Paper extends conformal prediction to complex survey data.
problem Applying distribution-free prediction intervals to complex survey data.
method Design-based conformal prediction for non-exchangeable data.
result Empirical guarantees of finite-sample coverage for complex survey data.
Unified framework for model-based RL with sample complexity guarantees.
problem Designing efficient posterior sampling methods for model-based RL.
method Optimistic posterior sampling, Hellinger distance reduction, data likelihood measurement.
result Unified algorithms with state-of-the-art sample complexity guarantees.
This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching. In one design, a data sketch is constructed using random column sampling followed by low dimensional embedding, while in the other, sketching is based on random column and row …
Efficient algorithms improve learning of large-margin halfspaces.
problem Learning large-margin halfspaces efficiently and reproducibly.
method Design of efficient, dimension-independent, polynomial-time algorithms; SGD-based approach; DP-to-Replicability reduction.
result Improved sample complexity compared to previous algorithms, with optimal sample complexity for one algorithm.
New method for identifying best designs in vector optimization with uncertain feedback.
problem Optimizing vector-valued outcomes with uncertain preferences.
method Stochastic bandit feedback, polyhedral ordering cone, ( ε , δ ε,δ ε , δ )-PAC Pareto set identification. result Sample complexity characterized and matched by the naïve elimination algorithm.
Study shows sample complexity for logistic regression with normal covariates.
problem Estimating parameters of logistic regression with normal design.
method Analyzes sample complexity in terms of dimension and inverse temperature.
result Shows two change-points in sample complexity curve based on inverse temperature.
New method improves stochastic kriging for high-dimensional simulations.
problem High-dimensional simulation models require prohibitive sample sizes and computational costs.
method Tensor Markov kernels and sparse grid experimental designs.
result Sample complexity grows only slightly with dimensionality, improving accuracy and efficiency.
New model-free RL algorithm tackles robust average-reward problems with finite sample complexity analysis.
problem Long-term decision-making in environments with varying dynamics.
method Proposes Robust Halpern Iteration (RHI) algorithm based on a black-box sampling oracle and multi-level Monte-Carlo estimator.
result Achieves ε-optimal robust policy with sample complexity of O(1/ε^(2+o(1))) under generative model setting.
Study shows k k k -NN regressor consistency in complex survey designs.
problem Lack of consistency results for k k k -NN regressor in complex survey data. method Analysis of regularity conditions on sampling design and data distribution.
result Consistency of k k k -NN regressor under complex survey designs. LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.
problem Optimizing large, complex design spaces is infeasible and unnecessary.
method LES uses Bayesian optimization to target solutions reachable by iterative optimizers.
result LES achieves strong sample efficiency compared to existing methods.
Wedge Sampling improves tensor completion with nearly-linear sample complexity.
problem Efficiently completing low-rank tensors from a subset of entries.
method Non-adaptive wedge sampling to promote structured connections in tensor completion.
result Polynomial-time algorithms achieve weak and exact recovery with nearly linear sample complexity.
A new sampling strategy improves reliability and robustness optimization for complex designs.
problem High sample requirements for optimizing reliability and robustness in complex designs.
method Local Latin Hypercube Refinement (LoLHR) for multi-objective design uncertainty optimization.
result LoLHR achieves better results compared to other surrogate-based strategies.
New insights into how to inspect and learn from multi-stage processes and AI reasoning.
problem Understanding how to attribute outcomes to early stages in multi-stage operations and AI reasoning.
method Information-theoretic analysis and mathematical proofs of four key results.
result Uniform checkpoint spacing is minimax-optimal for inspection design under homogeneous signal attenuation.
A new algorithm reduces sampling complexity for general distributions.
problem Sampling from complex target distributions efficiently.
method Recursive score estimation method for diffusion-based Monte Carlo.
result Gradient complexity improved from exponential to quasi-polynomial.
Study mini-batch SGD noise and its limits, proving complexity guarantees.
problem Analyzing the noise in mini-batch SGD and its impact on optimization.
method Examined the conditional covariance and diffusion limits of SGD under different sampling designs.
result Proved mean-square upper bounds and Fisher van Trees lower bounds for SGD, linking them to effective dimension and condition number.
In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed con…
SDRF estimates complex survey designs for conditional distributions.
problem Estimating conditional distributions under complex survey designs.
method Survey-calibrated distributional random forest (SDRF) with pseudo-population bootstrap and MMD split criterion.
result Established design consistency and model consistency for survey designs.
This paper introduces a new measure to identify model redundancy in compressed CNNs.
problem Identifying remaining model redundancy in compressed CNNs.
method Developed a statistical formulation of CNNs and compressed CNNs via tensor decomposition, revealing discrepancies in sample complexity and model redundancy.
result Introduced a new model redundancy measure, the K / R K/R K / R ratio, for compressed CNNs. Neural Optimal Design of Experiments improves inverse problem solving efficiency.
problem Optimal experimental design in inverse problems.
method Jointly trains a reconstruction model and design variables in a single loop.
result Significantly reduces computational complexity and improves reconstruction accuracy.
New algorithm selects optimal subset for multiclass classifier training.
problem Selecting optimal subset of labeled examples for multiclass classifier training.
method Regret minimization framework with new regularization scheme.
result New method consistently outperforms state-of-the-art approaches.
New algorithm improves active learning in agnostic pool-based classification.
problem Efficient active learning in the agnostic setting with minimized sample complexity.
method Solves an experimental design problem to determine a distribution over examples for label requests.
result Achieves sample complexity bounds never worse than best disagreement coefficient-based bounds, sometimes significantly smaller.
Algorithm identifies Pareto optimal designs efficiently for noisy, multi-objective functions.
problem Optimizing multi-objective functions with noisy data and large design spaces.
method Adaptive discretization and tree-based approach to identify Pareto optimal designs.
result Algorithm identifies Pareto optimal designs with fewer evaluations than exhaustive search.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling (MPS), is inspired by the classical posterior (Thompson) sampling algorithm for multi-armed…
Electrostatics method samples complex distributions deterministically.
problem Sampling and inference of complex, high-dimensional distributions.
method Electrostatics-based particle system with Newton mechanics principles.
result Method achieves comparable performance to other methods in benchmark tasks.
New method uses diffusion models to optimize experimental design efficiently.
problem Optimizing experimental design for high-dimensional and complex settings.
method Introduces a pooled posterior distribution and uses diffusion-based samplers for efficient sampling and optimization.
result Extends Bayesian Optimal Experimental Design to practical scenarios.
VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.
problem Identifying the best arm with variance constraint under fixed confidence.
method Parameter-free algorithm VA-LUCB, analyzing sample complexity and proving lower bounds.
result Optimal sample complexity up to a logarithmic factor in H V A H_{VA} H V A , demonstrated by experiments. Adaptive sampling method improves efficiency in complex target distributions.
problem Efficiency of importance sampling in complex target distributions, especially multimodal distributions in high-dimensional spaces.
method Proposes an adaptive scheme combining global sampling with delayed weighting to promote efficient exploration of target distributions.
result The proposed algorithm is geometrically convergent under mild assumptions and demonstrates improved efficiency in various numerical experiments.
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.
The paper bounds the complexity of GCNs using Rademacher complexity.
problem Understanding the sample complexity of GCNs.
method Derived tight upper and lower bounds of Rademacher complexity for GCN models.
result The derived bounds depend on the largest eigenvalue of the graph filter and the degree distribution.
Gradient-free framework for Bayesian experimental design in complex systems.
problem Optimal experimental design in systems where gradient information is unavailable.
method Combines EKI and ALDI for optimization and sampling, with approximations for scalable utility estimation.
result Demonstrates robust, accurate, and efficient experimental design in various complex systems.
New DEC variant improves sample complexity bounds in decision making.
problem Understanding sample-efficient learning guarantees in decision making.
method Introducing a new Constrained Decision-Estimation Coefficient (DEC) and using it to derive improved lower bounds.
result New lower bounds improve upon prior work in three aspects: expectation, global applicability, and improper reference models.
In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using l 1 / l 2 l_1/l_2 l 1 / l 2 -reg…
Self-attention prefers sparse functions of input sequences, reducing sample complexity.
problem Understanding the inductive biases of self-attention in modeling long-range dependencies.
method Theoretical analysis and synthetic experiments to probe sample complexity of learning sparse functions with Transformers.
result Bounded-norm Transformer networks can represent sparse functions of the input sequence with logarithmic sample complexity.
New RL algorithm reduces sample complexity for optimal learning.
problem Achieving optimal learning with minimal samples in RL.
method Early-settled variance reduction method with Q-learning sequences.
result Near-optimal regret achieved with sample size S A p o l y ( H ) SA\,\mathrm{poly}(H) S A poly ( H ) . Improved DP optimization for nonconvex, nonsmooth objectives with reduced sample complexity.
problem Differentially private optimization of nonconvex, nonsmooth objectives.
method Proposes single-pass and multi-pass DP algorithms with improved sample complexity.
result Sample complexity bounds improved by factors of Ω ( d ) Ω(\sqrt{d}) Ω ( d ) and Ω ( d 3 / 4 ) Ω(d^{3/4}) Ω ( d 3/4 ) . We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given L ≥ 2 L \ge 2 L ≥ 2 response surfaces over a continuous input space X \cal X X , the aim is to efficiently find the index of the minimal response across the entire X \cal X X . The response surfaces are not known and ha…
We study a recent model of collaborative PAC learning where k k k players with k k k different tasks collaborate to learn a single classifier that works for all tasks. Previous work showed that when there is a classifier that has very small error on all tasks, there is a collaborative algorithm that finds a single classifi…
New algorithm reduces sample complexity for new tasks by leveraging prior knowledge.
problem Designing reinforcement learning agents that reduce sample complexity for new tasks.
method Designing an algorithm that quickly identifies an accurate solution by seeking informative state-action pairs from related tasks, using a generative model.
result PAC bounds on sample complexity demonstrate the benefits of using prior knowledge.
Lower bounds and upper bounds on sample complexity for identifying linear dynamical systems.
problem Identifying an unknown linear dynamical system with limited data.
method Sample complexity lower and upper bounds, persistent excitation condition, active learning algorithm.
result Lower and upper bounds share the same dependency on key problem parameters.
Bayesian design improves accuracy without extra cost.
problem Nested inference in complex systems limits BED accuracy and efficiency.
method Grouped geometric pooled posterior with EKI formulation.
result Improved accuracy and stable estimators at comparable cost.
The paper offers efficient algorithms for combinatorial and linear bandits using empirical process theory.
problem Optimal algorithms for combinatorial and linear bandits with practical sample complexity.
method Empirical process theory, Gaussian-width, minimizing experimental design objective.
result Sample complexity matches lower bounds, especially for combinatorial classes.
New algorithm improves RL performance across different environments.
problem Improving reinforcement learning performance across various environments.
method Designing a fully model-free DRRL algorithm that learns from a single trajectory.
result Demonstrates superior robustness and sample efficiency compared to existing methods.
This paper improves sample efficiency for learning equilibria in multi-player games.
problem Sample-efficient learning of equilibria in games with many players.
method Designs algorithms for learning CCE and CE with polynomial sample complexity in the number of players.
result First to show polynomial sample complexity for learning CCE and CE in multi-player games.
Develops a prediction method based on sampling design.
problem Creating accurate individual predictions.
method Design-based approach using expected cross-validation results.
result Valid inference of unobserved prediction errors defined with respect to sampling design.
Paper analyzes convergence rates of two time-scale AC and NAC algorithms.
problem Finite-sample convergence rate analysis of two time-scale AC and NAC algorithms.
method Developed novel techniques for bias error and convergence rate analysis.
result Established non-asymptotic convergence rates for two time-scale AC and NAC.
New algorithms reduce sample complexity for multiclass contextual bandits.
problem Designing efficient algorithms for multiclass contextual bandits with sparse rewards.
method Two complementary approaches: decision-estimation coefficient analysis and low-variance exploration.
result Achieved optimal sample complexity bounds for multiclass contextual bandits.
Modern ML methods show unexpected behaviors that contradict classical statistics.
problem Modern machine learning methods exhibit behaviors at odds with classical statistical intuitions.
method Comparison between fixed and random design settings in ML and statistics.
result Moving from fixed to random designs reveals new insights into bias-variance tradeoffs and overfitting.