New PAC-Bayesian bounds for online learning with data streams.
problem Challenges of traditional PAC-Bayesian bounds in dynamic data collection.
method Developed new PAC-Bayesian bounds in online learning framework, using updated regret definition and batch-to-online conversion.
result PAC-Bayesian bounds hold for online learning with dependent data and non-convex losses.
Proposes OBS, a method to adaptively combine Bayesian models online.
problem Learning optimal combinations of Bayesian models in online learning.
method Empirical Bayes lens, Online Bayesian Stacking (OBS).
result Establishes a novel connection between OBS and portfolio selection.
Bayesian method adapts to unknown distribution shifts in online learning.
problem Online learning with unknown and irregular distribution shifts.
method Bayesian inference with change-point detection and beam search.
result Improves adaptation to new data distributions over state-of-the-art methods.
Bayesian online learning algorithm for one-pass data, achieving frequentist validity and uncertainty quantification.
problem Theoretical limitations in Bayesian online learning, especially in the one-pass setting.
method Proposed a new Bayesian online learning algorithm with a warm-start phase for the one-pass regime, establishing convergence rates and valid uncertainty quantification.
result The sequentially updated posterior attains optimal convergence rates and valid uncertainty quantification without diverging mini-batch sample sizes.
Develops Bayesian filtering for online learning and related problems.
problem Sequential machine learning challenges, especially non-stationarity, model misspecification, and high dimensionality.
method Modular adaptive framework, provably robust filter, and sequential parameter updates.
result Improved performance in dynamic, high-dimensional, and misspecified models.
Decentralized Gaussian processes for multi-agent systems.
problem Scalable and flexible learning solutions for multi-agent systems.
method Asymptotically exact decentralized solution to Gaussian processes, with online Bayesian model averaging for hyperparameter selection.
result Asymptotically exact decentralized Gaussian process approximation and online Bayesian model averaging.
Bayesian reflex models AI learning like the autonomic nervous system.
problem Online learning in dynamic AI environments.
method Bayesian online algorithms with belief maintenance, sequential updating, and uncertainty-driven action balancing.
result Unified framework for adaptive AI learning.
BONG optimizes Bayesian inference online with natural gradient descent.
problem Sequential Bayesian inference in online settings.
method Bayesian online natural gradient (BONG) approach based on variational Bayes.
result BONG outperforms other online VB methods in non-conjugate settings.
New methods improve Bayesian inference and decision-making in online learning.
problem Current Bayesian deep learning does not fully utilize joint predictives for sequential decision-making.
method Proposes new evaluation settings for active learning and active sampling, focusing on marginal and joint cross-entropies.
result Initial experiments suggest challenges in applying current BDL inference techniques in high-dimensional spaces.
Bayesian algorithms improve online learning with adversaries over infinite action spaces.
problem Online learning with adversaries over infinite action spaces.
method Developed a Thompson sampling algorithm for online learning with an adversary's prior over the space of actions.
result Thompson sampling over a Gaussian process prior achieves a rate of O ( β T d log ( 1 + d λ β ) ) O(β\sqrt{Td\log(1+\sqrt{d}\fracλβ)}) O ( β T d log ( 1 + d β λ ) ) against a β β β -bounded λ λ λ -Lipschitz adversary. Bayesian framework reduces online optimization regret.
problem Sequential optimization in dynamic environments with bounded losses.
method PAC-Bayes theory and Bayesian updating principles.
result Achieves O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret for bounded losses. Bayesian neural networks learn weights with closed-form updates.
problem Efficiently learning Bayesian neural networks with closed-form updates.
method Closed-form Bayesian inference for online learning of Gaussian-weighted BNNs.
result Closed-form expressions for sequential/online training of BNNs.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
problem Catastrophic forgetting in few-shot classification problems.
method Bayesian online learning, meta-learning, Laplace approximation, variational inference.
result Framework effectively achieves goal of overcoming catastrophic forgetting in few-shot classification.
LF-IBIS learns optimal policies online without explicit likelihood.
problem Bayesian RL challenges due to intractable likelihood functions.
method Combines ABC with IBIS for online belief updates.
result Approximates posterior distributions for policies and parameters.
Improves online learning with expert demonstrations, quality matters.
problem Improving online learning through offline demonstration data.
method Thompson sampling applied to a multi-armed bandit model, informed by expert demonstrations and Bayes' rule.
result Substantial empirical regret reduction with expert demonstrations, improving online performance.
Bayesian framework for online consensus prediction from expert feedback.
problem Online classification with expert consensus prediction, cost-effective.
method General Bayesian framework for dynamic expert consensus estimation.
result Demonstrated superior performance on large-scale crowdsourced datasets.
New framework connects online learning to statistical learning for better generalization bounds.
problem Deriving generalization bounds for statistical learning algorithms.
method Constructing an online learning game and showing a connection to statistical learning.
result Established a connection between online and statistical learning, leading to new generalization bounds.
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.
Paper proposes an efficient online learning method using an offline dataset for infinite horizon MDPs.
problem Efficient online reinforcement learning in infinite horizon MDPs with an unknown expert policy.
method Bayesian approach to model the expert's policy and minimize cumulative regret.
result Upper bound on regret of i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) for the Informed PSRL algorithm. The Gaussian mixture model is a classic technique for clustering and data modeling that is used in numerous applications. With the rise of big data, there is a need for parameter estimation techniques that can handle streaming data and distribute the computation over several processors. While online variants of the Exp…
Bayesian framework estimates label shift for improved classifier performance.
problem Label shift in supervised learning leading to degraded classifier performance.
method Bayesian framework with dynamic Dirichlet priors and online EM algorithms.
result Significant improvements in classifier accuracy over state-of-the-art methods.
Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such met…
New algorithms for fast online decision making using neural networks and martingale posteriors.
problem Online sequential decision making under uncertainty.
method Martingale posterior neural networks for fast online learning and decision making.
result Achieves competitive performance-speed trade-offs in non-stationary contextual bandits and Bayesian optimization.
The paper improves Bayesian optimization by calibrating uncertainty estimates.
problem Improper uncertainty estimates in Bayesian optimization when data is non-stationary.
method Proposes online learning algorithms to maintain calibration on non-i.i.d. data and integrates them into Bayesian optimization.
result Calibrated Bayesian optimization converges to better optima in fewer steps.
Paper develops a fast Bayesian method to predict toxic trades in financial transactions.
problem Predicting and managing toxic trades in financial transactions.
method PULSE: an online learning Bayesian procedure for neural networks.
result Neural networks trained with PULSE outperform traditional methods in predicting toxic trades.
Unified Bayesian framework for efficient off-policy evaluation and learning in large action spaces.
problem Efficient off-policy evaluation and learning in systems with correlated actions.
method Unified Bayesian framework with structured priors and sDM approach.
result sDM leverages action correlations without compromising computational efficiency.
Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional policy spaces - such as those found in recommendation and ranking problems - is often constrained by the limited number of experiments that c…
Proposes FIPO-BC for efficient online calibration of complex models.
problem Efficiently calibrating computationally expensive models with large datasets.
method Fixed inducing points online Bayesian calibration (FIPO-BC) algorithm.
result FIPO-BC is at least ten times faster than standard methods and enables online updates.
Proposes efficient Bayesian logistic regression for large sparse datasets.
problem Infeasibility of theoretical Bayesian methods for large sparse feature sets.
method Low complexity analytical approximations for sparse online logistic and probit regressions.
result Empirical results show superior performance compared to more complex methods.
Revisits online Laplace methods for neural networks, showing they are sound under certain conditions.
problem Online Laplace methods violate the Laplace approximation's critical assumption.
method Re-derives online Laplace methods, showing they target a variational bound on a mode-corrected variant of the Laplace evidence.
result Online Laplace and its mode-corrected counterpart share stationary points that satisfy the Laplace method's assumption.
Bayesian model for energy consumption helps electric vehicles navigate efficiently.
problem Limited battery capacity in electric vehicles makes energy efficient navigation challenging.
method Developed an online learning framework using Bayesian models and exploration strategies like Thompson Sampling.
result Established rigorous regret bounds for Thompson Sampling in both single-agent and multi-agent settings.
New method reduces forecasting error by up to 67% in various data types.
problem Outliers and model misspecification in online infinite hidden Markov models.
method Batched Robust iHMM (BR-iHMM) with bounded posterior influence function.
result Reduces one-step-ahead forecasting error by up to 67% in various data types.
This work tackles online memory selection in continual learning using information theory.
problem Online selection of a representative replay memory from data streams.
method Information-theoretic criteria (surprise, learnability) and Bayesian model for efficient computation.
result InfoRS improves robustness against data imbalance compared to reservoir sampling.
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
Kernel interpolation speeds up online Gaussian process updates.
problem Efficiently updating Gaussian process posteriors with new data.
method Structured kernel interpolation for constant-time updates.
result Exact inference maintained with constant-time updates.
Bayesian method improves online NARMAX model identification.
problem Online identification of nonlinear systems with small sample sizes and low noise.
method Variational Bayesian inference using message passing algorithm for polynomial NARMAX models.
result Variational Bayesian estimator outperforms recursive and offline least-squares methods.
Unified framework for Bayesian online learning in changing conditions.
problem Probabilistic online learning in non-stationary environments.
method BONE framework with three modelling choices and two algorithmic choices.
result Framework allows for reinterpreting and proposing new methods.
New method handles unknown task boundaries in continual learning.
problem Catastrophic forgetting in neural networks.
method Fixed-point equations for online variational Bayes optimization.
result Approximates online Bayes update for non-stationary data.
We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. Howe…
Paper proposes a recursive GPSSM for efficient online learning.
problem Efficient online learning for dynamical models with limited prior information.
method Recursive Gaussian Process State-Space Model with adaptive capabilities for domains and hyperparameters.
result Superior accuracy, computational efficiency, and adaptability compared to state-of-the-art methods.
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression. PA algorithms are formulated in terms of deterministic point-estimation problems governed by a set of user-defined hyperparameters: the a…
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
problem Detecting changes in data streams efficiently and reliably.
method Bayesian online changepoint detection with provable robustness and scalability.
result The proposed method is more than 10 times faster than previous approaches and provides provable robustness.
Bayesian optimization sped up with model approximations for safe online system optimization.
problem Efficiently optimize systems with safety guarantees under noisy conditions.
method Incorporate reduced physical models into Bayesian optimization, using Markov chain Monte Carlo for robust safety bounds.
result Significant acceleration of optimization for expensive functions with robust safety guarantees.
This work improves Gaussian process regression for large, non-stationary data.
problem Scalability issues and performance degradation for non-stationary data.
method Combines variational free energy approximations with online expectation propagation and local splitting steps.
result Incremental adaptation to locality, heterogeneity, and non-stationarity in training data.
L-HNNs improve Bayesian inference efficiency by reducing gradient computation.
problem Efficient Bayesian inference with minimal gradient computation.
method Integrating L-HNNs into NUTS with online error monitoring.
result L-HNNs in NUTS outperform NUTS in complex posterior densities.
Bayesian method improves multivariate periodontal outcome modeling.
problem Modeling periodontal outcomes is challenging and requires consideration of demographic differences.
method Jointly models multivariate outcomes using an online Bayesian transfer learning framework.
result Significant improvement over univariate RECaST method demonstrated.
Paper tackles policy selection with logged data and limited online interactions.
problem Safe evaluation and deployment of offline reinforcement learning policies.
method Active offline policy selection combining logged data with online interaction.
result Improves upon state-of-the-art OPE estimates and pure online policy evaluation.
This paper explores online learning of dynamics and state using ensemble Kalman filters.
problem Reconstructing dynamics from partial and noisy observations in real-time.
method Ensemble Kalman filter (EnKF) family of algorithms for online learning of dynamics and state.
result Demonstrates the efficiency and accuracy of online learning methods using Lorenz models.