, we repeatedly solve the optimization problem for a fixed $\alpha$ effectively performing a JKO update with a time-step $\alpha$.
Explainable AI via Learning to Optimize
Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Operator Splitting for Learning to Predict Equilibria in Convex Games
Systems of competing agents can often be modeled as games.
Feasibility-based Fixed Point Networks
This is accomplished using feasibility-based fixed point networks (F-FPNs).
JFB: Jacobian-Free Backpropagation for Implicit Networks
Unlike traditional networks, implicit networks solve a fixed point equation to compute inferences.
A Neural Network Approach Applied to Multi-Agent Optimal Control
Our approach is grid-free and scales efficiently to dimensions where grids become impractical or infeasible.
Optimization and Control
Wasserstein-based Projections with Applications to Inverse Problems
To bridge this gap, we present a new algorithm that takes samples from the manifold of true data as input and outputs an approximation of the projection operator onto this manifold.
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
On five high-dimensional density estimation and generative modeling tasks, OT-Flow performs competitively to state-of-the-art CNFs while on average requiring one-fourth of the number of weights with an 8x speedup in training time and 24x speedup in inference.
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization
We present an interior point method to solve the quadratic projection problem efficiently.
Numerical Analysis Numerical Analysis
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games
By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN).
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems
State-of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse-of-dimensionality.
ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic Regression
Solution of the least-squares problem can be be accelerated by pre-computing a factorization or preconditioner, and the separability in the smooth, convex problem can be easily parallelized across examples.
