The paper examines how algorithmic classification affects behavior and proposes democratizing stakes to mitigate predatory practices.
problem The impact of algorithmic classification on individual behavior and fairness in decision-making processes.
method Characterization of optimal classification by an algorithm designer and analysis of the effect of democratizing stakes.
result Optimal classification can lead to surprising behavior patterns, and democratizing stakes can mitigate predatory practices.
We present a model of predatory traders interacting with each other in the presence of a central reserve (which dissipates their wealth through say, taxation), as well as inflation. This model is examined on a network for the purposes of correlating complexity of interactions with systemic risk. We suggest the use of s…
Order matching systems form the backbone of modern equity exchanges, used by millions of investors daily. Thus, their operation is strictly controlled through numerous regulatory directives to ensure that markets are fair and transparent. Despite these efforts, market manipulation remains an open problem. In this work,…
While historically, economists have been primarily occupied with analyzing the behaviour of the markets, electronic trading gave rise to a new class of unprecedented problems associated with market fairness, transparency and manipulation. These problems stem from technical shortcomings that are not accounted for in the…
This paper examines unfair trading practices in NFT markets.
problem Sophisticated actors exploit market inefficiencies for unfair profits.
method Analyzes three types of opportunistic trading strategies.
result Identifies and categorizes unfair trading practices in NFT markets.
We study the competition of two strategic agents for liquidity in the benchmark portfolio tracking setup of Bank, Soner, Voß (2017). Specifically, both agents track their own stochastic running trading targets while interacting through common aggregated temporary and permanent price impact à la Almgren and Chriss (2001…
We consider a Nash equilibrium between two high-frequency traders in a simple market impact model with transient price impact and additional quadratic transaction costs. Extending a result by Schöneborn (2008), we prove existence and uniqueness of the Nash equilibrium and show that for small transaction costs the high-…
This paper optimizes liquidation strategies in DeFi protocols to prevent MEV attacks.
problem Predatory price manipulations and Maximal Extractable Value (MEV) attacks in DeFi protocols.
method Dynamic program modeling, Constant Product Market Maker (CPMM) transaction fees analysis.
result CPMM transaction fees make liquidation manipulations unprofitable for attackers.
The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…
Game theory model for optimal trading with end-of-day constraints.
problem Optimal trading strategy in a game between slow and fast traders.
method Coupled stochastic control problems, Fredholm integral equation solution.
result Explicit solution to the game with profitable strategies for both players.
Agent-based market shows herding cycles with square-root price impact.
problem Understanding herding cycles in agent-based markets.
method Agent-based model with 20,000 retail traders interacting with a single institutional agent.
result Agent discovers multi-cycle predatory strategy with 8-11 complete cycles over 2000 trading days.
Paper establishes no-regret property for practical EGO optimization.
problem No theoretical bounds on cumulative regret for practical EGO.
method Introduced practical EGO with a positive nugget, analyzed its regret bounds.
result Practical EGO is a no-regret algorithm with sublinear regret bounds.
Gluon optimizes LMO-based methods for large-scale tasks, improving performance and theory-practice gap.
problem LMO-based methods lack theoretical support for practical implementation and smoothness assumptions.
method Introduces Gluon, a new LMO-based method with refined smoothness model.
result Gluon's theoretical stepsizes match fine-tuned values, closing the theory-practice gap.
Classifies privacy policy segments for better user understanding.
problem Difficulty in understanding privacy policies due to legal jargon.
method Uses machine learning and deep learning techniques to classify privacy policy segments.
result Identifies data practices in privacy policies for better user comprehension.
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic i…
Overlay framework simplifies exotic derivative pricing.
problem Inconsistent pricing models for exotics.
method Combines path reweighting and conic optimisation.
result Practically model-independent price bands for exotics.
The study compares machine learning models for depression detection and highlights the importance of feature selection.
problem The choice of features in machine learning models for depression detection is crucial.
method Comparison of seven machine learning models on depression detection tasks.
result Optimal feature selection is essential for accurate and clinically acceptable classification solutions.
Study assesses environmental management accounting practices in Bangladesh.
problem Low environmental management accounting practices in Bangladeshi manufacturing companies.
method Developed a compliance checklist and evaluated practices using binary scoring.
result Environmental management accounting practices are poor in Bangladeshi manufacturing companies.
New framework provides privacy guarantees for practical federated learning.
problem Inadequate privacy guarantees for federated learning due to restrictive assumptions.
method Fed-α-NormEC, integrating multiple local updates, partial client participation, and standard assumptions. result Provably convergent and differentially private federated learning framework.
A practical guide to Variational Bayes methods.
problem Bayesian inference for data analysis practitioners.
method Explains and demonstrates commonly used Variational Bayes methods.
result Quick derivation and implementation of VB algorithms.
In the Best-K identification problem (Best-K-Arm), we are given N stochastic bandit arms with unknown reward distributions. Our goal is to identify the K arms with the largest means with high confidence, by drawing samples from the arms adaptively. This problem is motivated by various practical applications and…
NTK theory fails to predict practical behavior of large-width neural networks.
problem Theoretical limits of NTK do not match practical neural network architectures.
method Empirical investigation of NTK's applicability to large-width architectures.
result Practically relevant behavior of large-width architectures differs from NTK theory.
In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for a series of tests. She would typically take a few practice tests to know which areas she needs to improve upon. Based of the scores she obta…
BLAE solves batched linear bandits with optimal regret and practical performance.
problem Batched linear bandit problem with limited adaptivity.
method Integrates arm elimination with regularized G-optimal design, achieving minimax optimal regret.
result Achieves minimax optimal regret in both large-K and small-K regimes with O(loglogT) batches. A practical one-shot federated learning algorithm for cross-silo setting.
problem Limited applicability of existing one-shot federated learning algorithms due to specific model support and lack of privacy guarantees.
method FedKT, a one-shot federated learning algorithm that supports any classification models and provides differential privacy guarantees.
result FedKT significantly outperforms other state-of-the-art federated learning algorithms with a single communication round.
Study highlights how lazy data practices in fair ML research can unfairly impact minority groups.
problem Lazy data practices in fair ML research can unfairly impact minority groups.
method Systematic study of 280 experiments across 142 publications on 142 datasets.
result Unreflective data practices lead to biased findings and unfair treatment of minorities.
This thesis improves practical reinforcement learning methods with robustness, scalability, and efficiency.
problem Improving reinforcement learning methods for practical applications.
method Analyzes and develops robust, scalable, and efficient reinforcement learning algorithms.
result Proves the efficiency and robustness of new RL methods.
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial …
Proposes guidelines for developing medical AI products.
problem Lack of clear pathways for regulating medical AI.
method Statistical risk perspective and deep understanding of machine learning methodologies.
result Enhanced development of medical AI products and regulations.
New algorithm ensures global convergence in deep neural networks beyond NTK regime.
problem Existing global convergence guarantees do not apply to practical deep networks.
method Proposes an algorithm with global convergence guarantees under the expressivity condition.
result Algorithm ensures global convergence in practical settings beyond NTK regime.
Practical algorithm for contextual bandits with large action spaces.
problem Efficient algorithms for decision making in large, continuous action spaces.
method Uses computational oracles for supervised learning and optimization over the action space.
result Achieves sample complexity, runtime, and memory independent of the size of the action space.
RQMC improves QMC by providing practical error bounds for financial applications.
problem Lack of practical error estimates in QMC methods.
method Combines Sobol LDS with randomized scrambling methods.
result RQMC outperforms standard QMC in convergence rates and provides error bounds.
New method AnInfoNCE uncovers latent factors in contrastive learning with practical variability.
problem Theoretical assumptions of contrastive learning loss overlook practical variability in positive pairs.
method AnInfoNCE, a generalization of InfoNCE, models anisotropic variability to uncover latent factors.
result AnInfoNCE increases recovery of latent factors in CIFAR10 and ImageNet, albeit at the cost of accuracy.
PRoA assesses deep learning robustness against practical functional perturbations.
problem Inadequate practical robustness verification methods for deep learning systems.
method Probabilistic robustness assessment based on adaptive concentration.
result Statistical guarantees on probabilistic robustness against functional perturbations.
Addresses theoretical and practical aspects of Gaussian differential privacy.
problem Theoretical and practical challenges in privacy-preserving data analysis.
method Discussion of f-differential privacy and Gaussian differential privacy.
result Gaussian differential privacy can enhance privacy in various applications.
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy. We introduce three tools to ma…
Clinical AI models fail to transfer between sites due to site-specific practices.
problem Clinical AI models perform poorly at new sites.
method Identify and isolate site-specific clinical practices affecting data distribution.
result A potential solution to improve model transferability.
Stochastic Gradient Descent (SGD) is widely used in machine learning problems to efficiently perform empirical risk minimization, yet, in practice, SGD is known to stall before reaching the actual minimizer of the empirical risk. SGD stalling has often been attributed to its sensitivity to the conditioning of the probl…
Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also prov…
A practical algorithm improves approximate OT distances using quantization.
problem Substantial computational burden in computing OT distances for large samples.
method Introduces a quantization step to estimate OT distances between measures.
result The quantization step improves the performance of approximate solvers for entropy-regularized transport.
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Mean…
Review of Gerber-Shiu function for practical actuarial science.
problem Difficulty in numerical approximation and statistical inference of Gerber-Shiu function.
method Comprehensive review of formulations, surplus processes, numerical methods, and statistical inference.
result Enhanced understanding and practical guide for Gerber-Shiu function.
Aggregation defenses improve deep learning models' robustness against data poisoning attacks.
problem Data poisoning attacks manipulate deep learning models with malicious training samples.
method Deep Partition Aggregation, efficiency improvements, data-to-complexity ratio, poisoning overfitting phenomenon.
result Aggregation defenses boost poisoning robustness through the poisoning overfitting phenomenon.
One of the main practical applications of quasi-Monte Carlo (QMC) methods is the valuation of financial derivatives. We aim to give a short introduction into option pricing and show how it is facilitated using QMC. We give some practical examples for illustration.
This guide clarifies techniques for assessing and comparing model calibration and performance.
problem Assessing and comparing the calibration and performance of predictive models in insurance and actuarial practice.
method Clarifies statistical techniques for assessing model calibration and comparing models, emphasizing the importance of specifying the prediction target functional and choosing the appropriate scoring function.
result Provides guidance for the practical choice of scoring functions and illustrates results with real data case studies.
We consider the problem of online planning in a Markov Decision Process when given only access to a generative model, restricted to open-loop policies - i.e. sequences of actions - and under budget constraint. In this setting, the Open-Loop Optimistic Planning (OLOP) algorithm enjoys good theoretical guarantees but is …
New tensor completion method converges linearly and is highly practical.
problem Recovering low-rank tensors from sparse observations.
method Adapted alternating minimization to tensor setting.
result Linear convergence even with highly correlated factors.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
problem Minimizing surprise and catastrophe in high-frequency trading.
method Hierarchical RL with energy-based surprise value function.
result TradeR outperforms in abrupt price changes and maintains profitability.