Professional Work
I currently work at Google where I focus on very large scale machine learning for ad click prediction.
I recently completed Google's
Machine Learning Ninja program where worked on models for facial recognition, and investigated large margin methods for deep learning (published at NeuIPS 2018).
Research Background
My doctoral work focused on preference elicitation techniques for sequential decision making—and intersects Decision Making, Probablistic Modeling, Machine Learning and Optimization.
Select Publications
Preprint
Second Order Optimization Made Practical
R. Anil, V. Gupta, T. Koren, K. Regan and Y. Singer
arXiv Preprint
Optimization in machine learning, both theoretical and applied, is presently
dominated by first-order gradient methods such as stochastic gradient descent.
Second-order optimization methods that involve second-order derivatives and/or
second-order statistics of the data have become far less prevalent despite strong
theoretical properties, due to their prohibitive computation, memory and communication
costs.
In an attempt to bridge this gap between theoretical and practical optimization, we
present a proof-of-concept distributed system implementation of a second-order preconditioned
method (specifically, a variant of full-matrix Adagrad), that along with a few yet
critical algorithmic and numerical improvements, provides significant practical gains
in convergence on state-of-the-art deep models and gives rise to actual wall-time
improvements in practice compared to conventional first-order methods. Our design
effectively utilizes the prevalent heterogeneous hardware architecture for training
deep models which consists of a multicore CPU coupled with multiple accelerator units.
We demonstrate superior performance on very large learning problems in machine translation
where our distributed implementation runs considerably faster than existing gradient-based
methods.
NeurIPS 18
Large Margin Deep Networks for Classification
H. Mobahi, D. Krishnan, G. Elsayed, K. Regan and S. Bengio
Thirty-second Conference on Neural Information Processing Systems
We present a formulation of deep learning that aims at producing a large margin
classifier. The notion of margin, minimum distance to a decision boundary, has
served as the foundation of several theoretically profound and empirically suc-
cessful results for both classification and regression tasks. However, most large
margin algorithms are applicable only to shallow models with a preset feature
representation; and conventional margin methods for neural networks only enforce
margin at the output layer. Such methods are therefore not well suited for deep
networks. In this work, we propose a novel loss function to impose a margin on
any chosen set of layers of a deep network (including input and hidden layers).
Our formulation allows choosing any lp norm (p >= 1) on the metric measuring
the margin. We demonstrate that the decision boundary obtained by our loss has
nice properties compared to standard classification loss functions. Specifically, we
show improved empirical results on the MNIST, CIFAR-10 and ImageNet datasets
on multiple tasks: generalization from small training sets, corrupted labels, and
robustness against adversarial perturbations. The resulting loss is general and
complementary to existing data augmentation (such as random/adversarial input
transform) and regularization techniques (weight decay, dropout, and batch norm).
@inproceedings{elsayed2018large,
title={Large margin deep networks for classification},
author={Elsayed, Gamaleldin and Krishnan, Dilip and Mobahi, Hossein and Regan, Kevin and Bengio, Samy},
booktitle={Advances in neural information processing systems},
pages={842--852},
year={2018}
}
IJCAI 11
Robust Online Optimization of Reward-uncertain MDPs
Kevin Regan and Craig Boutilier
Twenty-Second Joint Conference on Artificial Intelligence
Imprecise-reward Markov decision processes (IRMDPs) are MDPs in which the
reward function is only partially specified (e.g., by some elicitation process).
Recent work using minimax regret to solve IRMDPs has shown, despite theoretical
intractability, how the set of policies that are nondom- inated w.r.t. reward
uncertainty can be exploited to accelerate regret computation. However,
the number of nondominated policies is generally so large as to undermine this
leverage. In this paper, we show how the quality of the approximation can be
improved online by pruning/adding nondominated policies during reward
elicitation, while maintain- ing computational tractability. Drawing insights
from the POMDP literature, we also develop a new anytime algorithm for
constructing the set of nondominated policies with provable (anytime)
error bounds.
@article{ijcai2011:rb:a,
author = {Kevin Regan and Craig Boutilier},
title = {
Robust Online Optimization of Reward-uncertain MDPs
},
journal = {
Twenty-Second Joint Conference on Artificial Intelligence (IJCAI 2011)
},
year = {2011}
}
ICML 09
Online Feature Elicitation in Interactive Optimization
Craig Boutilier, Kevin Regan and Paolo Viappiani
International Conference on Machine Learning
Most models of utility elicitation in decision support
and interactive optimization assume a predefined set of
"catalog" features over which user
preferences are expressed. However, users may
differ in the features over which they are most
comfortable expressing their preferences. In this
work we consider the problem of feature elicitation:
a user's utility function is expressed using
features whose definitions (in terms of "catalog"
features) are unknown. We cast this as a
problem of concept learning, but whose goal is
to identify only enough about the concept to
enable a good decision to be recommended. We
describe computational procedures for identifying
optimal alternatives w.r.t. minimax regret in
the presence of concept uncertainty; and describe
several heuristic query strategies that focus on
reduction of relevant concept uncertainty.
@article{icml09brv,
author = {Craig Boutilier, Kevin Regan and Paolo Viappiani},
title = {
Online Feature Elicitation in Interactive Optimization
},
journal = {
ICML-09 Iternational Conference on Machine Learning
},
year = {2009}
}
UAI 09
Regret-based Reward Elicitation for Markov Decision Processes
Kevin Regan and Craig Boutilier
The 25th Conference on Uncertainty in Artificial Intelligence
The specification of a Markov decision process (MDP) can
be difficult. Reward function specification is especially
problematic; in practice, it is often cognitively complex and
time-consuming for users to precisely specify rewards. This
work casts the problem of specifying rewards as one of preference
elicitation and aims to minimize the degree of precision with
which a reward function must be specified while still allowing
optimal or near-optimal policies to be produced. We first
discuss how robust policies can be
computed for MDPs given only partial reward information using the
minimax regret criterion. We then demonstrate how regret can be
reduced by efficiently eliciting reward information using
bound queries, using regret-reduction as a means for choosing suitable
queries. Empirical results demonstrate that regret-based reward
elicitation offers an effective way to produce near-optimal policies
without resorting to the precise specification of the entire reward
function.
@article{uai09rb,
author = {Kevin Regan and Craig Boutilier},
title = {
Regret-based Reward Elicitation for Markov Decision Processes
},
journal = {
UAI-09 The 25th Conference on Uncertainty in Artificial Intelligence
},
year = {2009}
}