Proposes a new investment strategy to optimize portfolio value within a target range.
problem Maximizing portfolio value within a specified range of returns.
method Two-stage least squares Monte Carlo method to handle complex payoffs.
result STRS strategy effectively contains portfolio value within the targeted range, improving risk-return trade-off.
Study examines pricing of target volatility options in fractional SABR model.
problem Pricing target volatility options in the lognormal fractional SABR model.
method Used Ito's calculus for a theoretical replicating strategy and derived approximations and closed-form expressions.
result Accuracy of approximations for target volatility option pricing in various parameter ranges.
SentRNA improves RNA design by integrating human strategies.
problem Designing sequences for large or complex RNA targets.
method SentRNA uses a neural network trained on human-designed RNA sequences.
result SentRNA solves complex targets previously unsolvable by machines.
Enhances neural network robustness with Mixup and TLAT.
problem Neural networks are sensitive to various perturbations and adversarial examples.
method Combines Mixup augmentation with Targeted Labeling Adversarial Training (TLAT).
result M-TLAT increases robustness against 19 corruptions and 5 adversarial attacks without reducing clean sample accuracy.
The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on generic solutions that can be applied to a wide range of problems. However, in practice, it is often the case…
TarMAC targets and coordinates multi-agent communication for cooperative tasks.
problem Coordinating multi-agent reinforcement learning in partially observable environments.
method Targeted multi-round communication approach without supervision.
result Improved performance and sample efficiency in diverse environments.
Paper tackles regularization and sparsification for quaternion neural networks.
problem Regularizing and sparsifying quaternion neural networks for compactness and real-time applications.
method Developed targeted regularization strategies for quaternion neural networks, extending l1 and structured regularization.
result Tailored strategies significantly reduce the number of connections and neurons, resulting in smaller, more compact networks.
Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resourc…
Proposes a new online learning strategy for multi-target regression in data streams.
problem Challenges in learning from high-throughput data streams, especially in multi-target regression.
method Extends existing online decision tree learning algorithm to consider inter-target dependencies.
result SST-HT presents superior predictive accuracy compared to state-of-the-art algorithms.
Enhances quantum sensing by eliminating multiple oscillations in field amplitude estimation.
problem Multiple oscillations in field amplitude estimation due to inter-qubit interactions at high qubit densities.
method Adopting a quantum circuit learning framework to approximate a target function by optimizing gate parameters.
result Elimination of multiple oscillations, leading to enhanced dynamic range of quantum sensing.
Adaptive kernel density estimation improves accuracy in high dimensions.
problem Challenges in high-dimensional density estimation with traditional methods.
method Pre-training a neural network to recommend location-adaptive kernels.
result Effective density estimation in high dimensions with improved accuracy.
Target Date Funds may not adapt well, leading to poor performance.
problem Target Date Funds may not adapt to investors' needs effectively.
method Modeling with historical returns and bootstrap resampling to compare performance.
result Adaptive investment strategies significantly outperform Target Date Funds.
New method accelerates Bayesian imaging using Langevin sampling.
problem Bayesian inference in imaging inverse problems with convex geometry.
method Stochastic relaxed proximal-point iteration targeting posterior distribution.
result Accelerated convergence for κ-strongly log-concave targets. CRPS improves GP-based sequential design for chemical space.
problem Finding molecules with specific properties in synthetic chemistry.
method Threshold-weighted CRPS as acquisition function for GP models in sequential design.
result Improved performance in molecule research with CRPS-based strategies.
Robotic grasping system learns to target objects from a single image.
problem Robotic grasping in unstructured environments.
method Distributed reinforcement learning, active vision, synchronous SGD.
result System learns to grasp unseen objects without retraining.
Universal preconditioning reduces sequential prediction regret.
problem Improving sequential prediction performance.
method Convolve target sequence with orthogonal polynomial coefficients.
result First sublinear and hidden-dimension-independent regret bounds.
We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…
End-to-end learnable Gaussian mixture priors improve diffusion models' exploration and expressiveness.
problem Challenges in diffusion models when priors differ from target distributions.
method End-to-end learnable Gaussian mixture priors (GMPs) with iterative refinement.
result Significant performance improvements across various benchmark problems.
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
problem Traditional Markowitz optimization limits to a single time scale, ignoring variance across different frequencies.
method Introduces multifrequency optimization allowing specification of target Hurst exponents across multiple time scales.
result Effective risk management strategy that aligns with investor preferences at various time scales.
AMMs enforce target-weighted portfolios, outperforming traditional funds in returns and tracking error.
problem Enforcing target-weighted portfolios in decentralized exchanges.
method Introducing a multi-asset fee structure to enforce a geometric mean market maker invariant, allowing compliance with the mandate to be verified directly from pool holdings.
result G3M portfolios outperform traditional funds in annualized returns and tracking error for certain fee ranges.
Proposes CRA framework for certifying fair predictive models.
problem Certifying fairness of predictive models trained on biased data.
method Formulates CRA for fairness queries, uses background knowledge and limited target population stats.
result Builds certifiably fair predictive models on target population.
Framework learns best model from diverse pretrained models for distribution shift.
problem No single pretrained model is best for all downstream tasks under distribution shift.
method Frontier Learning constructs a unified feature from white-box and black-box models, fitting a lightweight learner.
result Frontier Learning matches or outperforms strongest individual reuse strategy across settings.
Developing a range-aware Bayesian optimization framework for discovering diverse designs within target property windows.
problem Discovering multiple, distinct solutions within target property windows.
method Range-aware Bayesian optimization framework.
result Consistently recovers larger and more diverse sets of valid designs.
Dynamic rule-based investment strategies outperform static ones in pension schemes.
problem Managing retirement income with dynamic investment strategies.
method Rule-based investment strategies compared to dynamic programming.
result Rule-based strategies achieve higher probability of meeting retirement income targets.
New model uses pretrained biochemical language models to generate drug compounds.
problem Developing novel compounds targeting specific proteins.
method Exploits pretrained language models to initialize and fine-tune targeted molecule generation models.
result Warm-started models outperform baseline models, with one-stage strategy showing better generalization.
Commodity exchange-traded funds (ETFs) are a significant part of the rapidly growing ETF market. They have become popular in recent years as they provide investors access to a great variety of commodities, ranging from precious metals to building materials, and from oil and gas to agricultural products. In this article…
A new method uses deep invertible transformations to parallelize MCMC for large datasets.
problem Scaling MCMC methods to large distributed datasets.
method Introduces a deep invertible transformation to approximate subposteriors, enabling efficient parallel MCMC.
result Demonstrates superior performance compared to existing methods in various challenging scenarios.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.
problem Evaluating PDA methods without target labels and inconsistent experimental settings.
method Realistic evaluation of 7 PDA methods with 7 model selection strategies on 2 datasets.
result Accuracy drops up to 30 percentage points without target labels, only one method performs well.
Unified framework for response-adaptive targeting in multi-treatment experiments
problem Improving ethical and statistical efficiency in multi-treatment clinical trials
method Response-adaptive targeting strategies
result Unified framework for α-Rebalancing Targeting Strategies (αRTS) Optimal rebalancing strategy improves AMM pool performance by 25%.
problem Optimizing the sequence of weights in dynamic AMM pools to minimize rebalancing costs.
method Using optimal interpolation and a cheap-to-compute approximation to achieve nearly optimal rebalancing.
result Approximately-optimal weight changes lead to significant increases in pool performance (up to 25%) under various conditions.
We consider the problem of hedging a European contingent claim in a Bachelier model with transient price impact as proposed by Almgren and Chriss. Following the approach of Rogers and Singh and Naujokat and Westray, the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strat…
A heuristic method for determining input ranges for complex processes.
problem Determining input variable ranges for non-numeric, high-dimensional processes.
method Create synthetic training data and use a decision tree classifier.
result Validated on a real use case in a lamination factory.
Model predicts trading strategies based on latent demand and price impact.
problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.
L-ARC improves model fairness by localizing risk guarantees.
problem Improving model fairness in tasks like image segmentation and wireless networks.
method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.
result L-ARC produces prediction sets with improved fairness across different data subpopulations.
This paper develops closed-end formulas for VolTarget options.
problem Analyzing options linked to dynamic asset allocation strategies.
method Develops closed-end formulas for VolTarget options in a Black and Scholes setting.
result Closed-end formulas for option prices and hedging parameters.
This paper compares unsupervised domain adaptation methods for vision tasks.
problem Limited labeled data across domains leads to poor model performance.
method Classifies and compares non-deep and deep unsupervised domain adaptation methods.
result Summarizes and discusses potential directions for unsupervised domain adaptation.
Central bank strategy to maintain currency exchange rate within limits.
problem Maintaining a currency exchange rate within a target zone despite adverse economic trends.
method Modeling the problem with a continuous-time market impact model and solving it as a stochastic control problem.
result Optimal strategy minimizes accumulated inventory of foreign currency.
Paper proposes SiSTA for single-shot domain adaptation using target-aware generative augmentation.
problem Adapting models from source to target domains with limited target data.
method Fine-tunes a generative model on a single-shot target and uses novel sampling strategies for synthetic data.
result Improves performance by up to 20% over existing baselines in face attribute detection.
The paper improves guarantees for VI in symmetric cases.
problem Approximating intractable densities via VI with misspecified families.
method Extends previous robust VI results to wider divergences and non-log-concave targets.
result Guarantees for exact recovery of target mean and correlation matrix under various conditions.
Model predicts activist fund targets with 78.2% accuracy.
problem Predicting activist fund targets to mitigate risks and inform investments.
method Evaluated 123 model configurations using machine learning techniques.
result Best model achieved AUC-ROC of 0.782.
Develops a deep generative model for radar target recognition using HRRP data.
problem Automatic target recognition in radar systems using high-resolution range profiles.
method Recurrent gamma belief network (rGBN) with hybrid stochastic-gradient MCMC and variational inference.
result Efficient and accurate classification with interpretable latent structure.
This paper explores how representation learning can improve design-based causal inference.
problem Estimating causal effects in design-based studies is challenging due to the need for optimal weights.
method The authors propose an end-to-end estimation procedure that learns a flexible representation to minimize the error in choosing a representation.
result The proposed method is competitive in various causal inference tasks and shows promise for improving design-based weights.
We propose a general interpretation for long-range correlation effects in the activity and volatility of financial markets. This interpretation is based on the fact that the choice between `active' and `inactive' strategies is subordinated to random-walk like processes. We numerically demonstrate our scenario in the fr…
Bayesian optimization targets specific regions of the Pareto front in expensive multi-objective problems.
problem Finding the entire Pareto optimal set is impractical for expensive functions.
method Modified Bayesian multi-objective optimization using Gaussian Processes and targeting strategy.
result Efficient convergence to preferred regions of the Pareto front.
A novel diffusion method for Bayesian posterior sampling with theoretical guarantees.
problem Efficiently sampling from complex posterior distributions in Bayesian inversion.
method Diffusion-based posterior sampling using Langevin dynamics and PnP framework.
result The method converges even for multi-modal posterior distributions with theoretical error bounds.
New method targets sparsity to prevent overfitting in deep nets.
problem Overfitting in deep neural networks with small datasets.
method Targeted sparsity regularization to visualize and counteract overfitting.
result Significant increase in image classification performance without overfitting.
Investigates RI strategies for life insurers with LRD mortality rates.
problem Effect of long-range dependent mortality rates on RI strategies.
method Volterra mortality model, compound Poisson process, open-loop equilibrium mean-variance criterion.
result Explicit equilibrium RI controls derived and uniqueness studied.
New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.
problem Lack of accurate treatment effect estimates for machine learning-based targeting in social domains.
method Empirical assessment of targeting strategies using data from 5 real-world RCTs in various domains.
result Treatment effect-based targeting outperforms risk-based targeting, even with biased estimates.