The paper analyzes team formation on online platforms, tackling the exploration vs. exploitation dilemma.
problem Matching workers with tasks on online platforms, especially complex ones.
method Analyzed two settings: strongest member vs. weakest member, using regret bounds and optimal algorithms.
result Established fundamental regret bounds and designed near-optimal algorithms for team matchings.
Contextual bandits require careful exploration vs. exploitation to avoid biased outcome model estimation.
problem Contextual bandits face challenges in estimating outcome models due to rich heterogeneity and complex models.
method Developed parametric and non-parametric contextual bandits integrating balancing methods from causal inference and econometrics.
result First regret bound analyses for contextual bandits with balancing show competitive performance.
Proposes a heuristic to dynamically control exploration vs exploitation in Bayesian optimization.
problem The trade-off between exploration and exploitation in Bayesian optimization methods.
method Contextual Improvement heuristic to dynamically control the trade-off.
result Improves the speed and robustness of discovering optimal solutions.
Automated meta-learning for contextual bandits improves efficiency and performance.
problem Optimizing decision-making in dynamic environments like personalization and recommendation systems.
method End-to-end automated meta-learning pipeline using linearly annealed e-greedy policy.
result Model outperforms random exploration and other models with minimal tuning.
Greedy algorithm nearly outperforms exploration in contextual bandits.
problem Balancing exploration and exploitation in online learning.
method Smoothed analysis of the greedy algorithm in linear contextual bandits.
result Greedy algorithm nearly matches Bayesian regret rate under diversity conditions, with regret at most O ( T 1 / 3 ) O(T^{1/3}) O ( T 1/3 ) . New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
AutoML explores vs. exploits promising classifiers to improve performance.
problem Maximizing ML pipeline performance within limited time and resource constraints.
method Empirical study comparing exploiting vs. exploring the search space for promising classifiers.
result Exploiting the most promising classifiers does not statistically improve pipeline performance.
BINOCULARS improves experimental design by balancing exploration and exploitation.
problem Efficiently balancing exploration and exploitation in sequential experiments.
method BINOCULARS computes a batch of experiments, then selects a single point to evaluate, avoiding myopic approaches.
result BINOCULARS significantly outperforms myopic alternatives in real-world scenarios.
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 greedy algorithms improve Bayesian optimisation performance.
problem Optimizing continuous functions with exploration vs exploitation trade-offs.
method Introduced two novel ε-greedy acquisition functions and compared them with conventional methods.
result ε-greedy algorithms generally outperform conventional methods, especially in higher dimensions.
The paper explores the trade-off between recommendation system performance and bandwidth usage.
problem Balancing recommendation system performance with wireless bandwidth constraints.
method Analyzes two scenarios: multi-armed bandit with context and latent structure exploitation.
result Demonstrates a tradeoff between regret and bandwidth usage, with tight bounds for some instances.
PC-PG balances exploration and exploitation in reinforcement learning.
problem Local policy gradient methods struggle with exploration.
method PC-PG uses an ensemble of learned policies (policy cover) to balance exploration and exploitation.
result PC-PG provides strong theoretical guarantees and empirical validation.
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
problem Improving performance for risk-averse users in reinforcement learning.
method Introducing a tiered reinforcement learning approach with two policies: π e x t O π^{ ext{O}} π e x t O and π e x t E π^{ ext{E}} π e x t E . result Achieving constant regret for risk-averse users, independent of the number of episodes.
Algorithm optimizes two objectives in bandits: minimizing regret and identifying best arm.
problem Balancing exploration and exploitation for optimal performance in multi-armed bandits.
method Design and analysis of BoBW-lil'UCB ( γ ) (γ) ( γ ) algorithm, establishing lower bounds. result BoBW-lil'UCB ( γ ) (γ) ( γ ) achieves optimal performance for RM or BAI under different γ γ γ values. A scalable portfolio approach speeds up Bayesian optimization for noisy functions.
problem Efficiently selecting multiple designs in parallel for noisy, expensive black-box optimization.
method A portfolio approach that balances exploration and exploitation, using a scalable allocation strategy.
result Significant speed improvements over existing methods, with similar or better performance.
MADE improves exploration in RL by maximizing deviation from explored regions.
problem Efficient exploration in high-dimensional RL tasks with sparse rewards.
method Proposes a new exploration approach via maximizing the deviation of the occupancy of the next policy from explored regions, adding it as an adaptive regularizer to the RL objective.
result Significantly improves sample efficiency in navigation and locomotion tasks.
Paper explores how to use mixed types of side information for better recommendations.
problem Challenges in using heterogeneous side information for recommender systems.
method Proposes a framework to jointly capture flat and hierarchical side information.
result Demonstrates significant performance gains over state-of-the-art methods.
Thompson Sampling improves decision-making in partially observed contexts.
problem Balancing exploration and exploitation in partially observed contextual bandits.
method Thompson Sampling policy for learning optimal arms from noisy linear functions of unobserved context vectors.
result Thompson Sampling achieves poly-logarithmic regret and square-root consistency of parameter estimation.
New meta-RL method avoids exploration-exploitation trade-off.
problem Learning to explore and exploit simultaneously in meta-RL.
method Developed new objectives for exploration and exploitation.
result DREAM outperforms existing methods on complex tasks.
Dual supervised learning improves model performance for dual tasks.
problem Separate training of dual tasks misses probabilistic connections.
method Simultaneous training of dual tasks exploiting probabilistic correlations.
result Dual supervised learning improves practical performance across various applications.
Paper develops a dynamic Bayesian approach for active learning that optimizes exploration-exploitation balance.
problem Balancing exploration and exploitation in active learning for unknown functions.
method Develops BHEEM, a Bayesian hierarchical approach with approximate Bayesian computation for sampling trade-off parameters.
result BHEEM achieves at least 21% and 11% improvement over pure exploration and exploitation strategies respectively.
Pioneers a new network slicing solution for beyond-5G networks.
problem Ensuring diverse service level agreements (SLAs) in a multi-tenant environment with varying latency and throughput requirements.
method Leverages a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions based on system structure information.
result Near-optimal results in resource slicing decisions with no prior knowledge of traffic demand or channel quality statistics.
Overview of deep reinforcement learning, including elements and applications.
problem Optimizing decision-making in complex environments.
method Combining deep learning with reinforcement learning algorithms.
result Effective in diverse applications like games, robotics, and NLP.
GENE tackles sparse reward in RL by generating states to explore and exploit.
problem Sparse reward in reinforcement learning.
method Generative Exploration and Exploitation (GENE) method.
result GENE significantly outperforms existing methods in tasks with binary rewards.
Unified minimax value interval for off-policy evaluation and optimization.
problem Overcoming the exponential variance in off-policy evaluation and policy optimization.
method Unified minimax value interval using marginalized importance weights.
result Unified value interval with double robustness, valid when either value-function or importance-weight class is well specified.
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs CVaR); choice of return distribution (p…
The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights provide perhaps unexpected relationships between SVM and two other linear classifier…
New RL algorithm for large state spaces with explicit exploration and exploitation phases.
problem Reinforcement learning in large or infinite state spaces.
method Model-based approach with explicit exploration and exploitation phases.
result Proves near-optimal policy with polynomial sample complexity under certain assumptions.
NEXT learns efficient paths in high dimensions using neural exploration-exploitation trees.
problem Learning efficient path planning in high-dimensional spaces.
method Neural Exploration-Exploitation Trees (NEXT) integrating neural architecture and UCB algorithm.
result NEXT achieves better sample efficiency and outperforms state-of-the-art methods.
New AIM algorithm optimizes exploration-exploitation in bandits.
problem Balancing exploration and exploitation in decision-making.
method Approximate Information Maximization (AIM) algorithm.
result AIM outperforms Infomax and Thompson sampling with enhanced speed and tractability.
This work tackles the exploration-exploitation dilemma in RL by developing optimal policies that are inherently exploration-conscious.
problem The exploration-exploitation tradeoff in Reinforcement Learning, where policies need to balance new action exploration with past experience exploitation.
method Developed exploration-conscious criteria that result in optimal policies, solving these criteria by solving a surrogate Markov Decision Process.
result Demonstrated superior performance of exploration-conscious RL algorithms compared to non-exploration-conscious counterparts in both discrete and continuous action spaces.
This research tackles balancing exploration and exploitation in deep RL for partially observable systems.
problem Balancing exploration and exploitation in deep RL for partially observable systems.
method Deployed and tested several techniques including adaptive and deterministic exploration strategies, and a modified quadratic loss function.
result Adaptive methods better approximate the trade-off between exploration and exploitation.
Algorithm achieves optimal pricing with minimal exploration for dynamic markets.
problem Optimal pricing in dynamic markets with contextual information.
method Localized exploration-then-commit (LetC) algorithm with pure exploration, refinement, and exploitation stages.
result Achieves minimax optimal, dimension-free regret bound.
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.
Improved Thompson Sampling for Bayesian Optimization.
problem Handling the exploitation-exploration dilemma in Bayesian optimization.
method Incorporating epsilon-greedy policy into Thompson Sampling.
result Epsilon-greedy Thompson Sampling outperforms standard TS extremes.
Paper explores how knowledge distillation transfers inductive biases between models.
problem Transferring inductive biases between models for tasks with limited data.
method Knowledge distillation applied to models with different inductive biases (LSTMs vs. Transformers, CNNs vs. MLPs).
result Effect of inductive biases is transferred through knowledge distillation, impacting both performance and solution characteristics.
An agent explores indefinitely in an environment with unlimited rewards.
problem Balancing exploration and exploitation in environments with unlimited rewards.
method Simple example of an environment with unbounded rewards and optimal agent behavior.
result An optimal agent always explores to maximize rewards, regardless of accumulated knowledge.
Improved analysis of UCRL2 with empirical Bernstein inequality reduces exploration-exploitation regret.
problem Exploration-exploitation in communicating Markov Decision Processes.
method Analysis of UCRL2 with Empirical Bernstein inequalities (UCRL2B).
result Regret bound of O ~ ( D Γ S A T ) \widetilde{O}(\sqrt{DΓS A T}) O ( D Γ S A T ) for UCRL2B. In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally balances exploration and exploitation. In practice, however, EEM-based algorithms employ various approximations due to the computational ha…
Survey on methods for balancing exploration and exploitation in reinforcement learning.
problem Balancing exploration and exploitation in reinforcement learning, especially in domains with limited data.
method Survey of methods for computing robust solutions from fixed samples.
result Presentation of methods for balancing exploration-exploitation trade-off.
Proposes EE-Net for neural exploration in contextual bandits.
problem Exploitation-Exploration tradeoff in contextual bandits.
method Uses two neural networks: Exploitation and Exploration, to learn reward function and adaptively explore.
result Achieves O ( T log T ) \mathcal{O}(\sqrt{T\log T}) O ( T log T ) regret and outperforms existing methods. The paper explores learning good policies from past data in large state spaces.
problem Learning good policies from historical data in large state spaces.
method Introduces expressivity assumptions and data coverage for function approximation and algorithmic design.
result A variety of algorithms and their guarantees are presented based on assumptions and desired complexity.
Study compares two EEG reference points and finds LE montage improves machine learning performance.
problem Variability in EEG data affects machine learning performance.
method Comparison of Linked Ear (LE) and Averaged Reference (AR) montages in machine learning performance.
result A system trained on Linked Ear data outperforms one trained only on Averaged Reference data (77.2% vs. 61.4%).
Bayesian bandits use double sampling to balance exploration and exploitation.
problem Balancing exploration and exploitation in real-world systems.
method Develops a double sampling technique to balance exploration and exploitation in Bayesian settings.
result Empirically shows reduced cumulative regret compared to state-of-the-art alternatives.
MULEX separates exploration and exploitation in reinforcement learning.
problem Balancing discovery of new rewards with past behavior in reinforcement learning.
method Disentangles exploration and exploitation by optimizing multiple losses in parallel.
result MULEX achieves sample-efficiency and robustness in a hard-exploration environment.
This paper applies Thompson Sampling to asymmetric α \alpha α -stable bandits for financial and wireless data.
problem Optimizing exploration-exploitation in multi-armed bandits with asymmetric α \alpha α -stable distributions. method Thompson Sampling applied to unknown asymmetric α \alpha α -stable reward distributions. result Demonstrates effectiveness of Thompson Sampling for asymmetric α \alpha α -stable bandits. XploVAE improves recommendation by balancing known and novel items.
problem Balancing known and novel items for better recommendations.
method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.
result Demonstrates effectiveness on various real-world datasets.
Hyper addresses the hyperparameter tuning challenge in RL, improving exploration efficiency and robustness.
problem Hyperparameter tuning is a significant challenge in RL, especially for curiosity-based exploration methods.
method Hyper robustly explores by effectively regularizing exploration visits and decoupling exploitation.
result Hyper is provably efficient and robust in various RL environments.