New algorithm tackles stochastic bandits with unknown scale using kurtosis bounds.
problem Existing strategies for stochastic bandits require known scale parameters.
method Develops a scale-free algorithm for stochastic bandits with a bound on kurtosis.
result Generalizes results for Gaussian and uniform distributions to non-parametric setup.
New method for high-dimensional regression with unknown scale parameter.
problem High-dimensional linear regression with unknown scale parameter.
method Penalized Huber M M M -estimator with adaptive Lepski's method. result The method effectively calibrates scale in high-dimensional robust regression.
New method clusters unknown music artists using audio metrics.
problem Disambiguating large catalogs of unknown artists.
method Metric learning from audio data with negative sampling.
result Our method outperforms a classifier-based approach when audio data is available.
Bayesian model learns multiscale interactions in complex systems.
problem Understanding dynamic interplay between processes at different time scales.
method Bayesian learning framework with Particle Gibbs with Ancestor Sampling (PGAS) algorithm.
result Demonstrated the effectiveness of the proposed approach through simulations.
New algorithm reduces robust optimization scale for better constraint satisfaction.
problem Finding robust solutions to optimization problems with unknown constraints.
method Empirical domain reduction to determine robustness scale.
result Our algorithm's scale is less affected by parameter dimensionality.
Optimal nonparametric regression estimator adapts to unknown smoothness.
problem Nonparametric regression with unknown smoothness.
method Constructs an interpolating estimator that adapts to unknown smoothness.
result Minimax optimal rates achieved on Hölder classes.
Detects malware-infected clients and malicious domains using transfer learning.
problem Detecting malware-infected computers and malicious web domains from encrypted HTTPS traffic.
method Transfer learning with sluice networks to bootstrap each other's detection models.
result Outperforms known reference models and detects previously unknown malware and domains.
This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.
problem Designing causal bandit algorithms with unknown causal graphs and stochastic intervention models.
method Establishes novel regret bounds and presents a computationally efficient algorithm for unknown graph and soft interventions.
result Regret bounds for unknown graph and soft interventions, with a universal minimax lower bound.
Efficient algorithm controls unknown systems with adversarial perturbations.
problem Controlling unknown linear systems with adversarial perturbations and convex losses.
method Measures regret against an optimal linear policy, provides efficient algorithm with sublinear regret bound.
result First efficient algorithm with sublinear regret bound of T^{2/3}.
Estimates functions on unknown manifolds using multiscale regression.
problem Regression on unknown low-dimensional manifolds embedded in high-dimensional spaces.
method Low-dimensional coordinates at multiple scales, local polynomial fitting, data-driven wavelet thresholding.
result Optimal learning rates for estimating functions with nonuniform regularity.
The paper presents a method to infer unknown forcing functions in differential equations using Gaussian processes and adjoints.
problem Inferring unknown forcing functions in differential equations from noisy observations.
method Using adjoint methods to efficiently infer Gaussian process (GP) driven differential equations, with truncated basis expansions of the GP kernel.
result Efficient Bayesian inference of forcing functions modeled as GPs using adjoints, with lower computation than MCMC methods.
Algorithm minimizes regret while adhering to unknown safety constraints.
problem Online learning with unknown safety constraints.
method General meta-algorithm leveraging online regression and learning oracles.
result Concrete algorithm with T \sqrt{T} T regret for linear constraints. CMTRF improves recommendation accuracy by transforming rating scales.
problem Non-linear transformation of rating scales disrupts low-rank structure in rating matrices.
method CMTRF performs regression up to unknown monotonic transforms over user segments, coupled with matrix factorization.
result CMTRF outperforms other baselines in synthetic and real-world datasets.
New algorithms achieve logarithmic regret in learning linear quadratic control systems.
problem Learning in Linear Quadratic Control systems with unknown parameters.
method Efficient algorithms for two scenarios: unknown A A A or B B B with certain conditions. result Regret scales logarithmically with the number of steps, not square root.
New algorithm reduces regret bounds for Bayesian optimization with unknown hyperparameters.
problem Optimizing black-box functions with unknown hyperparameters, especially length scale.
method Length Scale Balancing (LB) - aggregating multiple surrogate models with varying length scales.
result LB achieves a regret bound only logaritically away from the oracle algorithm.
Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.
problem Efficient optimisation of expensive black-box functions in unknown search spaces.
method Expands search space over iterations based on a hyperharmonic series, scales to high dimensions.
result Sub-linear regret growth for both algorithms.
A main goal of regression is to derive statistical conclusions on the conditional distribution of the output variable Y given the input values x. Two of the most important characteristics of a single distribution are location and scale. Support vector machines (SVMs) are well established to estimate location functions …
ABCDEFG learns causal graphs from interventional data efficiently.
problem Learning causal graphs from interventional data is challenging.
method Amortized Bayesian Causal Discovery of Extended Factor Graphs (ABCDEFG)
result Estimates a posterior distribution that identifies the true causal graph up to an equivalence class.
Empirical median performs well in estimating location with varying scales.
problem Estimating location with varying scales in data.
method Analysis of empirical median as an estimator.
result Matching upper and lower bounds on estimation error.
c-lasso is a Python tool for robust and sparse regression with linear constraints.
problem Sparse and robust linear regression with linear constraints.
method Estimates coefficients and scale under linear constraints using perspective M-estimators.
result Provides estimators for various loss functions with linear constraints.
New algorithm handles MDPs with unknown, changing rewards efficiently.
problem Handling MDPs with unknown, changing rewards in large state spaces.
method Developed an algorithm with O ( τ ( ln ∣ S ∣ + ln ∣ A ∣ ) T ln ( T ) ) O(\sqrt{τ(\ln|S|+\ln|A|)T}\ln(T)) O ( τ ( ln ∣ S ∣ + ln ∣ A ∣ ) T ln ( T )) regret bound and a modified algorithm with polynomial complexity. result Achieved state-of-the-art regret bounds for large scale MDPs with changing rewards.
The paper tackles high-dimensional mixed linear regression with unknown parameters and proposes methods for estimation, confidence intervals, and hypothesis testing.
problem High-dimensional mixed linear regression with unknown parameters and covariance structure.
method Iterative high-dimensional EM algorithm for estimating regression vectors, debiased estimators for individual coordinates, and large-scale multiple testing procedure.
result Asymptotic normality of debiased estimators and FDR control for hypothesis testing.
DeepONet models system discrepancies with low data.
problem Modeling complex systems with limited data.
method Bi-fidelity modeling using DeepONet for uncertain and partially unknown systems.
result DeepONet effectively models complex systems with parametric uncertainty and partial unknownness.
Robust and fast method for large-scale stochastic optimization.
problem Large-scale stochastic optimization problems.
method Auxiliary variable construction coupled with adaptive inverse Hessian approximation.
result Encouraging performance on real-world problems with millions of observations and unknowns.
New methods adapt conformal prediction to unknown subpopulation shifts.
problem Failure of conformal prediction under unknown subpopulation shifts.
method Proposes new methods that adapt conformal prediction to unknown subpopulation shifts without explicit subpopulation labels.
result Ensures valid coverage guarantees without explicit knowledge of subpopulation structure.
The target of X \mathcal{X} X -armed bandit problem is to find the global maximum of an unknown stochastic function f f f , given a finite budget of n n n evaluations. Recently, X \mathcal{X} X -armed bandits have been widely used in many situations. Many of these applications need to deal with large-scale data sets. To deal with…
Algorithm learns to play against unknown opponents in sequential games.
problem Designing strategies for a learner to interact with an unknown opponent in repeated sequential games.
method Kernel-based regularity assumptions and a novel algorithm combining bilevel optimization and online learning.
result Algorithm achieves sublinear regret guarantees and is effective in specific game settings.
PS4POMDPs algorithm simplifies online learning for episodic POMDPs with unknown models.
problem Learning in POMDPs is harder than in MDPs; online learning is especially challenging.
method Posterior Sampling-based reinforcement learning algorithm (PS4POMDPs)
result Bayesian regret scales as √number of episodes and is polynomial in other parameters.
New algorithm for context bandits with continuous actions.
problem Efficient decision-making with unknown action structures.
method Reduction-style algorithm combining supervised learning.
result Proven to work in general and validated with experiments.
New algorithms reduce contextual bandits' regret without knowing reward noise variances.
problem Reducing regret in contextual bandits with unknown reward noise variances.
method Developed new algorithms based on the optimism principle.
result Regret scales as the square root of the sum of measurement variances, not the time horizon.
Bayesian investor learns unknown asset drift, trades mean-variance optimal portfolio, but policy is robust to observation model distortion.
problem Bayesian portfolio selection with observation model distortion
method Robust Bayesian portfolio selection
result Robust policy and its price are closed form, with price of robustness half the variance of the non-robust investor's loss.
Analyzes generalization error in distributed linear regression.
problem Understanding generalization performance in distributed learning.
method Analytical characterization of generalization error in linear regression with distributed learning.
result Generalization error increases dramatically when nodes estimate close to the number of observations.
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown. In particular, we follow the methodology outlined in \citet{sussman2012universally} to construct consistent estimates for the …
Study uncovers statistical optimality of nonconvex tensor completion methods.
problem Estimating a low-rank tensor from incomplete and corrupted observations.
method Two-stage estimation algorithm for nonconvex optimization.
result Nonconvex tensor completion achieves optimal ℓ 2 \ell_{2} ℓ 2 accuracy. Introduces a new stationary GE-process for gold price analysis.
problem Analyzing gold price data with a flexible stationary process.
method Developed a new stationary GE-process with three parameters. Analyzed synthetic and real gold price data.
result Maximum likelihood estimators can be obtained for the unknown parameters.
New framework recovers reward and rationality parameters from game behavior.
problem Statistical ambiguity in identifying reward and rationality parameters in competitive games.
method Blind Inverse Game Theory (Blind-IGT) using entropy-regularized Quantal Response Equilibrium and Normalized Least Squares (NLS) estimator.
result Optimal convergence rate of O ( N − 1 / 2 ) \mathcal{O}(N^{-1/2}) O ( N − 1/2 ) for joint parameter recovery. A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume t…
A new multi-scale vector quantization method for unsupervised data.
problem Efficiently reconstructing unsupervised data with minimal distortion.
method Reconstruction trees, inspired by decision trees, explore data in a multi-scale fashion.
result Analysis of expected distortion under fixed unknown distribution, with asymptotic and finite sample results.
Algorithm predicts performance of learning in multi-layer networks with matrix-valued hidden variables.
problem Signal recovery and learning in multi-layer neural networks with matrix-valued hidden variables.
method Unified approximation algorithm for MAP and MMSE inference, extending ML-VAMP to handle matrix-valued unknowns.
result Performance of ML-Mat-VAMP algorithm can be predicted in a random large-system limit.
Study online control of unknown time-varying systems with negative and positive results.
problem Online control of time-varying systems with unknown dynamics.
method Algorithmic upper bounds and lower bounds for different policy classes.
result Sublinear adaptive regret bounds for Disturbance Response policies.
Paper recovers latent causal structure and linear transformation from indirect observations.
problem Recovering latent causal structure and linear transformation from indirect observations.
method Established sufficient conditions for DAG recovery, leveraged score function properties, and used soft/hard interventions.
result Perfect recovery of latent DAG structure and linear transformation up to scaling using soft interventions, hard interventions with additional hypothesis testing.
New algorithm minimizes regret in unknown LQ control systems.
problem Online learning in Linear Quadratic Control systems with unknown matrices.
method Developed an online learning algorithm with guarantees on expected regret.
result Upper bounds on regret are minimal and match existing lower bounds.
New algorithm adapts to unknown smoothness in contextual bandits.
problem Adapting to unknown smoothness in non-parametric multi-armed bandits.
method Develops a self-similarity condition-based policy to adapt to unknown smoothness.
result Matches known smoothness case's regret rate for differentiable and non-differentiable payoff functions.
RGI improves robustness of GAN-inversion for image restoration and anomaly detection.
problem Robustness of GAN-inversion to unknown gross corruptions.
method Proposes RGI and R-RGI methods with provable robustness guarantees.
result Restored images and corrupted region masks converge to ground truth under mild assumptions.
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
problem Recovering signals from noisy measurements without prior knowledge of the convolution kernel or signal regularity.
method Parametrizing the convolution kernel and prior length-scales, jointly estimated in the inversion procedure.
result Accurate reconstructions of signals with varying regularity and unknown kernel size.
Certainty equivalent controllers perform nearly optimally in LQ control problems with unknown dynamics.
problem Optimizing control in systems with unknown transition dynamics.
method Analysis of certainty equivalent controllers and comparison to optimal LQ controllers, using perturbation bounds for discrete Riccati equations.
result Sub-optimality gap scales as the square of the parameter error, improving upon previous results.
Study tackles nonlinear factor models with unknown monotone links from incomplete and noisy data.
problem Learning nonlinear factor models with unknown monotone links from incomplete and noisy data.
method Formulated as joint recovery of low-rank factors, loadings, and nonlinear link function; proposed BCD algorithm with regularization.
result Established convergence guarantees and sublinear regret bounds for link-function updates.