The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…
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48 results for “Greedy Design”
Greedy policy maximizes information in unknown linear systems.
problem Exploration in unknown linear dynamical systems.
method Online greedy policy maximizing information.
result Competitive performance compared to gradient-based methods.
A new method achieves optimal uniformity in designs with minimal flexibility.
problem Achieving optimal uniformity in designs with minimal flexibility.
method Derive a lower bound on the uniformity constant and use a greedy construction to achieve this bound, then extend the scheme for more flexibility.
result A simple greedy construction achieves the optimal uniformity constant.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of -sparse signals b…
Transforms offline greedy algorithms to online algorithms for combinatorial problems.
problem Online decision-making in time-varying combinatorial environments.
method General framework using Blackwell approachability and Bandit Blackwell approachability.
result Achieves regret in full information setting and regret in bandit setting.
Given a finite family of functions, the goal of model selection aggregation is to construct a procedure that mimics the function from this family that is the closest to an unknown regression function. More precisely, we consider a general regression model with fixed design and measure the distance between functions by …
Automates supervised learning pipeline design with matrix and tensor factorization.
problem Designing effective supervised learning pipelines with many choices.
method Uses matrix and tensor factorization to model pipeline search space and develops greedy experiment design protocols.
result Demonstrates the effectiveness of the approach on real-world classification problems.
Bayesian sOED uses PG reinforcement learning for efficient experiment design.
problem Optimizing sequential experiments for nonlinear models with limited data.
method Formulated as POMDP, solved via PG methods with neural network parameterization.
result Demonstrated advantages over batch and greedy designs in contaminant source inversion.
This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in that…
Greedy MI maximization method outperforms existing approaches in nonlinear models.
problem Maximizing mutual information in nonlinear models with non-Gaussian noise.
method Greedy approaches based on log-Sobolev inequalities for computationally inexpensive MI lower bounds.
result Proposed method outperforms random selection and Gaussian approximations.
We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of thes…
Large-scale L1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. High-performance algorithms and implementations are critical to efficiently solving these problems. Building…
This work improves RLHF performance guarantees using greedy sampling.
problem Learning the KL-regularized target with preference feedback.
method General preference model, greedy sampling.
result Order-wise improvements over existing RLHF performance guarantees.
Greedy algorithms outperform UCB in many-armed bandit problems.
problem Optimizing multi-armed bandit problems with many arms.
method Subsampled UCB and greedy algorithms comparison.
result Greedy algorithms achieve optimal regret in many-armed bandit problems.
A new method combines classical and machine learning PDE solvers efficiently.
problem Combining classical and machine learning PDE solvers to reduce computational cost and improve accuracy.
method Proposes an approximate greedy router to select solvers at each iteration, mimicking a greedy approach.
result Consistently reduces final error and AUC of the error trajectory compared to single-solver baselines and hybrid approaches.
The paper analyzes greedy algorithms for MMD minimization, showing their efficiency and approximation error.
problem Minimizing Maximum Mean Discrepancy (MMD) for probability measure quantization.
method Iterative algorithms including kernel herding, greedy MMD minimization, and Sequential Bayesian Quadrature (SBQ).
result The greedy algorithms have a lower approximation error than SBQ, but are significantly faster.
New methods optimize experiment selection for sequential data, improving model accuracy.
problem Optimizing experiment selection for sequential data in multidimensional cases.
method Adopting greedy experiment selection methods for maximum likelihood estimation.
result Proposed methods produce consistent and asymptotically normal estimators.
New insights into how randomization affects greedy model selection.
problem Understanding the impact of feature subsampling on greedy model selection.
method Investigated greedy forward selection with feature subsampling, proving effects on bias and variance.
result Ensembling with feature subsampling reduces both bias and variance, unlike convex base learners.
Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical …
SGAS improves neural architecture search by choosing and pruning operations greedily.
problem NAS often fails to generalize in final evaluation.
method Divides search into sub-problems and chooses/prunes candidate operations greedily.
result SGAS finds state-of-the-art architectures with minimal computational cost.
In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are amenable to optimization via the standard greedy algorithm. Despite its desirab…
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals. This algorithm is designed to run in a distributed setting, with local message passing, making it communication efficient. It is based on coordinate descent and uses locally greedy u…
Bayesian approach improves -greedy exploration in RL.
problem Improving -greedy exploration in model-free RL.
method Introducing a Bayesian model update for based on BMC.
result Proposed - exttt{BMC} algorithm efficiently balances exploration and exploitation.
Several recent deep neural networks experiments leverage the generalist-specialist paradigm for classification. However, no formal study compared the performance of different clustering algorithms for class assignment. In this paper we perform such a study, suggest slight modifications to the clustering procedures, and…
Proposes a novel neural architecture for sparse coding using learned greedy pursuit.
problem Lack of interpretability in neural network architectures for sparse coding.
method Unfolded and learned version of Orthogonal Matching Pursuit (OMP) algorithm.
result Demonstrates flexibility and efficiency of the Learned Greedy Method (LGM) in various experiments.
This paper develops a coreset method for GNNs that speeds up training on large graphs.
problem Training Graph Neural Networks (GNNs) on large-scale graphs is computationally expensive.
method The paper proposes a spectral greedy coreset (SGGC) method that selects ego-graphs based on spectral embeddings.
result SGGC significantly speeds up GNN training on large graphs and outperforms other coreset methods.
New analysis shows Thompson Sampling can work with greedy approximations in combinatorial bandits.
problem Thompson Sampling's theoretical limits with greedy approximations in combinatorial semi-bandits.
method Study with greedy oracle, providing lower and upper bounds on regret.
result First theoretical results showing TS can work with greedy approximations, breaking misconceptions.
OOMP selects features online for sparse linear regression.
problem Feature selection in high-dimensional sparse linear models.
method Online algorithm that alternates between feature selection and coefficient estimation.
result Theoretical guarantees and computational complexity analysis of OOMP.
Often times, in many design problems, there is a need to select a small set of informative or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization that provides for a formal way to solve such problems, has recently received significant attention from the controls co…
We study the problem of causal structure learning when the experimenter is limited to perform at most non-adaptive experiments of size . We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolve…
A new method compresses conditional distributions of labelled data.
problem No existing method directly compresses the conditional distribution of labelled data.
method Introduce Average Maximum Conditional Mean Discrepancy (AMCMD), derive a closed form estimator, and extend Kernel Herding (KH) to Average Conditional Kernel Herding (ACKH).
result Directly compressing conditional distributions outperforms joint distribution compression and greedy selection.
We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection pr…
Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions on the design matrix. The Bayesian compressive sensing approach r…
Regularization-induced exploration improves contextual bandit performance.
problem Complex reward models in real-world contextual bandits are hard to explore effectively.
method Regularization-induced exploration using stochasticity in cross-validation.
result Regularization-induced exploration leads to reliable exploration in large-scale business environments.
New algorithm for combinatorial bandit problems reduces regret.
problem Optimal selection of sets of arms in bandit problems.
method SGB algorithm with optimized exploration of unselected arms.
result Achieves -regret bound of .
We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…
Greedy algorithm achieves sublinear regret for various distributions.
problem Efficient performance of greedy algorithms in linear contextual bandit problems.
method Introduced Local Anti-Concentration (LAC) condition to ensure sublinear regret.
result Greedy algorithm achieves cumulative expected regret.
Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.
problem Reducing the cost of evaluating alternatives in large-scale screening tasks.
method Propose a top- greedy evaluation mechanism and the EFG- algorithm for efficient budget allocation.
result Prove that EFG- is both sample-optimal and consistent in large-scale virtual screening.
Greedy algorithm optimizes consumption habits with power utility.
problem Optimizing lifetime consumption with habit formation under power utility.
method Developed a greedy algorithm using Monte Carlo simulation.
result Greedy solution is a good approximation to the optimal solution.
A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.
problem Privacy and communication costs in learning Bayesian network structures from decentralized data.
method Fed-Sparse-BNSL, combining differential privacy with greedy updates targeting only a few relevant edges per participant.
result Achieves utility close to non-private baselines while offering stronger privacy and communication efficiency.
Neural net reweighing improves selectivity in molecule binding studies.
problem Improving selectivity in neural net models for molecule binding studies.
method Greedy algorithm to reweight loss function based on Wasserstein distance.
result Proven to make neural net weights approach limiting distribution of another dataset.
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource c…
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problemcs.LG
New distributions allow greedy arm selection in sparse bandit problems.
problem Sparse contextual bandit problem with sparse parameters and feature distributions.
method Introduced new distribution classes and demonstrated that mixtures of these distributions are also greedy-applicable.
result Greedy algorithm applicable to a wider range of arm feature distributions, including those with origin-asymmetric support.
ε-shotgun optimizes expensive function evaluations in parallel.
problem Optimizing expensive black-box functions efficiently.
method ε-greedy batch Bayesian optimisation.
result ε-shotgun outperforms state-of-the-art batch methods.
Simpler ε-greedy with longer action durations improves exploration.
problem Limited exploration capability of ε-greedy in complex domains.
method Temporally extended ε-greedy with repeated actions for random durations.
result Temporally extended ε-greedy outperforms sophisticated methods on various domains.
The paper tackles lifelong learning in multi-armed bandits, aiming to minimize average regret over multiple tasks.
problem Minimizing average regret in multi-armed bandits over multiple tasks.
method Confidence interval tuning of UCB algorithms and greedy algorithms applied to a bandit over bandit approach.
result Empirical improvement over previous work in the mortal bandit problem.
This paper is a follow up to the previous author's paper on convex optimization. In that paper we began the process of adjusting greedy-type algorithms from nonlinear approximation for finding sparse solutions of convex optimization problems. We modified there three the most popular in nonlinear approximation in Banach…
Introduces greedy feature selection for classifier-dependent feature ranking.
problem Feature selection for classification tasks.
method Greedy feature selection, identifying the most important feature at each step based on the selected classifier.
result Theoretical and numerical benefits of greedy feature selection.