46 citations · 191 across the 9 of their papers we have counts for
9 papers
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
Theodore Papalexopoulos, Christian Tjandraatmadja, Ross Anderson +2
Discrete black-box optimization problems are challenging for model-based optimization (MBO) algorithms, such as Bayesian optimization, due to the size of the search space and the n…
Solving Mixed Integer Programs Using Neural Networks
Vinod Nair, Sergey Bartunov, Felix Gimeno +16
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue, Ross Anderson, Christian Tjandraatmadja
Value-function-based methods have long played an important role in reinforcement learning. However, finding the best next action given a value function of arbitrary complexity is n…
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Christian Tjandraatmadja, Ross Anderson, Joey Huchette +3
We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons. Unlike pr…
CAQL: Continuous Action Q-Learning
Moonkyung Ryu, Yinlam Chow, Ross Anderson +2
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains. One challenge in applying Q-learning to continuous-action RL problems, h…
Strong mixed-integer programming formulations for trained neural networks
Ross Anderson, Joey Huchette, Christian Tjandraatmadja +1
We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network. Our formulation requires a single binary v…
Strong mixed-integer programming formulations for trained neural networks
Ross Anderson, Joey Huchette, Will Ma +2
We present strong mixed-integer programming (MIP) formulations for high-dimensional piecewise linear functions that correspond to trained neural networks. These formulations can be…
How Could Polyhedral Theory Harness Deep Learning?
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
The holy grail of deep learning is to come up with an automatic method to design optimal architectures for different applications. In other words, how can we effectively dimension…
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that…