New algorithms control FDX while achieving more power in online multiple testing.
problem Problems with previous online multiple testing methods, including high FDX and low power.
method Developed new dynamic algorithms that adjust testing levels based on accumulated wealth.
result SupLORD algorithm achieves higher power and FDR control in synthetic experiments.
A PID-based feedback-control system improves multiple KPIs in RTB display advertising.
problem Challenges in simultaneously improving multiple KPIs in RTB campaigns.
method Sequential Control using PID-based feedback and importance metrics.
result Effective in simultaneously controlling multiple KPIs in both simulations and live traffic.
Study optimizes resource allocation in noisy systems for better control.
problem Limited attention in stochastic systems with multiplicative noise.
method Analytical and numerical methods for optimal attention allocation.
result Effective resource allocation enhances noise estimation and control decisions.
We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…
New robust control method for uncertain systems using bootstrapped noise.
problem Designing controllers robust to model uncertainties in finite data.
method Least-squares model estimator, bootstrap resampling, multiplicative noise LQR.
result Significantly outperforms certainty equivalent controllers in numerical tests.
This paper tackles robust control of LQR systems with multiplicative noise using policy gradient methods.
problem Robustness in reinforcement learning control of complex systems with multiplicative noise.
method Policy gradient algorithms with gradient domination property for non-convex cost functions.
result Global convergence of policy gradient algorithms to the globally optimum control policy.
A new control chart detects shifts in binary data streams quickly and reliably.
problem Early detection of small shifts in multiple binary data streams.
method Cumulative Standardized Binomial EWMA (CSB-EWMA) chart with exact variance derivation.
result Adaptive control limits ensure robust detection across different data distributions.
Optimizes control of noisy discrete systems without system matrix knowledge.
problem Optimal control of discrete-time systems with additive and multiplicative noises.
method Stochastic Lyapunov and Riccati equations, model-free reinforcement learning.
result Model-free reinforcement learning algorithm converges to optimal control policy.
Solves optimal control for trading multiple mean-reverting assets.
problem How to construct a portfolio from mean-reverting assets.
method Optimal control problem for power utility agent.
result Nearly explicit solution with properties of optimal solution.
SynthBH uses synthetic data to control FDR in multiple testing.
problem Controlling false discovery rate in multiple hypothesis testing.
method SynthBH, a synthetic-powered multiple testing procedure.
result SynthBH guarantees FDR control with synthetic data.
Framework for controlling multiple risks in AI models.
problem Enforcing multiple risk constraints in generative AI models.
method Formalizes problem, introduces two dynamic programming algorithms.
result Achieves nearly tight control of all constraint risks under mild assumptions.
Researchers solve a complex financial control problem with explicit policies.
problem Constrained LQ control with multiplicative noise in financial risk management.
method Derived analytical control policy using state separation property and solving coupled Riccati equations.
result Explicit piece-wise affine control policy for optimal control of stochastic systems.
Optimizes control of hybrid systems with multiple switching processes.
problem Optimal control of hybrid systems with multiple Markov switching processes.
method Combines two separate Markov chains into one synthetic chain, derives HJB equations, and solves the portfolio choice problem.
result Derives explicit solutions and value functions for the optimal control problem.
This paper tackles robust control of noisy systems with uncertain distributions.
problem Optimal control of sampled-data stochastic systems with multiplicative noise and distributional ambiguity.
method Develops a convex relaxation to handle the ``concave-max'' geometry and derives a probabilistic performance guarantee.
result Derives an explicit, non-asymptotic bound on the duality gap and proves robust viability conditions.
DeepFDR uses deep learning for better FDR control in neuroimaging data.
problem Spatial dependence among voxel-based tests in neuroimaging data.
method DeepFDR leverages unsupervised deep learning-based image segmentation.
result DeepFDR outperforms existing methods in FDR control and computational efficiency.
NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.
problem Controlling FDR in complex large-scale problems with indirect relations among covariates.
method NeurT-FDR uses a deep Black-Box framework that parametrizes test-level covariates as a neural network and adjusts auxiliary covariates through a regression framework.
result NeurT-FDR makes substantially more discoveries in real datasets compared to competitive baselines.
The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entai…
Framework calibrates ML models for risk control in various tasks.
problem Achieving statistical guarantees for model predictions.
method Reframing risk control as multiple hypothesis testing, applying statistical techniques.
result New calibration methods for multi-label classification, instance segmentation, outlier detection, and confidence set coverage.
DART2 enhances multiple testing by leveraging ancillary information robustly.
problem Enhancing multiple testing power with uncertain ancillary information.
method Distance-assisted multiple testing procedure (DART2) that handles both helpful and misleading ancillary information.
result DART2 asymptotically controls FDR and improves power when ancillary information is helpful, maintaining FDR and power otherwise.
Deep neural nets solve complex stochastic control problems.
problem Solving stochastic optimal control problems with control multiplicative noise.
method Deep recurrent neural networks and LSTM.
result Deep learning algorithm solves complex stochastic control problems efficiently.
A new method for multiple testing reduces false discoveries while maximizing power.
problem Maximizing statistical power while controlling false discoveries in multiple testing scenarios.
method Adaptive sampling approach inspired by multi-armed bandits to minimize sample size.
result The method achieves sample complexity close to information theoretic lower bounds and outperforms uniform sampling.
Two-stage risk control for ranked retrieval systems.
problem Assessing prediction uncertainty and risk control in sequential machine learning systems.
method Developed two-stage risk control methods based on LTT and CRC frameworks, leveraging sequential nature of retrieval and ranking phases.
result The proposed methods provide theoretical guarantees and reduce computational burden compared to prior work.
Agents learn and control complex mechanical systems through shared memories.
problem Controlling multi-joint dynamical systems.
method Coupled autoregressive active inference agents using Bayesian filtering and minimizing expected free energy.
result Demonstrated learning and control of a double mass-spring-damper system.
Unified framework controls false discovery rate in bandit multiple testing.
problem Designing adaptive algorithms to identify true discoveries in multiple hypothesis testing.
method Unified modular framework using e-processes for FDR control in arbitrary settings.
result Unified framework ensures FDR control for dependent and simultaneous arm queries.
Unified framework uses all data to improve multiple testing efficiency.
problem Improving predictive uncertainty control in decision-making.
method Uses all available data (null, alternative, unlabelled) for score construction and calibration.
result Significantly improves power and adaptability across diverse scenarios.
The paper develops online methods to control familywise error rate in growing hypothesis testing sequences.
problem Controlling familywise error rate in a growing sequence of hypotheses over time.
method Unified algorithmic concepts for offline and online FWER control, including new adaptive online algorithms.
result Substantial gains in power demonstrated and formally proved in a Gaussian sequence model.
Two kernel Stein tests control decision errors in non-parametric model comparison.
problem Non-parametric multiple model comparison.
method Two statistical tests controlling false positive and false discovery rates.
result The first test has a higher true positive rate than the second under appropriate conditions.
Paper proposes AdaDetect for FDR-controlled novelty detection.
problem Semi-supervised novelty detection with probabilistic classification.
method Data-adaptive learning of transformation to control FDR.
result Control of false discovery rate on detected novelties.
Extracts controllable models from videos of real-world activities.
problem Creating realistic and controllable character models from video data.
method Two networks: one for pose and control signal to next pose, and another for pose, new pose, and background to output frame.
result High-quality, controllable character models can be generated from arbitrary videos.
Synthesizes methods for estimating treatment effects with multiple controls.
problem Estimating treatment effects with multiple controls and a single treated unit.
method Generalizes synthetic control and difference-in-differences methods to allow negative weights and permanent differences.
result Allows for more flexible and precise estimation of treatment effects.
New PAC-Bayes bound controls multiple error types simultaneously.
problem Current PAC-Bayes bounds are limited to scalar metrics.
method Bounding KL divergence between empirical and true probabilities of multiple error types.
result First PAC-Bayes bound for rich information-rich certificates.
Pareto Testing optimizes model performance under multiple constraints.
problem Optimizing machine learning models with multiple conflicting objectives.
method Two-stage process combining optimization and statistical testing.
result Models can be configured to satisfy multiple statistical guarantees and objectives.
Study optimizes trading in multiple assets with cross-effects.
problem Optimizing trade execution in multiple assets with cross-impact effects.
method Formulated as a stochastic control problem, extended to progressively measurable controls, solved using linear-quadratic control theory.
result Cross-hedging effects can be optimal, e.g., trading in an asset without an initial position.
NeurT-FDR controls FDR by incorporating feature hierarchy.
problem Controlling FDR in complex, large-scale hypothesis testing problems.
method NeurT-FDR uses a neural network to parametrize test-level covariates and a regression framework to adjust feature hierarchy.
result NeurT-FDR makes substantially more discoveries than competitive baselines.
Deep Hedging learns optimal strategies for various risk levels.
problem Finding optimal hedging policies for diverse risk aversions.
method Continuous Reinforcement Learning with actor-critic algorithm.
result Demonstrated effectiveness in a stochastic volatility model.
Paper finds efficient OPE estimator for multiple logging policies with minimum variance.
problem Finding optimal importance sampling weights for multiple logging policies with varying variances.
method Established efficiency bound under stratified sampling and proposed an estimator achieving this bound.
result Proposed estimator achieves minimum variance for any instance.
Paper proposes a privacy-preserving method to control false discoveries.
problem Protecting individual information in hypothesis tests while controlling false discoveries.
method Differentially private adaptive FDR control method with privacy guarantee.
result The method controls the FDR metric exactly at a user-specified level with privacy.
Private online FDR control for adaptive testing under differential privacy.
problem Controlling false discoveries in adaptive multiple hypothesis testing with privacy constraints.
method Private online algorithms based on non-private results, ensuring privacy and statistical performance.
result Strong guarantees for privacy and statistical performance in FDR and power.
MCP learns reusable skills for complex tasks by combining simple ones.
problem Learning complex tasks with many skills requires impractical amounts of data.
method Factorizes skills into primitives that can be combined multiplicatively.
result MCP can learn and reuse skills for novel tasks from pre-training.
Combines control variates and adaptive importance sampling for Monte Carlo integration.
problem Improving Monte Carlo integration accuracy with control variates and adaptive sampling.
method A quadrature rule combining control variates and adaptive importance sampling.
result Non-asymptotic bound on the probabilistic error of the procedure.
The paper introduces a limit version of multiple stopping options such that the holder selects dynamically a weight function that control the distribution of the payments (benefits) over time. In applications for commodities and energy trading, a control process can represent the quantity that can be purchased by a fix…
Paper offers a dual formulation for consumption problem with multiplicative habit.
problem Optimal consumption with multiplicative habit formation.
method Dual formulation using Fenchel's Duality Theorem.
result Strong duality result linking primal and dual controls.
The paper tackles calibrating long-term behaviors with multiple styles using programmatic style-consistency.
problem Generating long-term sequential behaviors with multiple styles simultaneously.
method Leverage programmatic labeling functions to specify controllable styles and derive style-consistency as a learning objective.
result Learned policies can be calibrated for up to 1024 distinct style combinations.
New methods complete multiple incomplete kernel matrices while controlling model flexibility.
problem Incomplete data in multiple kernel learning.
method Parameterized model matrix with restrictions on model covariance and use of LogDet divergence to ensure positive definiteness.
result Proposed methods yield significant improvements in generalization performance.
Extends conformal prediction for controlling expected risk of monotone loss functions.
problem Controlling expected risk of monotone loss functions.
method Generalizes split conformal prediction with coverage guarantee, extending to distribution shift, quantile risk, multiple, adversarial, and expectations of U-statistics.
result Tight up to an O(1/n) factor, with worked examples in computer vision and natural language processing. T-Rex selector selects variables fast and controls FDR in high-dimensional data.
problem Variable selection in high-dimensional data with FDR control.
method Fused solutions of early terminated random experiments.
result FDR control at target level with high variable selection power.
A new framework enables real-time task trade-off control.
problem Conflict between multiple related tasks in a fixed model capacity.
method Formulates MTL as a preference-conditioned multiobjective optimization problem; uses a hypernetwork-based neural network.
result A single model can handle different trade-off preferences among multiple tasks.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.