An adversarial detector identifies anomalous sequences in sequential data.
problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.
Develops methods for inference after detecting a change in sequential data.
problem Inference after a detected change in sequential data.
method General framework for constructing confidence sets using only data up to a stopping time.
result First general method for sequential changepoint localization with theoretical guarantees.
Unified framework for generating set-valued outputs.
problem Handling unordered set outputs with varying sizes.
method Sequential Set Generation (SSG) framework.
result SSG outperforms baseline methods in experiments.
A sequential classifier minimizes test samples for binary and multi-class classification.
problem Minimizing test samples for sequential classification with unknown distributions.
method Proposes a classifier for binary and multi-class problems, analyzing error probabilities and extending results.
result Significant advantage over non-sequential classifiers, achieving same exponents without rejection option.
Optimal sequential testing for Markovian data with lower and upper bounds.
problem Sequential hypothesis testing for Markovian data.
method Non-asymptotic lower bounds and optimal test design.
result Optimal test matches lower bound asymptotically.
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.
IDS algorithm optimizes sequential decisions in various monitoring settings.
problem Optimizing sequential decisions in complex monitoring scenarios.
method Information-directed sampling (IDS) algorithm for linear partial monitoring.
result IDS achieves nearly worst-case rate optimality in finite-action games.
Develops a new framework for analyzing sequential decision-making problems using information theory.
problem Lack of information-theoretic generalization bounds for sequential decision-making problems.
method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.
result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.
Teaches sequential learners with changing inner states to improve future performance.
problem Teaching sequential learners with evolving inner states.
method Introduces an optimal control approach for multi-agent learning.
result Optimal control approach improves future performance of learners.
A universal framework for constructing confidence sets using sequential likelihood mixing.
problem Constructing reliable confidence sets for realizable likelihood functions.
method Sequential likelihood mixing, integrating Bayesian inference and regret inequalities.
result Establishes fundamental connections and provable coverage guarantees for various inference techniques.
An efficient algorithm for k-median clustering in a sequential setting without substitutions.
problem Clustering a sequence of examples without being able to substitute centers later.
method An efficient algorithm with a multiplicative approximation factor of twice the offline algorithm's factor, and an optimal offline algorithm.
result The efficient algorithm achieves a good approximation of the optimal offline solution.
This paper shows how to construct sequential tests with power one against weakly compact sets in Polish spaces.
problem Testing composite null hypotheses involving weakly compact sets in Polish spaces.
method Develops sequential tests for i.i.d. laws in Polish spaces, providing a sufficient condition for power one.
result Power-one sequential tests exist for weakly compact sets against their complements in i.i.d. laws in Polish spaces.
Efficiently updates posterior tree distributions over meta-trees.
problem Updating posterior distributions over meta-trees efficiently.
method Batch updating method for posterior tree distributions.
result More efficient batch updating method.
The paper sets limits for sequential prediction and recursive algorithms using entropy analysis.
problem Fundamental limitations in sequential prediction and recursive algorithms.
method Entropic analysis to investigate underlying relationships of data and noises.
result Derives Lp bounds quantifiable in conditional entropy. This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
New bounds for sequential tests under power-one error levels.
problem Determining stopping times for sequential tests with power-one error levels.
method Proved two lower bounds for stopping times under specific conditions.
result Upper and lower bounds for sequential tests are shown to be tight.
Study batch learning in linear bandits with context, achieving near-optimal performance.
problem Sequential batch learning in linear contextual bandits with finite actions.
method Established regret bounds and provided algorithms for two settings: arbitrary contexts and i.i.d. contexts.
result Regret upper bound nearly matches lower bound, showing polynomial and logarithmic batch requirements.
OMLE combines optimism and MLE for efficient sequential decision making.
problem Efficiently solving sequential decision making problems, especially in partially observable settings.
method Combines optimism for exploration and maximum likelihood estimation for model learning.
result OMLE learns near-optimal policies for a wide range of sequential decision making problems.
Develops anytime-valid conformal and PAC prediction for streaming data.
problem Lack of guarantee in traditional conformal methods for sequential settings.
method Extends conformal and PAC prediction frameworks to handle streaming data.
result Provides anytime-valid prediction sets for sequential settings.
We consider the problem of sequential prediction and provide tools to study the minimax value of the associated game. Classical statistical learning theory provides several useful complexity measures to study learning with i.i.d. data. Our proposed sequential complexities can be seen as extensions of these measures to …
Gaussian process (GP) models have become a well-established frameworkfor the adaptive design of costly experiments, and notably of computerexperiments. GP-based sequential designs have been found practicallyefficient for various objectives, such as global optimization(estimating the global maximum or maximizer(s) of a …
New method constructs confidence sets for GLMs via game theory.
problem Developing reliable confidence intervals for GLM parameters.
method Reduction to sequential prediction games with low regret.
result Online-to-confidence-set conversions provide new types of intervals.
The paper proposes a method to construct confidence sets using likelihood ratios for sequential decision-making.
problem Constructing valid uncertainty estimates for unknown quantities in sequential decision-making.
method The method uses likelihood ratios to create any-time valid confidence sequences without specialized treatment for each application.
result The proposed confidence sets maintain the prescribed coverage in a model-agnostic manner and their size depends on the choice of estimator sequence.
We consider the problem of inferring a latent function in a probabilistic model of data. When dependencies of the latent function are specified by a Gaussian process and the data likelihood is complex, efficient computation often involve Markov chain Monte Carlo sampling with limited applicability to large data sets. W…
Self-distillation improves constrained language generation by aligning models with target distributions.
problem Sparse and uninformative reward signals in constrained generation settings.
method Iteratively refining the base model through self-distillation, incorporating learned twist functions and proposals.
result Substantial gains in generation quality through improved model alignment with target distributions.
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.
Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.
problem Sequential experimental design with varying costs for changing design variables.
method Adapted batch algorithms to sequential problem, proposing cost-aware and cost-ignorant methods.
result Cost-aware algorithm outperforms tuned process-constrained algorithms in all settings considered.
A new method reduces hyperparameter tuning evaluations by using sequential tests.
problem Time-consuming hyperparameter tuning in machine learning.
method Sequential Random Search (SQRS) extending regular random search.
result SQRS finds similarly well-performing parameter settings with fewer evaluations.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
problem Estimating excursion sets of vector-valued Gaussian processes.
method Clarifying the connection between continuous Gaussian processes and Gaussian measures in Banach spaces, extending concepts and properties from scalar-valued settings to vector-valued settings.
result Consistency results for sequential design strategies can be applied to vector-valued Gaussian processes.
A simple algorithm improves model generalization in expert advice settings.
problem Improving model generalization in expert advice settings.
method A naive aggregation algorithm for point estimations of high-dimensional nonlinear functions.
result Conditions for optimal parameter estimates through sequential mixing distribution strategies.
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.
New method for sequential probability assignment reduces regret using contextual Shtarkov sums.
problem Minimizing regret in sequential probability assignment with arbitrary hypothesis classes.
method Introducing contextual Shtarkov sum and contextual Normalized Maximum Likelihood (cNML) algorithm.
result The contextual Shtarkov sum characterizes minimax regret and provides a minimax optimal strategy.
A sequential learning framework improves domain generalization performance.
problem Training models robust to domain shift across multiple domains.
method Inspired by lifelong learning, a sequential training approach optimizing for all following domains.
result Improves performance on DG benchmarks with a simple, fast algorithm.
This paper studies statistical estimation in optional regression models.
problem Estimating parameters in regression models with optional semimartingale processes.
method Structural least squares (LS) estimates and their sequential versions.
result Strong consistency of LS-estimates and fixed accuracy of sequential LS-estimates.
New method improves generative model performance by fully conditioning variational posteriors.
problem Inaccurate inference due to partial conditioning of variational posteriors in sequential LVMs.
method Introduces fully-conditioned approximate posteriors to improve generative model performance.
result Improves generative modelling and multi-step prediction performance.
Heterogeneous SVO leads to diverse policies in sequential social dilemmas.
problem Understanding how diverse social value orientations affect behavior in sequential social dilemmas.
method Extending prior reinforcement learning studies, we instantiated heterogeneous SVO in a sequential social dilemma setting and measured task-specific diversity metrics.
result Heterogeneous SVO leads to meaningfully diverse policies across various incentive structures.
New framework improves robust inference in HMMs under model misspecification.
problem Inference in general state-space HMMs under likelihood misspecification.
method Generalized Bayesian Inference (GBI) and Sequential Monte Carlo (SMC) methods.
result Improved performance in object tracking and Gaussian process regression.
New algorithm adapts to changing action sets in sequential decision making.
problem Adapting to a changing action set in sequential decision making problems.
method Breaks the problem into inferring action structure and optimizing policy.
result Demonstrates efficiency on real-world lifelong learning problems.
Batched Neural Bandits reduces policy updates in sequential decision-making.
problem Sequential decision-making with batched policy changes.
method BatchNeuralUCB algorithm combining neural networks and optimism.
result Achieves similar regret as fully sequential version with fewer policy updates.
DP-SPRT improves privacy in sequential tests with near-optimal error rates.
problem Privacy constraints in sequential probability ratio tests.
method A wrapper for SPRT that uses a private mechanism to determine when to stop based on predefined intervals.
result DP-SPRT achieves near-optimal error rates and privacy guarantees.
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
GIB improves neural network generalization by dynamically selecting task-relevant features across different sequential environments.
problem Poor generalization of deep neural networks to unseen environments.
method Proposes a gated information bottleneck (GIB) approach that dynamically drops spurious correlations and selects task-relevant features.
result GIB outperforms other IB approaches in adversarial robustness and OOD detection.
Sequential coordinate ascent is more robust in high-dimensional linear regression.
problem Behavior difference between sequential and parallel coordinate ascent in variational inference.
method Comparison of sequential and parallel coordinate ascent algorithms in high-dimensional linear regression.
result Sequential algorithm converges under more relaxed conditions than parallel algorithm.
Study finds conditions for certain warped product manifolds to be quasi-Einstein.
problem Conditions for quasi-Einstein sequential warped product manifolds.
method Investigated necessary and sufficient conditions for specific types of manifolds.
result Identified conditions for sequential warped product manifolds to be quasi-Einstein.
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method. We conduct several conventional sequential estimation procedures separately, and properly integrate their results while maintaining the desired statistical properties. A…
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection del…
New model accounts for sequential dependence in LLM reliability.
problem Uncertainty in LLM reliability assessment due to sequential interactions.
method Extended Bayesian framework with Hidden Markov Model for sequential dependence.
result Ignoring sequential dependence leads to overconfident reliability estimates.
A new model for sequential prediction handles adversarial examples by allowing abstention.
problem Sequential prediction algorithms fail with adversarial examples, leading to incorrect predictions.
method Proposes a new model that allows abstention from predictions on adversarial examples, scaling error with VC dimension.
result A learner's error scales with the VC dimension of the hypothesis class, matching the stochastic setting.