Balances the regret of different algorithms in bandit and RL problems.
problem Model selection in bandit and reinforcement learning.
method Estimates and balances the empirical regrets of algorithms.
result Achieves near-optimal regret compared to the optimal base algorithm.
Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mecha…
A new XVA strategy rooted in balance sheet perspective improves equity process for bank shareholders.
problem Counterparty risk valuation adjustments (XVAs) in financial derivatives.
method Develops a cost-of-capital XVA strategy in a balance sheet perspective, solving explicitly in static setup and dynamically in trade context.
result Ensures a submartingale equity process corresponding to a target hurdle rate on capital at risk.
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framew…
BI-MAML learns multiple tasks without forgetting old ones.
problem Catastrophic forgetting in meta learning.
method Incremental model adaptation with balanced learning strategy.
result BI-MAML outperforms state-of-the-art models in accuracy and efficiency.
Optimal student loan repayment strategies vary based on loan size.
problem Finding the most cost-effective repayment strategy for federal student loans.
method Analyzing the impact of different repayment strategies on total cost for varying loan sizes.
result Optimal repayment strategies depend on the loan balance, with different approaches for small, large, and intermediate balances.
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. …
HedgeAgents boosts financial trading with balanced strategies.
problem Inefficient trading strategies under rapid market changes.
method Integrates LLMs with multi-agent system for robust decision-making.
result 70% annualized return and 400% total return over 3 years.
Paper optimizes energy trading on DA markets using RL.
problem Volatility and randomness in renewable energy sources.
method Markov Decision Process, reinforcement learning, evolutionary algorithm.
result RL-based strategy generates highest market profits.
Improves causal inference with observational data by balancing features and weights.
problem Achieving balance in predictive features for causal inference with observational data.
method Integrates balancing weights into representation learning for causal learning.
result Developed an algorithm for accurate estimation of causal effects.
Automates phased release strategy to balance risk and speed.
problem Balancing risk and speed in phased product releases.
method Formalizes as constrained batched bandit problem, uses adaptive Bayesian approach.
result Proposes algorithm that determines optimal release percentages.
Existence of balanced embedding proved for complex manifold into infinite-dimensional space.
problem Balanced embedding of non-compact complex manifolds into infinite-dimensional projective space.
method Gradient flow in a Hilbert space, long-time existence established by perturbation, convergence depends on a priori bounds.
result Existence of balanced embedding proved in a model case.
In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner. Since the weighted Laplacian strategy inherits the virtu…
Study examines how balancing methods affect model behavior in imbalanced classification problems.
problem Impact of balancing methods on model behavior in imbalanced classification problems.
method Used Explainable Artificial Intelligence tools (variable importance method, partial dependence profile, accumulated local effects) to compare model behavior before and after balancing.
result Significant changes in model behavior due to balancing methods, leading to biased models.
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challen…
New meta-learning method improves domain generalization by balancing parameters closer to domain centroids.
problem Improving domain generalization by reducing overfitting to specific domains.
method Arithmetic meta-learning with arithmetic-weighted gradients to balance parameters closer to domain centroids.
result Experimental validation of improved domain generalization performance.
Paper uses DRL to optimize portfolios, balancing risk and return.
problem Optimizing portfolios under market uncertainty and risk constraints.
method Integrates Sharpe ratio-based reward with risk control mechanisms, uses PPO for adaptive asset allocation.
result DRL agent stabilizes volatility but sacrifices risk-adjusted returns.
RL optimizes resource allocation in MG by balancing experience and exploration.
problem Optimal resource allocation in competitive scenarios.
method Introduced RL to MG, allowing dynamic strategy adjustment based on experience and expected rewards.
result Achieves optimal resource coordination by balancing exploitation and exploration.
Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships …
AlphaZero assesses new chess variants for balance and dynamics.
problem Designing engaging and balanced game rules, especially for chess variants.
method Used AlphaZero to learn near-optimal strategies for nine chess variants.
result AlphaZero reveals novel strategic and tactical patterns in chess variants.
This work improves policy-based training by proposing an evaluation balance objective for GFlowNets.
problem Reliable estimation of policy divergence under directed acyclic graphs remains challenging.
method Proposes an evaluation balance objective over partial episodes to measure policy divergence and improve policy-based training reliability.
result Evaluation balance strengthens policy-based training reliability and broadens its flexibility.
We introduce a variant of Shepp's classical urn problem in which the optimal stopper does not know whether sampling from the urn is done with or without replacement. By considering the problem's continuous-time analog, we provide bounds on the value function and in the case of a balanced urn (with an equal number of ea…
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
Compound interest as well as inflation grows exponentially with time, whereas other means to repay debt grow polynomially. For this and other, mostly political, reasons, debt without inflation is unsustainable. We suggest a discontinuous way to eliminate debt by nullifying it. This scenario is preferable to current cen…
Study shows sudden loss of balance in stock market networks after 2011, reducing predictability.
problem Reduced predictability in stock markets due to structural changes.
method Rank correlations and weighted signed networks to analyze interconnectivity and balance.
result Sudden loss of balance in stock market networks after 2011, leading to decreased predictability.
Optimizes dividend payments to balance risk and reward.
problem Maximizing mean-variance of accumulated discounted dividends up to ruin.
method Develops a time-consistent equilibrium strategy using a verification lemma.
result Equilibrium strategy is a barrier strategy for low risk aversion.
The basic financial purpose of corporation is creation of its value. Liquidity management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. These boo…
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.
Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized mean…
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific a…
Proposes a balanced multi-component and multi-layer neural network for efficient function approximation.
problem Accurately and efficiently approximating complex functions with high degrees of freedom and computational cost.
method Inspired by a multi-component approach, MMNN combines single-layer networks with a multi-layer decomposition strategy.
result Significant reduction in training parameters, more efficient training process, and improved accuracy compared to FCNNs or MLPs.
A new amortized variance-reduced gradient (AVRG) algorithm was developed in \cite{ying2017convergence}, which has constant storage requirement in comparison to SAGA and balanced gradient computations in comparison to SVRG. One key advantage of the AVRG strategy is its amenability to decentralized implementations. In th…
Optimizes liquidations in decentralized finance to manage credit risk.
problem Managing and liquidating positions in decentralized finance exchanges.
method Formulated as an ergodic optimal control problem, derived closed-form solutions for optimal liquidation strategies.
result Closed-form solutions balance immediate executions with price impacts and long-term rewards.
A simple strategy optimizes broker-client trading, reducing price discounts for informed traders.
problem Optimizing broker-client trading to balance client flow and informed trader losses.
method Modelled as a stochastic control problem, derived optimal strategy in closed form, introduced algorithm.
result Optimal strategy reduces price discounts for informed traders, balancing client flow and informed trader losses.
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
problem Impermanent loss in automated market makers (AMMs).
method Analytical derivation of a static replication formula using European options, and numerical example with real data.
result Guaranteed hedging coverage for all final prices within a predefined interval.
This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.
problem Predictive multiplicity due to Rashomon effect in high-stakes environments.
method Investigates the impact of balancing and filtering techniques on predictive multiplicity using 21 real-world datasets.
result Data-centric AI strategies can mitigate predictive multiplicity, but preprocessing methods may introduce it.
The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, s…
This paper balances bias and variance in adaptive importance sampling using mirror descent.
problem Large variance in adaptive importance sampling weights.
method Regularization strategy with power raised importance weights connected to mirror descent.
result The regularization parameter balances bias and variance.
This review analyzes deep learning methods for electricity price forecasting across different markets.
problem Insufficient analysis of deep learning methods in electricity price forecasting.
method Unified taxonomy of deep learning components, analysis of trends across markets.
result Shift toward probabilistic, microstructure-centric, and market-aware designs.
Optimizes liquidity withdrawal timing for AMM LPs to balance fees and impermanent loss.
problem Balancing fees and impermanent loss in automated market makers.
method Stochastic control problem with endogenous stopping time, numerical solutions via Euler scheme and Longstaff-Schwartz method.
result Optimal exit strategy depends on volatility, fees, and market dynamics.
This paper studies four trading algorithms of a professional trader at a multilateral trading facility, observing a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either internalizing or regular, either a hedge fund or a brokery agency. The spe…
TKIL improves class-balanced performance in incremental learning.
problem Catastrophic forgetting in sequential learning tasks.
method Introduces Tangent Kernel for Incremental Learning (TKIL) based on Neural Tangent Kernel (NTK).
result TKIL achieves better overall accuracy and variance across classes.
AgABC improves ABC algorithm by balancing exploration and exploitation.
problem Balancing global and local search abilities in ABC algorithm.
method Divide population into groups and assign different search strategies to members.
result Proposed AgABC algorithm outperforms other algorithms in accuracy and stability.
Study exact community recovery in noisy SBM with limited queries.
problem Community recovery in noisy stochastic block models with limited queries.
method Balanced uniform querying, two-stage adaptive strategy, sublinear queries, subsampled graph.
result Adaptive querying can improve exact recovery limits in noisy SBM.
CBDA improves active learning for semantic segmentation, especially with imbalanced classes.
problem Class imbalance degrades performance in domain adaptive active learning.
method Class Balanced Dynamic Acquisition (CBDA) selects more balanced labels for active learning.
result CBDA increases minority class performance and outperforms baselines by 0.6-2.4 mIoU.
We study a linear price impact model including other liquidity takers, whose flow of orders either follows a Poisson or a Hawkes process. The optimal execution problem is solved explicitly in this context, and the closed-formula optimal strategy describes in particular how one should react to the orders of other trader…
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
problem Improving investment strategy performance on noisy financial data.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced noise augmentation and bottleneck size significantly boost strategy effectiveness.
The paper addresses classification imbalance by framing it as a transfer learning problem.
problem Classification imbalance where one class is much rarer than the other.
method The paper studies oversampling procedures to balance classes, focusing on SMOTE and bootstrapping.
result The excess risk decomposes into balanced training rate and transfer cost, with SMOTE having a higher transfer cost.