18 research outputs found
An Entropy Search Portfolio for Bayesian Optimization
Bayesian optimization is a sample-efficient method for black-box global
optimization. How- ever, the performance of a Bayesian optimization method very
much depends on its exploration strategy, i.e. the choice of acquisition
function, and it is not clear a priori which choice will result in superior
performance. While portfolio methods provide an effective, principled way of
combining a collection of acquisition functions, they are often based on
measures of past performance which can be misleading. To address this issue, we
introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio
construction which is motivated by information theoretic considerations. We
show that ESP outperforms existing portfolio methods on several real and
synthetic problems, including geostatistical datasets and simulated control
tasks. We not only show that ESP is able to offer performance as good as the
best, but unknown, acquisition function, but surprisingly it often gives better
performance. Finally, over a wide range of conditions we find that ESP is
robust to the inclusion of poor acquisition functions.Comment: 10 pages, 5 figure
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box
operations not accessible through traditional techniques. The success of
Bayesian optimization methods such as Expected Improvement (EI) are
significantly affected by the degree of trade-off between exploration and
exploitation. Too much exploration can lead to inefficient optimization
protocols, whilst too much exploitation leaves the protocol open to strong
initial biases, and a high chance of getting stuck in a local minimum.
Typically, a constant margin is used to control this trade-off, which results
in yet another hyper-parameter to be optimized. We propose contextual
improvement as a simple, yet effective heuristic to counter this - achieving a
one-shot optimization strategy. Our proposed heuristic can be swiftly
calculated and improves both the speed and robustness of discovery of optimal
solutions. We demonstrate its effectiveness on both synthetic and real world
problems and explore the unaccounted for uncertainty in the pre-determination
of search hyperparameters controlling explore-exploit trade-off.Comment: Accepted for publication in the proceedings of 2018 Computing
Conferenc
Tuning Word2vec for Large Scale Recommendation Systems
Word2vec is a powerful machine learning tool that emerged from Natural
Lan-guage Processing (NLP) and is now applied in multiple domains, including
recom-mender systems, forecasting, and network analysis. As Word2vec is often
used offthe shelf, we address the question of whether the default
hyperparameters are suit-able for recommender systems. The answer is
emphatically no. In this paper, wefirst elucidate the importance of
hyperparameter optimization and show that un-constrained optimization yields an
average 221% improvement in hit rate over thedefault parameters. However,
unconstrained optimization leads to hyperparametersettings that are very
expensive and not feasible for large scale recommendationtasks. To this end, we
demonstrate 138% average improvement in hit rate with aruntime
budget-constrained hyperparameter optimization. Furthermore, to
makehyperparameter optimization applicable for large scale recommendation
problemswhere the target dataset is too large to search over, we investigate
generalizinghyperparameters settings from samples. We show that applying
constrained hy-perparameter optimization using only a 10% sample of the data
still yields a 91%average improvement in hit rate over the default parameters
when applied to thefull datasets. Finally, we apply hyperparameters learned
using our method of con-strained optimization on a sample to the Who To Follow
recommendation serviceat Twitter and are able to increase follow rates by 15%.Comment: 11 pages, 4 figures, Fourteenth ACM Conference on Recommender System
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.Engineering and Applied Science
Discovering Representations for Black-box Optimization
The encoding of solutions in black-box optimization is a delicate,
handcrafted balance between expressiveness and domain knowledge -- between
exploring a wide variety of solutions, and ensuring that those solutions are
useful. Our main insight is that this process can be automated by generating a
dataset of high-performing solutions with a quality diversity algorithm (here,
MAP-Elites), then learning a representation with a generative model (here, a
Variational Autoencoder) from that dataset. Our second insight is that this
representation can be used to scale quality diversity optimization to higher
dimensions -- but only if we carefully mix solutions generated with the learned
representation and those generated with traditional variation operators. We
demonstrate these capabilities by learning an low-dimensional encoding for the
inverse kinematics of a thousand joint planar arm. The results show that
learned representations make it possible to solve high-dimensional problems
with orders of magnitude fewer evaluations than the standard MAP-Elites, and
that, once solved, the produced encoding can be used for rapid optimization of
novel, but similar, tasks. The presented techniques not only scale up quality
diversity algorithms to high dimensions, but show that black-box optimization
encodings can be automatically learned, rather than hand designed.Comment: Presented at GECCO 2020 -- v2 (Previous title 'Automating
Representation Discovery with MAP-Elites'
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
Many advances that have improved the robustness and efficiency of deep
reinforcement learning (RL) algorithms can, in one way or another, be
understood as introducing additional objectives, or constraints, in the policy
optimization step. This includes ideas as far ranging as exploration bonuses,
entropy regularization, and regularization toward teachers or data priors when
learning from experts or in offline RL. Often, task reward and auxiliary
objectives are in conflict with each other and it is therefore natural to treat
these examples as instances of multi-objective (MO) optimization problems. We
study the principles underlying MORL and introduce a new algorithm,
Distillation of a Mixture of Experts (DiME), that is intuitive and
scale-invariant under some conditions. We highlight its strengths on standard
MO benchmark problems and consider case studies in which we recast offline RL
and learning from experts as MO problems. This leads to a natural algorithmic
formulation that sheds light on the connection between existing approaches. For
offline RL, we use the MO perspective to derive a simple algorithm, that
optimizes for the standard RL objective plus a behavioral cloning term. This
outperforms state-of-the-art on two established offline RL benchmarks