Paper develops robust OPF method using contextual information.
problem Optimal Power Flow problem under incomplete uncertainty knowledge.
method Distributionally robust chance-constrained formulation with probability trimmings and optimal transport.
result Distributional robustness improves expected cost and system reliability.
Model compresses event-like contexts using gated surprise signals.
problem Perceiving a dynamic world as organized events.
method Hierarchical, surprise-gated recurrent neural network architecture.
result Achieves best performance on multiple event processing tasks.
CoSMIC extends flow-based SVI to transdimensional problems.
problem Bayesian structure learning and model selection with multi-model parameter spaces.
method Normalizing flows with a combined stochastic variational transdimensional inference approach.
result Improved performance on high-cardinality model spaces.
New method for scalable stochastic neural networks using Kronecker Flow.
problem Scaling stochastic neural networks to high dimensions.
method Kronecker Flow, a scalable parameterization of noise generation.
result Competitive performance on various tasks compared to existing methods.
Paper proposes a new method to learn code semantics using an Intermediate Representation (IR) and embeddings.
problem Lack of robust methods to comprehend program semantics robustly.
method Defines an embedding space (inst2vec) based on IR of code, leveraging both data- and control-flow.
result A single RNN architecture and fixed inst2vec embeddings outperform specialized approaches on various tasks.
Contextualized ML learns context-dependent effects using deep learning.
problem Learning heterogeneous and context-dependent effects in data.
method Applying deep learning to the meta-relationship between contextual information and context-specific parametric models.
result Unified framework for cluster analysis and cohort modeling.
IDS improves reinforcement learning with contextual information.
problem Optimizing IDS for contextual reinforcement learning.
method Investigated contextual bandit problems and proposed a computationally-efficient IDS.
result Contextual IDS outperforms conditional IDS by considering future contexts.
SADCBO optimizes contextual variables by balancing relevance and cost.
problem Optimizing contextual variables with varying costs and unknown relevance.
method Adaptive selection of relevant contextual variables using sensitivity analysis and early stopping.
result Consistent improvement in optimization across various examples.
Anomaly detection method separates contextual from behavioral attributes.
problem Detect anomalies in data without labeled examples.
method Uses joint deep variational generative models.
result Robust to anomalous or novel contextual attributes.
The bundle approach and n-contextuality reveal quantum model contextuality.
problem Understanding contextuality in quantum models using topology.
method Using the bundle approach, we describe contextuality as the non-existence of global sections in the measure bundle. We introduce n-contextuality to explore model dependence on scenario topology.
result Quantum theory and GHZ models exhibit all levels of n-contextuality, showing contextuality is related to holonomy group non-triviality.
Paper proves CFlows can approximate any diffeomorphism and applies it in Bayesian optimization.
problem Proving the universality of CFlows in approximating diffeomorphisms.
method Deriving the universality of Para-CFlows through affine coupling layers and invertible linear transforms.
result Para-CFlows can approximate any diffeomorphism in C^k-norm.
OSOM solves multi-armed and linear contextual bandits efficiently.
problem Simultaneously optimal algorithm for multi-armed and linear contextual bandits.
method Design of a single computationally efficient algorithm that adapts to both regimes.
result Simultaneously optimal regret rates in both simple multi-armed and linear contextual bandits.
Proposes a neural network for contextual regression.
problem Improving model efficiency and interpretability in regression with contextual features.
method Simple contextual neural network (SCtxtNN) that separates context identification from context-specific regression.
result SCtxtNN achieves lower excess mean squared error and more stable performance than feed-forward neural networks.
Paper tackles domain adaptation for contextual bandits with sub-linear regret.
problem Adapting contextual bandit algorithms across domains with distribution shift.
method Learn a bandit model for the target domain using feedback from the source domain.
result Sub-linear regret bound maintained across domains.
Transformers model contextual relations using probabilistic measures, revealing their expressive power.
problem Lack of clear understanding of Transformer's ability to model contextual relations.
method Introduced a measure-theoretic framework connecting softmax attention and entropy-regularized optimal transport.
result Transformer architectures can approximate arbitrary contextual relations, and the choice of normalization affects how these relations are represented.
Paper solves stochastic contextual linear bandits using linear bandit algorithms.
problem Stochastic contextual linear bandits with unknown context distribution.
method Establishes a reduction framework to convert to linear bandit problems.
result Achieves nearly optimal regret bound of O ( d T log T ) O(d\sqrt{T\log T}) O ( d T log T ) . Introduces R package for contextual bandit algorithms.
problem Lack of standardized tools for comparing bandit algorithms.
method Object-oriented R package for parallelized comparison of contextual and context-free bandit policies.
result Facilitates simulation and offline analysis of bandit algorithms.
The study explores whether model selection guarantees apply to contextual bandits.
problem Applying model selection guarantees to contextual bandits.
method Investigates whether similar guarantees for model selection in statistical learning can be extended to contextual bandit learning.
result Initial findings suggest that model selection guarantees may not directly apply to contextual bandits.
Faster algorithm reduces contextual bandit regret with fewer offline regression calls.
problem Optimizing reward in contextual bandits with unknown functions.
method Designing a simple algorithm with O ( log T ) {O}(\log T) O ( log T ) offline regression calls. result Achieves statistically optimal regret with minimal offline calls.
Efficiently handles contextual bandits with diffusion models.
problem Challenges in online decision-making with contextual bandits.
method Leverage pre-trained diffusion models as priors to capture action dependencies.
result Developed an algorithm for efficient posterior approximation.
This study analyzes how RNNs process context in sentiment analysis.
problem Understanding how recurrent neural networks process context in sentiment analysis.
method Developed methods to reverse engineer RNNs, identifying contextual effects and quantifying their strength and timescale.
result Identified inputs that induce contextual effects and quantified their properties.
Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.
problem Improving the performance of Thompson Sampling in contextual bandits with sub-Gaussian rewards.
method Proved comprehensive bounds on Thompson Sampling expected cumulative regret based on mutual information and lifted information ratio for sub-Gaussian rewards.
result Explicit regret bounds for various contextual bandit scenarios.
Data poisoning attacks can manipulate contextual bandit algorithms to pull target arms.
problem Offline data poisoning attacks in contextual bandits.
method General attack framework based on convex optimization.
result Attacker can force bandit algorithm to pull target arm for target contextual vector.
CADR estimator improves inference for contextual bandit data.
problem Valid inference on contextual bandit data.
method CADR estimator for policy value, addressing adaptive data collection challenges.
result CADR provides correct coverage of confidence intervals.
Develops balanced linear contextual bandit algorithms to reduce estimation bias.
problem Estimation bias in contextual bandits with rich heterogeneity or complex models.
method Integrates balancing methods from causal inference to reduce estimation bias.
result First regret bound analyses for linear contextual bandits with balancing.
Deep learning tackles contextual multi-armed bandits with principled exploration.
problem Contextual multi-armed bandits in industrial applications.
method Bayesian neural network with dropout for non-linear modeling and Thompson sampling for principled exploration.
result Substantially reduces regret compared to existing methods.
New algorithm learns optimal exploration parameters for contextual bandits.
problem Learning optimal exploration in contextual bandits.
method Proposes two algorithms that learn optimal exploration parameters online based on context and reward.
result Demonstrates improved performance in learning optimal exploration compared to traditional methods.
Conversational UCB accelerates bandit learning with user feedback.
problem Slow learning speed in traditional contextual bandit algorithms.
method Generalized contextual bandit to conversational contextual bandit, leveraging both behavioral and conversational feedbacks.
result ConUCB achieves a smaller regret upper bound, indicating faster learning speed.
The paper tackles learning varying DAG structures based on contextual features.
problem Learning a single DAG for the entire population from observational data.
method A neural network that maps contextual features to a weighted adjacency matrix of a DAG, with a projection layer to ensure acyclicity.
result The new approach can recover context-specific DAGs where existing methods fail.
A framework for auto-tuning hyper-parameters in contextual bandit algorithms.
problem Auto-tuning hyper-parameters in real-time for contextual bandit algorithms.
method Proposes a Syndicated Bandits framework to learn multiple hyper-parameters dynamically.
result Achieves optimal regret bounds under certain scenarios and handles multiple contextual bandit algorithms.
New algorithm improves learning efficiency in multi-task contextual bandits.
problem Improving learning efficiency in multi-task contextual bandits.
method Alternating projected gradient descent (GD) and minimization estimator for low-rank feature matrix recovery.
result Proved regret bound for multi-task learning algorithm.
New algorithm for contextual dueling bandits achieves nearly optimal regret.
problem Contextual dueling bandits with feedback on preferred options.
method Proposes FGTS.CDB, a Thompson sampling algorithm for linear contextual dueling bandits.
result Achieves nearly minimax-optimal regret of i l d e O ( d T ) ilde{\mathcal{O}}(d\sqrt T) i l d e O ( d T ) . New algorithm reduces contextual bandits to efficient regression.
problem Developing efficient algorithms for contextual bandits with general function classes.
method Reduction from contextual bandits to online regression with oracle.
result First universal and optimal reduction with no overhead.
New method for contextual bandit with missing rewards.
problem Contextual bandit with missing rewards in online settings.
method Combining contextual bandit approach with unsupervised learning (clustering) to estimate missing rewards.
result Promising empirical results on real-life datasets.
The paper tackles minimax optimality in continuum contextual bandits with Hölder continuity.
problem Minimizing regret in a continuum of contexts with Hölder continuity.
method Proves a static-to-contextual regret conversion theorem and analyzes various dependency cases.
result Achieves minimax optimal contextual regret for convex and strongly convex bandits.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
LMC-TS uses MCMC for efficient posterior sampling in contextual bandits.
problem Efficiency of Thompson sampling for high-dimensional contextual bandits.
method Langevin Monte Carlo for direct posterior sampling.
result LMC-TS achieves sublinear regret bound for linear contextual bandits.
New method for contextual bandits with corrupted context.
problem Contextual bandits with corrupted context in online settings.
method Combining contextual bandit and multi-armed bandit approaches.
result Improved learning from all iterations, including corrupted ones.
Proposes a new algorithm for graph-based semi-parametric contextual bandits.
problem Non-stationarity in human behavior and social interaction.
method SemiGraphTS algorithm for graph-based semi-parametric reward model.
result Derives an upper bound of cumulative regret for graph-based semi-parametric model.
MELEE learns good exploration strategies for contextual bandits.
problem Interactive contextual bandit exploration trade-off.
method Meta-learning from synthetic data to learn a good exploration policy.
result MELEE outperforms seven strong baseline algorithms on real-world datasets.
Develops a new algorithm for high-dimensional contextual data in bandit problems.
problem Challenges of high-dimensional contextual data in traditional linear bandits.
method Contextual Bandits with Random Projection (CBRAP) algorithm.
result Provides a linear upper regret bound associated with reduced dimensions.
New algorithm estimates treatment effects for more efficient contextual bandits.
problem Contextual bandits struggle with action-independent reward redundancies.
method Reduces contextual bandits to heterogeneous treatment effect estimation.
result Heterogeneous treatment effect estimation leads to more efficient model estimation.
New algorithm for contextual bandits with linear constraints using regression.
problem Contextual bandits with packing and covering constraints.
method Modular Lagrangian approach via regression.
result First vanishing-regret guarantees for CBwLC (or CBwK).
AdaLinUCB optimizes exploration-exploitation for contextually varying costs.
problem Optimizing decision-making in environments with varying exploration costs.
method Adaptive Upper-Confidence-Bound (AdaLinUCB) algorithm for opportunistic learning.
result AdaLinUCB achieves O((log T)^2) regret bound, significantly outperforming other algorithms.
Contextual information helps identify the best arm more efficiently.
problem Best arm identification with contextual covariate information.
method Proposed a context-aware version of the 'Track-and-Stop' strategy.
result Expected number of arm draws matches lower bound asymptotically.
A simple algorithm reduces federated contextual linear bandits' regret efficiently.
problem Solving federated contextual linear bandits with asynchronous agents.
method Proposed a simple algorithm exttt{FedLinUCB} based on optimism principle.
result Proved exttt{FedLinUCB} has bounded regret i l d e O ( d ∑ m = 1 M T m ) ilde{O}(d\sqrt{\sum_{m=1}^M T_m}) i l d e O ( d ∑ m = 1 M T m ) and communication complexity i l d e O ( d M 2 ) ilde{O}(dM^2) i l d e O ( d M 2 ) . This paper proposes using contextualized word representations for better concept taxonomies.
problem Current taxonomy learning systems define concepts as single words, limiting their semantic understanding.
method Defines concepts as synsets, learns density-based approximations of contextualized word representations, and measures similarity and hypernymy.
result Contextualized word representations can improve the accuracy of concept taxonomies.
A new algorithm avoids worst-case outcomes in risky contexts.
problem Risk-averse behavior in contextual bandits is challenging.
method Developed a first risk-averse contextual bandit algorithm with online regret guarantees.
result First algorithm with an online regret guarantee for risk-averse contextual bandits.