Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
A tutorial on non-asymptotic system identification methods.
problem Identifying system parameters in linear models.
method Covering technique, Hanson-Wright Inequality, method of self-normalized martingales.
result Streamlined proofs of least-squares based estimator performance.
New findings on complexity limits in fixed budget bandit identification.
problem Determining the best possible error rate for fixed budget bandit identification.
method Analyzing the best non-adaptive sampling procedures and showing the existence of complexities.
result No fixed complexity for certain bandit identification tasks.
ADSGD method speeds up model identification in sparse optimization.
problem Implicit model identification in sparse optimization problems.
method Accelerated Doubly Stochastic Gradient Method (ADSGD) for faster explicit model identification.
result ADSGD achieves faster explicit model identification and improved algorithm efficiency.
This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploiting some of the problem's information theoretic properties. This focus, along with recent advances in Information Theoretic Machine Learning…
Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.
problem Inefficient elimination strategies in bandit identification.
method Adaptive elimination methods that update sampling rules frequently and reduce problem size.
result Adaptive elimination methods achieve better sample complexity and computational efficiency.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
problem Parameter identification and prediction in Volterra integral equations driven by Gaussian noise.
method Improved deep neural networks framework that incorporates inter-output relationships into the loss function.
result The framework enhances parameter estimation accuracy and provides accurate solutions for modeling stochastic systems.
The paper explores how missing data problems are related to causal inference.
problem Missing data in experiments makes causal inference difficult.
method The paper reinterprets missing data as a form of causal inference by considering counterfactual variables.
result Identification assumptions in missing data can be encoded using graphical models of counterfactual and observed variables.
ERFit identifies dynamic equations from data with minimal supervision.
problem Data-driven sparse system identification in science and engineering.
method Entropic Regression method.
result ERFit package simplifies sparse system identification for various applications.
Paper tackles good arm identification in stochastic bandits.
problem Identifying good arms with minimal samples.
method Proposes DGAI, a differentiable algorithm to improve sample complexity.
result DGAI outperforms baseline algorithms in synthetic and real-world datasets.
Optimal best-arm identification in linear bandits reduces sampling budget.
problem Identifying the best arm with fixed confidence in stochastic linear bandits.
method A simple algorithm that tracks an optimal proportion of arm draws, updated as rarely as desired.
result The algorithm's sampling complexity matches known lower bounds, asymptotically almost surely and in expectation.
We study the problem of identifying the top m arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that w…
A new algorithm identifies one of several nearly optimal arms in linear bandits.
problem Identifying one arm that is close to the best arm in linear bandits.
method Developed a procedure to adapt best-arm identification algorithms for ε-best-answer identification in transductive linear stochastic bandits. result Proposed an asymptotically optimal algorithm for ε-best-answer identification. Online algorithm identifies PDEs from noisy data snapshots.
problem Identifying PDEs from sequential solution snapshots.
method Combines weak-form discretization with online proximal gradient descent.
result Efficiently identifies and tracks systems with time-varying coefficients.
Paper tackles best mixed arm identification with cost constraints in bandit models.
problem Finding the best mixed arm with cost constraints in a stochastic bandit model.
method Proposes SFSR algorithm combining successive reject and score-function-based rejection criteria.
result Upper and lower bounds on mis-identification probability show exponential decay with budget.
Geometric families of low-rank covariances improve flexibility and tractability in high dimensions.
problem Interpolating and identifying covariance matrices in high dimensions with limited data.
method Differential geometric construction of low-rank covariance families, interpolation on manifolds, and distance minimization for identification.
result Differential geometric covariance families offer significant flexibility and computational tractability.
Cyclic coordinate descent identifies models in finite time and converges linearly.
problem Model identification in composite nonsmooth optimization problems.
method Cyclic coordinate descent for a wide class of functions.
result Explicit local linear convergence rates for coordinate descent.
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures …
Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.
problem Best arm identification (BAI) problem with limitations of the original KG algorithm.
method Follows the one-step look ahead of KG but chooses the measurement that maximizes the probability of selecting the best arm.
result Improved knowledge gradient (iKG) is asymptotically optimal and easier to extend to variant BAI problems.
Study best arm identification with safety constraints in bandit problems.
problem Real-world decision-making with safety constraints.
method Analyzed linear and monotonic reward and safety constraints, proposed algorithms.
result Guaranteed safe learning in both linear and general reward/safety constraint settings.
This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem wit…
Investigation of the market graph attracts a growing attention in market network analysis. One of the important problem connected with market graph is to identify it from observations. Traditional way for the market graph identification is to use a simple procedure based on statistical estimations of Pearson correlatio…
New method identifies diffusion sources on networks with statistical confidence.
problem Identifying sources of diffusion on networks without restrictive assumptions.
method Statistical framework and confidence set inference approach based on hypothesis testing.
result Efficiently produces a small subset of nodes covering the source node with any confidence level.
We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization. We demonstrate how the design of the cost function promotes a model with either a continuous change in dynamic…
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estima…
Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the cov…
Simplified identification methods for causal inference with arbitrary interventional distributions.
problem Estimating cause-effect relationships from data with experimental interventions.
method Using Single World Intervention Graphs and nested model factorization, we provide algorithms for identifying causal parameters from mixed observational and interventional distributions.
result Our algorithms are complete for certain types of interventional marginal distributions.
Paper tackles dynamic graph topology identification in time-varying graphs.
problem Dynamic graph topology identification in time-varying graphs.
method Proposes an online algorithm for time-varying optimization, with intrinsic temporal regularization.
result Demonstrates performance on Gaussian graphical model problem.
New algorithm achieves near optimal sample complexity for 1-identification problem.
problem Determining if an arm's mean reward is at least a known threshold with high probability.
method Design of Sequential-Exploration-Exploitation (SEE) algorithm with non-asymptotic analysis.
result Achieves near optimality in sample complexity, matching upper and lower bounds up to a polynomial logarithmic factor.
Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimizat…
The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.
problem Identifying the treatment that maximizes the expected natural direct potential outcome (NDPO) in causal mediation analysis.
method Developed a fixed-confidence best-arm identification (BAI) algorithm based on the Track-and-Stop (TaS) framework, using a cutting-set method to solve a semi-infinite optimization problem.
result The proposed algorithm achieves sample-efficient identification with a high-probability correctness guarantee and asymptotic optimality.
New algorithm improves best arm identification in Bayesian settings.
problem Finding the arm with the highest mean in unknown distributions.
method Developed a variant of successive elimination algorithm.
result Achieved optimal performance in Bayesian setting with logarithmic gap.
New algorithms solve complex function optimization problems.
problem Optimizing unknown functions in competitive learning models.
method Proposed F-LCB algorithm based on UCB-type methods for nonlinear optimization.
result Regret upper bounds for the F-LCB algorithm derived from base algorithms' convergence rates.
BAICS identifies best arm with fairness constraints on subpopulations.
problem Identify the best arm while ensuring fairness across subpopulations.
method Formulated and solved BAICS problem, analyzed complexity, designed algorithm.
result Algorithm's sample complexity matches theoretical lower bound.
New algorithm identifies optimal subtrees in fixed-budget tree search.
problem Identifying optimal subtrees in fixed-budget Monte Carlo Tree Search.
method ε-agnostic algorithm for max-min action identification.
result Misidentification probability decays exponentially with sample size.
Proposes SPCA to incorporate structural constraints in model identification.
problem Model identification with partial structural knowledge.
method Structural Principal Component Analysis (SPCA) that leverages structural information.
result Demonstrates improved model estimates using synthetic and industrial data.
Contextual information helps identify the best arm more efficiently.
problem Best arm identification with contextual covariate information.
method Proposed a context-aware version of the 'Track-and-Stop' strategy.
result Expected number of arm draws matches lower bound asymptotically.
Paper unifies subspace identification and DMD for dynamical systems.
problem Estimating dynamical models from data.
method Unified optimization and regression problems for SID and DMD.
result Proves equivalence of SID and DMD for optimal model construction.
Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robust…
We study the problem of identifying the policy space of a learning agent, having access to a set of demonstrations generated by its optimal policy. We introduce an approach based on statistical testing to identify the set of policy parameters the agent can control, within a larger parametric policy space. After present…
Proposes an algorithm for infinite-dimensional sparse learning in system identification.
problem System identification without known model structures.
method Atomic norm regularization and greedy algorithm for solving an infinite-dimensional group lasso problem.
result The proposed algorithm outperforms benchmark methods in impulse response fitting and pole location estimation.
Unified method for learning from selectively labeled data.
problem Classification with selectively labeled data from multiple decision-makers.
method Unified cost-sensitive learning (UCL) approach.
result Unified method for robust classification in selective labeling.
Optimal multi-fidelity best-arm identification reduces cost with better accuracy.
problem Finding the best arm with highest mean reward at minimum cost.
method Gradient-based approach with asymptotically optimal cost complexity.
result Asymptotically optimal cost complexity compared to existing methods.
Study robust best-arm identification in linear bandits with lower bounds and algorithms.
problem Identify a near-optimal robust arm in linear bandits with adversarial actions.
method Propose instance-dependent lower bounds and both static and adaptive bandit algorithms.
result Sample complexity matches the lower bound and algorithms effectively identify robust arms.
Study quantile multi-armed bandits for identifying the best arm with a specified quantile level.
problem Identifying the arm with the highest quantile in multi-armed bandits with private rewards.
method Proposed a (non-private) and differentially private successive elimination algorithms for best-arm identification.
result The proposed algorithms are essentially optimal for quantile bandit problems, with finite sample complexity even for distributions with infinite support-size.
New algorithm optimizes best arm identification with minimal regret.
problem Best arm identification in multi-armed bandit problems.
method Characterized Bayesian simple regret with continuity conditions of prior, proposed a simple algorithm.
result Proposed algorithm achieves rate-optimal Bayesian simple regret.
Sparse Bayesian learning algorithm for estimating interaction kernels in Motsch-Tadmor model.
problem Data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model.
method Variational framework reformulating kernel identification as a subspace identification problem; sparse Bayesian learning algorithm with informative priors.
result Accurate, robust, and interpretable estimation of interaction kernels across various noise levels and data regimes.
Paper tackles best arm identification with cost consideration.
problem Best arm identification with cost consideration in product development.
method Derives a theoretical lower bound and proposes algorithms CTAS and CO.
result Simple algorithms can deliver near-optimal performance.