Optimal online data collection for semiparametric inference reduces regret.
problem Sequential data collection decisions for efficient estimation under budget constraints.
method Online Moment Selection framework; Explore-then-Commit and Explore-then-Greedy policies.
result Online data collection policies achieve zero regret relative to an oracle policy.
New method uses online learning to improve AIPW estimators for adaptively collected data.
problem Estimating treatment effects with adaptively collected data.
method Online learning to minimize sequentially weighted estimation error.
result Local minimax lower bound shows optimality of AIPW estimator.
Improves likelihood-free inference by using a new sampling approach to avoid biased data collection.
problem Efficient Bayesian inference without likelihood evaluation for real-world datasets.
method Introduces Neural Proposal (NP) to sample simulation inputs i.i.d. for unbiased posterior inference.
result Demonstrates improved performance, especially for multi-modal posteriors, through experiments.
New method reduces memory usage for Bayesian inverse problems on large grids.
problem Solving large-scale linear inverse problems with Gaussian process priors.
method Implicit representation of posterior covariance matrices, sequential disintegrations of Gaussian measures.
result Significant reduction in uncertainty for high-density regions estimation.
In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it. Thus, we propose a framework to sequentially update a maximum margin classifier by taking advantage of the Maximum…
New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.
problem Privacy protection in sequential hypothesis testing for sensitive data.
method Renyi differential privacy, Wald's Sequential Probability Ratio Test (SPRT).
result Private algorithm with strong privacy guarantees and theoretical performance analysis.
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what wi…
New test detects independence in streaming data, adapting to data complexity.
problem Independence testing in streaming data with adaptive stopping.
method Sequential kernelized independence tests using betting principles.
result Valid inference in streaming data with improved power.
The study initiates a theoretical analysis of dynamic benchmarking models.
problem Lack of theoretical foundation and empirical studies in dynamic benchmarks.
method Examined two realizations of dynamic benchmarking: sequential and hierarchical dependency models.
result Sequential dynamic benchmarks show initial performance improvement but can stall after three rounds due to label noise.
New algorithm for sequential off-policy learning improves performance over batch methods.
problem Training policies from logged interaction data in a sequential setting.
method Combines Logarithmic Smoothing with online PAC-Bayesian tools.
result Improves performance and accelerates convergence in sequential off-policy learning.
Efficiently audits model fairness with continuous monitoring and flexible data collection.
problem Continuous monitoring and flexible data collection for fairness auditing.
method Sequential, anytime-valid inference and game-theoretic statistics.
result Demonstrated efficacy on three fairness datasets.
The classification procedure of streaming data usually requires various ad hoc methods or particular heuristic models. We explore a novel non-parametric and systematic approach to analysis of heterogeneous sequential data. We demonstrate an application of this method to classification of the delays in responding to the…
Optimizes data collection for ranking and selection problems.
problem Identifying the best system from multiple solutions with limited data.
method Sequential sampling algorithm with MPB estimator and kernel ridge regression.
result OSAR achieves optimal sampling ratios almost surely in the limit.
Probabilistic active meta-learning improves data efficiency in robotics.
problem Data-efficient learning in robotics where data collection is expensive.
method Conceptualizing meta-learning with a probabilistic latent variable model for sequential task selection.
result Improves data efficiency compared to baselines on simulated robotic experiments.
Study optimizes sampling to avoid extreme tail risks in unknown heavy-tailed distributions.
problem Identify optimal alternative with minimal extreme tail risk from unknown heavy-tailed distributions.
method Data-driven sequential sampling policies to maximize likelihood of selecting the optimal alternative.
result Proposed methods outperform existing approaches in identifying the optimal alternative.
Post-ADC inference corrects bias in statistical inference after active data collection.
problem Bias in inference after active data collection.
method Post-ADC inference framework that corrects bias from both ADC process and data-driven target construction.
result Valid inference for data collected by SMBO methods like GP-UCB and TPE.
This thesis improves OCO algorithms for dynamic data environments.
problem Sequential, changing data in big data environments.
method Designing algorithms to adapt to changing environments.
result Improved algorithms for online resource allocation.
A new framework optimizes manufacturing decisions with less data and time.
problem Optimizing complex systems with multiple conflicting objectives.
method Data-driven Bayesian optimization using sequential learning.
result The proposed algorithm achieves the actual Pareto front with less data.
Bayesian optimization agent learns user preferences from pairwise comparisons.
problem Learning user preferences from unknown and infinite choices.
method Sequential Bayesian optimization with pairwise comparisons.
result Optimal agent strategy minimizes remaining system uncertainty.
Estimators computed from adaptively collected data do not behave like their non-adaptive brethren. Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method -- W-decorrelation -- for transformi…
Sequential tests for two-sample and independence testing using betting strategies.
problem Testing sequential data for two-sample and independence without kernel selection issues.
method Prediction-based betting strategies that adaptively determine distribution and joint distribution.
result Prediction-based tests outperform kernel-based approaches in high-dimensional or structured data settings.
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
problem Missing data in medical records due to sensor off-times and uneven data collection.
method Sequential variational autoencoders (VAEs) with a new methodology called Shi-VAE.
result Shi-VAE achieves the best performance in terms of both metrics compared to state-of-the-art methods.
A new method uses active learning to improve bile duct stone evaluation.
problem Efficiently collecting necessary patient data in sequential healthcare decisions.
method Developed an active learning-based multistage sequential decision-making model.
result Improves estimation efficiency by 62%-1838% compared to baseline methods.
The paper addresses estimating long-term treatment effects with monotone missing data.
problem Estimating long-term treatment effects with missing data, especially monotone missing.
method The paper introduces the sequential missingness assumption for identification and proposes three novel estimation methods: inverse probability weighting, sequential regression imputation, and SeqMSM. It also introduces a balancing-enhanced approach, BalanceNet, to improve estimation accuracy.
result The proposed methods, including BalanceNet, effectively estimate long-term treatment effects with monotone missing data.
New testing method for robust actor-critic bandit algorithms.
problem Balancing data collection for app performance and user adherence.
method Modified actor-critic algorithm and novel testing procedure.
result Testing procedure is robust to critic misspecification.
UCB algorithm provides stable sample means for sequential data.
problem Challenges in inferential tasks with sequential data.
method Stability property of UCB algorithm for multiarmed bandit problems.
result UCB algorithm ensures asymptotically normal sample means.
A new sequential test for unnormalized densities.
problem Testing unnormalized densities with adaptive stopping.
method Sequential kernelized Stein discrepancy test, using non-uniform Stein kernels.
result Valid test with asymptotic lower bound for growth.
Develops methods to test nonstationarity and detect change points in RL.
problem Nonstationary environments in reinforcement learning.
method Consistent testing procedure and change point detection method.
result Validates the effectiveness of the proposed methods in real data.
A new method for federated learning aggregates data from multiple sites efficiently.
problem Aggregating data from multiple sites securely and effectively.
method Sequential federated learning with distributed computing.
result Preserves information from individual analyses and accelerates the analysis process.
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.
Modern computing and communication technologies can make data collection procedures very efficient. However, our ability to analyze large data sets and/or to extract information out from them is hard-pressed to keep up with our capacities for data collection. Among these huge data sets, some of them are not collected f…
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
We model and correct bias in sequential evaluation, improving ranking accuracy.
problem Sequential evaluation bias in online, irrevocable scoring.
method Modeling the rating process, posing as statistical inference, proposing an online algorithm.
result Near-linear time, online algorithm with guarantees in ranking metrics, information theoretically optimal.
Expected signatures map data streams to lower dimensions, improving ML performance.
problem Leveraging model-free embeddings for domain-agnostic machine learning.
method Expected signatures map data streams to lower dimensions, with convergence results bridging empirical and theoretical estimators.
result A modified expected signature estimator with lower mean squared error for martingale processes.
Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the …
Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from th…
Paper proposes active learning for mining conflict dynamics from textual data.
problem Lack of detailed information on conflict dynamics in existing datasets.
method Active learning with a large, encoder-only language model.
result Performance similar to human coding with reduced human annotation.
Paper proves conformal prediction works for any data distribution.
problem Quantifying risk in AI systems with non-exchangeable data.
method Developed a method to extend conformal prediction to any data distribution.
result Valid conformal prediction guarantees for any data distribution.
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.
Safe Active Learning (SAL) for GPODE models improves model performance without compromising safety.
problem Efficient and safe data collection for GPODE models.
method Sequential data suggestion constrained by safety, maximizing information gain.
result SAL GPODE outperforms standard data collection methods.
Accumulation of standardized data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of contemporary microarray collections. While short oligonucleotide arrays …
The paper addresses uncertainty in demand prediction for dynamic pricing.
problem Uncertainty quantification in the demand function for dynamic pricing.
method Developed a debiased approach to construct accurate confidence intervals for the demand function.
result Asymptotic normality guarantee of the debiased estimator for the demand function.
Frequentist method estimates uncertainty in RNNs without altering architecture.
problem Uncertainty quantification in RNNs for decision-making.
method Jackknife resampling and influence functions to estimate variability.
result The method provides theoretical coverage guarantees on uncertainty intervals.
In this paper, we develop a new sequential regression modeling approach for data streams. Data streams are commonly found around us, e.g in a retail enterprise sales data is continuously collected every day. A demand forecasting model is an important outcome from the data that needs to be continuously updated with the …
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…
Bridges uplift modeling and sequential decision-making with online budget allocation.
problem Treatment allocation under budget constraints in digital advertising.
method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.
result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.
Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the…
Study aggregation of statistical evidence under unknown dependence using group-invariance.
problem Aggregating statistical evidence under unknown and complex dependence structures.
method Develops a framework using group-invariance and permutation-based constructions to aggregate evidence across transformed datasets.
result Shows uniform improvement in critical values for single-batch aggregation over deterministic calibrations, adapting to unknown dependence structures.