25 research outputs found
Faster Predict-and-Optimize with Davis-Yin Splitting
In many applications, a combinatorial problem must be repeatedly solved with
similar, but distinct parameters. Yet, the parameters are not directly
observed; only contextual data that correlates with is available. It is
tempting to use a neural network to predict given , but training such a
model requires reconciling the discrete nature of combinatorial optimization
with the gradient-based frameworks used to train neural networks. When the
problem in question is an Integer Linear Program (ILP), one approach to
overcoming this issue is to consider a continuous relaxation of the
combinatorial problem. While existing methods utilizing this approach have
shown to be highly effective on small problems (10-100 variables), they do not
scale well to large problems. In this work, we draw on ideas from modern convex
optimization to design a network and training scheme which scales effortlessly
to problems with thousands of variables
ViT-A*: Legged Robot Path Planning using Vision Transformer A*
Legged robots, particularly quadrupeds, offer
promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance.
This paper addresses the challenge of enabling legged robots
to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose
an approach that utilizes differentiable planners, allowing the
learning of end-to-end global plans via a neural network for
commanding quadruped robots. The approach leverages 2D
maps and obstacle specifications as inputs to generate a global
path. To enhance the functionality of the developed neural
network-based path planner, we use Vision Transformers (ViT)
for map pre-processing, to enable the effective handling of
larger maps. Experimental evaluations on two real robotic
quadrupeds (Boston Dynamics Spot and Unitree Go1) demonstrate the effectiveness and versatility of the proposed approach
in generating reliable path plans
Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem
We study the problem of learning a function that maps context observations
(input) to parameters of a submodular function (output). Our motivating case
study is a specific type of vehicle routing problem, in which a team of
Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to
recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent
monitoring tasks. {We want to learn the mapping from observations of UAV task
routes and wind field to the parameters of a submodular objective function,
which describes the distribution of landing positions of the UAVs .}
Traditionally, such a learning problem is solved independently as a prediction
phase without considering the downstream task optimization phase. However, the
loss function used in prediction may be misaligned with our final goal, i.e., a
good routing decision. Good performance in the isolated prediction phase does
not necessarily lead to good decisions in the downstream routing task. In this
paper, we propose a framework that incorporates task optimization as a
differentiable layer in the prediction phase. Our framework allows end-to-end
training of the prediction model without using engineered intermediate loss
that is targeted only at the prediction performance. In the proposed framework,
task optimization (submodular maximization) is made differentiable by
introducing stochastic perturbations into deterministic algorithms (i.e.,
stochastic smoothing). We demonstrate the efficacy of the proposed framework
using synthetic data. Experimental results of the mobile charging station
routing problem show that the proposed framework can result in better routing
decisions, e.g. the average number of UAVs recharged increases, compared to the
prediction-optimization separate approach.Comment: camera-ready version for IROS 202
Contrastive Losses and Solution Caching for Predict-and-Optimize
Many decision-making processes involve solving a combinatorial optimization
problem with uncertain input that can be estimated from historic data.
Recently, problems in this class have been successfully addressed via
end-to-end learning approaches, which rely on solving one optimization problem
for each training instance at every epoch. In this context, we provide two
distinct contributions. First, we use a Noise Contrastive approach to motivate
a family of surrogate loss functions, based on viewing non-optimal solutions as
negative examples. Second, we address a major bottleneck of all
predict-and-optimize approaches, i.e. the need to frequently recompute optimal
solutions at training time. This is done via a solver-agnostic solution caching
scheme, and by replacing optimization calls with a lookup in the solution
cache. The method is formally based on an inner approximation of the feasible
space and, combined with a cache lookup strategy, provides a controllable
trade-off between training time and accuracy of the loss approximation. We
empirically show that even a very slow growth rate is enough to match the
quality of state-of-the-art methods, at a fraction of the computational cost.Comment: Accepted at IJCAI202
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
Latent structure models are a powerful tool for modeling language data: they
can mitigate the error propagation and annotation bottleneck in pipeline
systems, while simultaneously uncovering linguistic insights about the data.
One challenge with end-to-end training of these models is the argmax operation,
which has null gradient. In this paper, we focus on surrogate gradients, a
popular strategy to deal with this problem. We explore latent structure
learning through the angle of pulling back the downstream learning objective.
In this paradigm, we discover a principled motivation for both the
straight-through estimator (STE) as well as the recently-proposed SPIGOT - a
variant of STE for structured models. Our perspective leads to new algorithms
in the same family. We empirically compare the known and the novel pulled-back
estimators against the popular alternatives, yielding new insight for
practitioners and revealing intriguing failure cases.Comment: EMNLP 202