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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for “exploit”

Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modelingcs.LG

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.

active learning Bayesian hierarchical modeling exploration-exploitation approximate Bayesian computation black-box function

2023-04-16abs ↗pdf ↗

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrificescs.LG

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.

meta-reinforcement learning exploration-exploitation end-to-end training local optima task-relevant information

2020-08-06abs ↗pdf ↗

Balancing Adaptability and Non-exploitability in Repeated Gamescs.GT

LAFF algorithm balances adaptability and non-exploitability in repeated games.

problem Low regret in repeated games against unknown opponent classes.

method LAFF algorithm searches within sub-algorithms optimal for each opponent class and uses a punishment policy for exploitation.

result LAFF guarantees sublinear regret uniformly over possible opponents, except exploitative ones, for which it guarantees linear regret.

repeated games non-exploitability regret minimization multi-agent learning

2021-12-20abs ↗pdf ↗

Generative Exploration and Exploitationcs.LG

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.

reinforcement learning sparse reward exploration exploitation binary rewards

2019-04-21abs ↗pdf ↗

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Treescs.LG

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.

path planning high dimensions neural networks exploration-exploitation sample efficiency

2019-02-28abs ↗pdf ↗

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Languagecs.LG

Autoconj automates conjugacy exploitation in Python without a DSL.

problem Time-consuming and error-prone conjugacy derivations.

method Operates on Python functions, supports any PPL.

result Accelerates development of inference algorithms.

conjugacy Python automated inference PPL

2018-11-29abs ↗pdf ↗

Approximate information for efficient exploration-exploitation strategiesstat.ML

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.

bandit problems exploration-exploitation AIM algorithm entropy gradient asymptotic bound

2023-07-04abs ↗pdf ↗

Epsilon-Greedy Thompson Sampling to Bayesian Optimizationcs.LG

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.

Thompson Sampling Bayesian Optimization Epsilon-Greedy Reinforcement Learning Exploitation-Exploration

2024-03-01abs ↗pdf ↗

Local Saddle Point Optimization: A Curvature Exploitation Approachcs.LG

Gradient-based optimization methods are the most popular choice for finding local optima for classical minimization and saddle point problems. Here, we highlight a systemic issue of gradient dynamics that arise for saddle point problems, namely the presence of undesired stable stationary points that are no local optima…

2018-05-15abs ↗pdf ↗

Explicit Explore-Exploit Algorithms in Continuous State Spacescs.LG

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.

reinforcement learning exploration-exploitation continuous state spaces neural networks sample efficiency

2019-11-01abs ↗pdf ↗

Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learningstat.ML

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.

Reinforcement Learning Deep Learning Exploration-Exploitation Autonomous Driving Deep Recurrent Q-Network

2023-10-12abs ↗pdf ↗

Improved Analysis of UCRL2 with Empirical Bernstein Inequalitycs.LG

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

\widetilde{O}(\sqrt{DΓS A T})

for UCRL2B.

UCRL2 Empirical Bernstein Inequality Exploration-Exploitation Markov Decision Processes Regret Analysis

2020-07-10abs ↗pdf ↗

Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsitystat.CO

A trend in compressed sensing (CS) is to exploit structure for improved reconstruction performance. In the basic CS model, exploiting the clustering structure among nonzero elements in the solution vector has drawn much attention, and many algorithms have been proposed. However, few algorithms explicitly consider corre…

2011-05-04abs ↗pdf ↗

Graph-Based Active Learning: A New Look at Expected Error Minimizationstat.ML

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…

2016-09-03abs ↗pdf ↗

New Paradigms for Exploiting Parallel Experiments in Bayesian Optimizationstat.ML

New BO methods exploit parallel experiments, reducing search time and improving solution quality.

problem Limitation of Bayesian optimization in exploiting parallel experiments.

method Propose new parallel BO paradigms that exploit the structure of the system to partition the design space.

result Significantly reduce search time and increase probability of finding global solutions.

Bayesian optimization parallel experiments performance function level sets partially-separable structures

2022-10-03abs ↗pdf ↗

AutoML: Exploration v.s. Exploitationcs.LG

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.

AutoML Meta-learning Classifier selection Hyper-parameter tuning Performance optimization

2019-12-23abs ↗pdf ↗

Exploiting arbitrage requires short sellingq-fin.MF

Short selling is key to exploiting arbitrage opportunities in financial markets.

problem Theoretical basis for differences in financial service regulations.

method Analyzing semimartingales to show arbitrage opportunities require short selling.

result Arbitrage opportunities can only be exploited through short selling.

arbitrage short selling financial markets semimartingales regulation

2020-11-25abs ↗pdf ↗

Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systemsstat.ML

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.

recommender systems variational auto-encoder exploitation exploration personalized recommendations

2020-06-05abs ↗pdf ↗

Better safe than sorry: Risky function exploitation through safe optimizationstat.AP

Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test…

2016-02-02abs ↗pdf ↗

Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Gamescs.LG

Study proposes new OPE estimators for two-player zero-sum games.

problem Evaluating new policies using historical data from a different policy in multi-player zero-sum games.

method Doubly robust and double reinforcement learning estimators to project exploitability.

result Prove exploitability estimation error bounds and regret bounds for policy profiles.

Off-policy evaluation Two-player zero-sum games Exploitability Doubly robust Double reinforcement learning

2020-07-04abs ↗pdf ↗

Reinforcement Learning through Active Inferencecs.LG

Active inference enhances RL by balancing exploration and exploitation.

problem Traditional RL's balance between exploration and exploitation is often suboptimal.

method Developed a new decision-making objective based on active inference.

result The new algorithm successfully balances exploration and exploitation on various RL benchmarks.

reinforcement learning active inference exploration-exploitation free energy decision making

2020-02-28abs ↗pdf ↗

Adversarial Exploitation of Policy Imitationcs.LG

The paper shows how imitation learning can be exploited to extract DRL policies.

problem The confidentiality and integrity of DRL policies are vulnerable to model extraction attacks.

method Developed proof-of-concept attacks using imitation learning techniques.

result Demonstrated the feasibility of launching black-box attacks against DRL policies.

Deep Reinforcement Learning Policy Imitation Model Extraction Attacks Adversarial Exploitation Integrity Attacks

2019-06-03abs ↗pdf ↗

Parallelizing Thompson Samplingcs.LG

Batch Thompson Sampling reduces exploration-exploitation trade-off in online decision making.

problem Balancing exploration and exploitation in online decision making.

method Introducing a batch Thompson Sampling framework for stochastic multi-arm bandit and linear contextual bandit problems.

result Achieves asymptotic regret bound with

O(\log T)

batch queries, significantly reducing interactions.

Thompson Sampling Online Decision Making Bandit Problems Exploration-Exploitation Exponential Reduction

2021-06-02abs ↗pdf ↗

Explore or exploit? A generic model and an exactly solvable casecond-mat.dis-nn

Finding a good compromise between the exploitation of known resources and the exploration of unknown, but potentially more profitable choices, is a general problem, which arises in many different scientific disciplines. We propose a stylized model for these exploration-exploitation situations, including population or e…

2013-10-18abs ↗pdf ↗

Kernel-Based Reinforcement Learning: A Finite-Time Analysiscs.LG

Kernel-UCBVI algorithm balances exploration and exploitation in metric state-action spaces.

problem Exploration-exploitation dilemma in finite-horizon reinforcement learning with metric state-action spaces.

method Kernel-UCBVI, leveraging smoothness and kernel estimators of rewards and transitions.

result First regret bound for kernel-based RL using smoothing kernels,

O(H^3 K^{2d/(2d+1)})

Kernel-based reinforcement learning Metric state-action spaces Exploration-exploitation Regret bound Finite-horizon problems

2020-04-12abs ↗pdf ↗

Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimizationstat.ML

Bayesian optimization offers the possibility of optimizing black-box operations not accessible through traditional techniques. The success of Bayesian optimization methods such as Expected Improvement (EI) are significantly affected by the degree of trade-off between exploration and exploitation. Too much exploration c…

2018-07-03abs ↗pdf ↗

Localized exploration in contextual dynamic pricing achieves dimension-free regretstat.ML

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.

dynamic pricing contextual learning regret minimization exploration-exploitation

2024-12-26abs ↗pdf ↗

Exploration-Exploitation in Constrained MDPscs.LG

The paper analyzes CMDPs, balancing exploration and exploitation to avoid constraint violations.

problem Balancing exploration and exploitation in CMDPs to satisfy constraints.

method Two approaches: optimistic planning and incremental updates of primal and dual variables.

result Both approaches achieve sublinear regret on utility and constraint violations, with stronger guarantees for the linear programming approach.

CMDPs exploration-exploitation constraint satisfaction optimistic planning dual formulation

2020-03-04abs ↗pdf ↗

A Short Survey on Probabilistic Reinforcement Learningcs.LG

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.

reinforcement learning exploration-exploitation robust solutions fixed samples balance

2019-01-21abs ↗pdf ↗

Bayesian bandits: balancing the exploration-exploitation tradeoff via double samplingstat.ML

Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates. One general class of algorithms for such learning is the multi-armed bandit setting. Randomized probability matching, based upon …

2017-09-10abs ↗pdf ↗

Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedulecs.LG

Paper finds wide minima are better for generalization and proposes a new learning rate schedule.

problem The challenge of finding optimal learning rates for model training.

method The paper introduces a new hypothesis about the density of wide minima and designs an explore-exploit learning rate schedule.

result The explore-exploit learning rate schedule improves model performance and reduces training time.

wide minima generalization learning rate explore-exploit accuracy

2020-03-09abs ↗pdf ↗

Exploiting oddsmaker bias to improve the prediction of NFL outcomesstat.AP

Accurately predicting the outcome of sporting events has been a goal for many groups who seek to maximize profit. What makes this challenging is that the outcome of an event can be influenced by many factors that dynamically change across time. Oddsmakers attempt to estimate these factors by using both algorithmic and …

2017-10-18abs ↗pdf ↗

AdaLinUCB: Opportunistic Learning for Contextual Banditscs.LG

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 bandits opportunistic learning exploration-exploitation regret minimization adaptive algorithms

2019-02-20abs ↗pdf ↗

Coregionalised Locomotion Envelopes - A Qualitative Approachstat.ML

'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with …

2018-03-13abs ↗pdf ↗

Greedy inference with structure-exploiting lazy mapsstat.CO

Framework uses low-dimensional maps to solve high-dimensional Bayesian inference problems.

problem High-dimensional Bayesian inference problems.

method Structure-exploiting lazy maps and flows, focusing on low-dimensional subspace.

result Weak convergence of generated distributions to the posterior.

Bayesian inference lazy maps structure exploitation Kullback-Leibler divergence inverse autoregressive flows

2019-05-31abs ↗pdf ↗

PAC-Bayesian Analysis of the Exploration-Exploitation Trade-offcs.LG

We develop a coherent framework for integrative simultaneous analysis of the exploration-exploitation and model order selection trade-offs. We improve over our preceding results on the same subject (Seldin et al., 2011) by combining PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a combinatio…

2011-05-23abs ↗pdf ↗

A Blessing in Disguise? DeFi Exploits and Short-Horizon Responses in U.S. Commercial Paper Spreadsq-fin.GN

DeFi exploits lead to reduced CP spreads, contrary to contagion hypothesis.

problem Vulnerabilities in DeFi destabilize traditional short-term funding markets.

method Analysis of commercial paper spreads and regulatory segmentation.

result DeFi exploits lead to a 'Flight-to-Quality' pattern, narrowing rather than widening CP spreads.

DeFi Commercial Paper Flight-to-Quality Regulatory Segmentation Operational Shocks

2026-01-13abs ↗pdf ↗

Hyperellipticity and Systoles of Klein Surfacesmath.DG

Given a hyperelliptic Klein surface, we construct companion Klein bottles, extending our technique of companion tori already exploited by the authors in the genus 2 case. Bavard's short loops on such companion surfaces are studied in relation to the original surface so to improve a systolic inequality of Gromov's. A ba…

2012-01-01abs ↗pdf ↗

In-context Exploration-Exploitation for Reinforcement Learningcs.LG

ICEE learns new RL tasks in less time with a Transformer model.

problem Efficient in-context policy learning for reinforcement learning.

method In-context Exploration-Exploitation (ICEE) algorithm that optimizes efficiency without explicit Bayesian inference.

result ICEE solves Bayesian optimization problems as efficiently as Gaussian process biased methods but in significantly less time.

reinforcement learning in-context learning exploration-exploitation Transformer model Bayesian inference

2024-03-11abs ↗pdf ↗

Exploration Conscious Reinforcement Learning Revisitedcs.LG

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.

Reinforcement Learning Exploration-Exploitation Optimal Policies Markov Decision Process Tabular RL

2018-12-13abs ↗pdf ↗

Thompson Sampling on Asymmetric

α

-Stable Banditsstat.ML

This paper applies Thompson Sampling to asymmetric $\alpha$ -stable bandits for financial and wireless data.

problem Optimizing exploration-exploitation in multi-armed bandits with asymmetric

\alpha

-stable distributions.

method Thompson Sampling applied to unknown asymmetric

\alpha

-stable reward distributions.

result Demonstrates effectiveness of Thompson Sampling for asymmetric

\alpha

-stable bandits.

Thompson Sampling Bandits Asymmetric $\alpha$-stable Exploration-Exploitation Financial Data

2022-03-19abs ↗pdf ↗

On Exploration, Exploitation and Learning in Adaptive Importance Samplingstat.ML

AIS algorithm balances exploration and exploitation for efficient sampling.

problem Balancing exploration and exploitation in adaptive importance sampling.

method Daisee algorithm, partition-based approach, pseudo-regret analysis.

result Daisee achieves

\mathcal{O}(\sqrt{T}(\log T)^{\frac{3}{4}})

cumulative pseudo-regret.

adaptive importance sampling bandit algorithms regret analysis online learning partition-based sampling

2018-10-31abs ↗pdf ↗

Zeroth-Order Supervised Policy Improvementcs.LG

ZOSPI improves RL policies with global value function exploitation.

problem Limited sample efficiency of PG algorithms in RL.

method Zeroth-order supervised policy improvement, leveraging global value function estimation.

result ZOSPI achieves competitive results with remarkable sample efficiency.

reinforcement learning policy gradient zeroth-order supervised learning sample efficiency

2020-06-11abs ↗pdf ↗

The tick-by-tick dynamical consistency of price impact in limit order booksphysics.soc-ph

Constant price impact functions, much used in financial literature, are shown to give rise to paradoxical outcomes since they do not allow for proper predictability removal: for instance the exploitation of a single large trade whose size and time of execution are known in advance to some insider leaves the arbitrage o…

2007-02-23abs ↗pdf ↗

MULEX: Disentangling Exploitation from Exploration in Deep RLcs.LG

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.

reinforcement learning exploration exploitation deep learning policy optimization

2019-07-01abs ↗pdf ↗

Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisationstat.ML

New PAC-Bayes bounds use Wasserstein distances to improve generalization.

problem Lack of geometric properties in existing PAC-Bayes bounds.

method Developed new PAC-Bayes bounds with Wasserstein distances.

result Optimization guarantees translate to good generalization abilities.

PAC-Bayes Wasserstein Generalization Optimization SGD

2023-04-14abs ↗pdf ↗

Classification of solitons for pluriclosed flow on complex surfacesmath.DG

We give a classification of compact solitons for the pluriclosed flow on complex surfaces. First, by exploiting results from the Kodaira classification of surfaces, we show that the complex surface underlying a soliton must be Kähler except for the possibility of steady solitons on minimal Hopf surfaces. Then, we const…

2018-02-01abs ↗pdf ↗

A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimizationcs.LG

The problem of optimizing unknown costly-to-evaluate functions has been studied for a long time in the context of Bayesian Optimization. Algorithms in this field aim to find the optimizer of the function by asking only a few function evaluations at locations carefully selected based on a posterior model. In this paper,…

2012-03-30abs ↗pdf ↗