36 research outputs found
Relaxations for inference in restricted Boltzmann machines
We propose a relaxation-based approximate inference algorithm that samples
near-MAP configurations of a binary pairwise Markov random field. We experiment
on MAP inference tasks in several restricted Boltzmann machines. We also use
our underlying sampler to estimate the log-partition function of restricted
Boltzmann machines and compare against other sampling-based methods.Comment: ICLR 2014 workshop track submissio
Learning from many trajectories
We initiate a study of supervised learning from many independent sequences
("trajectories") of non-independent covariates, reflecting tasks in sequence
modeling, control, and reinforcement learning. Conceptually, our
multi-trajectory setup sits between two traditional settings in statistical
learning theory: learning from independent examples and learning from a single
auto-correlated sequence. Our conditions for efficient learning generalize the
former setting--trajectories must be non-degenerate in ways that extend
standard requirements for independent examples. They do not require that
trajectories be ergodic, long, nor strictly stable.
For linear least-squares regression, given -dimensional examples produced
by trajectories, each of length , we observe a notable change in
statistical efficiency as the number of trajectories increases from a few
(namely ) to many (namely ). Specifically, we
establish that the worst-case error rate this problem is
whenever . Meanwhile, when , we establish a (sharp)
lower bound of on the worst-case error rate, realized by
a simple, marginally unstable linear dynamical system. A key upshot is that, in
domains where trajectories regularly reset, the error rate eventually behaves
as if all of the examples were independent altogether, drawn from their
marginals. As a corollary of our analysis, we also improve guarantees for the
linear system identification problem
Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
Quantum computing is powerful because unitary operators describing the
time-evolution of a quantum system have exponential size in terms of the number
of qubits present in the system. We develop a new "Singular value
transformation" algorithm capable of harnessing this exponential advantage,
that can apply polynomial transformations to the singular values of a block of
a unitary, generalizing the optimal Hamiltonian simulation results of Low and
Chuang. The proposed quantum circuits have a very simple structure, often give
rise to optimal algorithms and have appealing constant factors, while usually
only use a constant number of ancilla qubits. We show that singular value
transformation leads to novel algorithms. We give an efficient solution to a
certain "non-commutative" measurement problem and propose a new method for
singular value estimation. We also show how to exponentially improve the
complexity of implementing fractional queries to unitaries with a gapped
spectrum. Finally, as a quantum machine learning application we show how to
efficiently implement principal component regression. "Singular value
transformation" is conceptually simple and efficient, and leads to a unified
framework of quantum algorithms incorporating a variety of quantum speed-ups.
We illustrate this by showing how it generalizes a number of prominent quantum
algorithms, including: optimal Hamiltonian simulation, implementing the
Moore-Penrose pseudoinverse with exponential precision, fixed-point amplitude
amplification, robust oblivious amplitude amplification, fast QMA
amplification, fast quantum OR lemma, certain quantum walk results and several
quantum machine learning algorithms. In order to exploit the strengths of the
presented method it is useful to know its limitations too, therefore we also
prove a lower bound on the efficiency of singular value transformation, which
often gives optimal bounds.Comment: 67 pages, 1 figur
Efficient and Modular Implicit Differentiation
Automatic differentiation (autodiff) has revolutionized machine learning. It
allows expressing complex computations by composing elementary ones in creative
ways and removes the burden of computing their derivatives by hand. More
recently, differentiation of optimization problem solutions has attracted
widespread attention with applications such as optimization as a layer, and in
bi-level problems such as hyper-parameter optimization and meta-learning.
However, the formulas for these derivatives often involve case-by-case tedious
mathematical derivations. In this paper, we propose a unified, efficient and
modular approach for implicit differentiation of optimization problems. In our
approach, the user defines (in Python in the case of our implementation) a
function capturing the optimality conditions of the problem to be
differentiated. Once this is done, we leverage autodiff of and implicit
differentiation to automatically differentiate the optimization problem. Our
approach thus combines the benefits of implicit differentiation and autodiff.
It is efficient as it can be added on top of any state-of-the-art solver and
modular as the optimality condition specification is decoupled from the
implicit differentiation mechanism. We show that seemingly simple principles
allow to recover many recently proposed implicit differentiation methods and
create new ones easily. We demonstrate the ease of formulating and solving
bi-level optimization problems using our framework. We also showcase an
application to the sensitivity analysis of molecular dynamics.Comment: V2: some corrections and link to softwar