21,728 research outputs found
Decentralized projected Riemannian gradient method for smooth optimization on compact submanifolds
We consider the problem of decentralized nonconvex optimization over a
compact submanifold, where each local agent's objective function defined by the
local dataset is smooth. Leveraging the powerful tool of proximal smoothness,
we establish local linear convergence of the projected gradient descent method
with unit step size for solving the consensus problem over the compact
manifold. This serves as the basis for analyzing decentralized algorithms on
manifolds. Then, we propose two decentralized methods, namely the decentralized
projected Riemannian gradient descent (DPRGD) and the decentralized projected
Riemannian gradient tracking (DPRGT) methods. We establish their convergence
rates of and , respectively, to
reach a stationary point. To the best of our knowledge, DPRGT is the first
decentralized algorithm to achieve exact convergence for solving decentralized
optimization over a compact manifold. The key ingredients in the proof are the
Lipschitz-type inequalities of the projection operator on the compact manifold
and smooth functions on the manifold, which could be of independent interest.
Finally, we demonstrate the effectiveness of our proposed methods compared to
state-of-the-art ones through numerical experiments on eigenvalue problems and
low-rank matrix completion.Comment: 32 page
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Federated Recommender Systems (FedRecs) are considered privacy-preserving
techniques to collaboratively learn a recommendation model without sharing user
data. Since all participants can directly influence the systems by uploading
gradients, FedRecs are vulnerable to poisoning attacks of malicious clients.
However, most existing poisoning attacks on FedRecs are either based on some
prior knowledge or with less effectiveness. To reveal the real vulnerability of
FedRecs, in this paper, we present a new poisoning attack method to manipulate
target items' ranks and exposure rates effectively in the top-
recommendation without relying on any prior knowledge. Specifically, our attack
manipulates target items' exposure rate by a group of synthetic malicious users
who upload poisoned gradients considering target items' alternative products.
We conduct extensive experiments with two widely used FedRecs (Fed-NCF and
Fed-LightGCN) on two real-world recommendation datasets. The experimental
results show that our attack can significantly improve the exposure rate of
unpopular target items with extremely fewer malicious users and fewer global
epochs than state-of-the-art attacks. In addition to disclosing the security
hole, we design a novel countermeasure for poisoning attacks on FedRecs.
Specifically, we propose a hierarchical gradient clipping with sparsified
updating to defend against existing poisoning attacks. The empirical results
demonstrate that the proposed defending mechanism improves the robustness of
FedRecs.Comment: This paper has been accepted by SIGIR202
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
Barren plateaus in quantum tensor network optimization
We analyze the barren plateau phenomenon in the variational optimization of quantum circuits inspired by matrix product states (qMPS), tree tensor networks (qTTN), and the multiscale entanglement renormalization ansatz (qMERA). We consider as the cost function the expectation value of a Hamiltonian that is a sum of local terms. For randomly chosen variational parameters we show that the variance of the cost function gradient decreases exponentially with the distance of a Hamiltonian term from the canonical centre in the quantum tensor network. Therefore, as a function of qubit count, for qMPS most gradient variances decrease exponentially and for qTTN as well as qMERA they decrease polynomially. We also show that the calculation of these gradients is exponentially more efficient on a classical computer than on a quantum computer
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems
This paper presents a policy parameterization for learning-based control on
nonlinear, partially-observed dynamical systems. The parameterization is based
on a nonlinear version of the Youla parameterization and the recently proposed
Recurrent Equilibrium Network (REN) class of models. We prove that the
resulting Youla-REN parameterization automatically satisfies stability
(contraction) and user-tunable robustness (Lipschitz) conditions on the
closed-loop system. This means it can be used for safe learning-based control
with no additional constraints or projections required to enforce stability or
robustness. We test the new policy class in simulation on two reinforcement
learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum.
We find that the Youla-REN performs similarly to existing learning-based and
optimal control methods while also ensuring stability and exhibiting improved
robustness to adversarial disturbances
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules
We target the problem of automatically synthesizing proofs of semantic
equivalence between two programs made of sequences of statements. We represent
programs using abstract syntax trees (AST), where a given set of
semantics-preserving rewrite rules can be applied on a specific AST pattern to
generate a transformed and semantically equivalent program. In our system, two
programs are equivalent if there exists a sequence of application of these
rewrite rules that leads to rewriting one program into the other. We propose a
neural network architecture based on a transformer model to generate proofs of
equivalence between program pairs. The system outputs a sequence of rewrites,
and the validity of the sequence is simply checked by verifying it can be
applied. If no valid sequence is produced by the neural network, the system
reports the programs as non-equivalent, ensuring by design no programs may be
incorrectly reported as equivalent. Our system is fully implemented for a given
grammar which can represent straight-line programs with function calls and
multiple types. To efficiently train the system to generate such sequences, we
develop an original incremental training technique, named self-supervised
sample selection. We extensively study the effectiveness of this novel training
approach on proofs of increasing complexity and length. Our system, S4Eq,
achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent
programsComment: 30 pages including appendi
Offline and Online Models for Learning Pairwise Relations in Data
Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pruning
of large-scale deep learning models, i.e., promoting their weight sparsity. As
illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the
potential of improving their generalization ability. At the core of LTH,
iterative magnitude pruning (IMP) is the predominant pruning method to
successfully find 'winning tickets'. Yet, the computation cost of IMP grows
prohibitively as the targeted pruning ratio increases. To reduce the
computation overhead, various efficient 'one-shot' pruning methods have been
developed, but these schemes are usually unable to find winning tickets as good
as IMP. This raises the question of how to close the gap between pruning
accuracy and pruning efficiency? To tackle it, we pursue the algorithmic
advancement of model pruning. Specifically, we formulate the pruning problem
from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the
BLO interpretation provides a technically-grounded optimization base for an
efficient implementation of the pruning-retraining learning paradigm used in
IMP. We also show that the proposed bi-level optimization-oriented pruning
method (termed BiP) is a special class of BLO problems with a bi-linear problem
structure. By leveraging such bi-linearity, we theoretically show that BiP can
be solved as easily as first-order optimization, thus inheriting the
computation efficiency. Through extensive experiments on both structured and
unstructured pruning with 5 model architectures and 4 data sets, we demonstrate
that BiP can find better winning tickets than IMP in most cases, and is
computationally as efficient as the one-shot pruning schemes, demonstrating 2-7
times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems
(NeurIPS 2022
Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data
Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative
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