arXiv research

New algorithm prevents strategic replication in multi-armed bandit problems.

Study liquidity provision in decentralized exchanges considering risk aversion and replication costs.

Bayesian models can be tricked into believing false data.

Paper examines financial engineering problems and introduces AlphaZero for better replication strategies.

LLMs mimic human traders in finance, but not as much as expected.

Study explores algorithmic collusion in repeated games using various learning dynamics.

New framework for robust uncertainty quantification in strategic settings.

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

New framework reduces strategic manipulation cost for minority groups in fair classification.

Study compares employers with and without anticipating strategic labor force responses.

New algorithm ensures consistent results in constrained MAB problems.

Unified framework for fixed-income pricing and liability replication.

Characterizes super-replication prices in a financial market model.

New study on replicability and stability in machine learning algorithms.

Study on computational aspects of replicable learning, bridging statistical and algorithmic perspectives.

The paper classifies self-replicating 3D shapes using algebraic models.

New algorithm learns optimal policies in strategic MDPs with private types.

Deep neural network (DNN) has demonstrated its success in multiple domains. However, DNN models are inherently vulnerable to adversarial examples, which are generated by adding adversarial perturbations to benign inputs to fool the DNN model to misclassify. In this paper, we present a cross-layer strategic ensemble fra…

New findings show strategic interactions can undermine model expressiveness in machine learning.

Extends super-replication theorem with dynamic strategies and transaction costs.

Study reveals statistical bias in dataset replication, reducing accuracy drop from 11-14% to 3.6%.

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

Study replicability in high-dimensional statistics, resolving open problems.

New uniformity tester ensures consistent results across different samples.

ERICA assesses replicability of cluster analysis results.

Strategic feature selection in high-stakes domains like healthcare.

Study allocates resources to strategic agents while balancing cost and incentives.

In this work we introduce the notion of fully incomplete markets. We prove that for these markets the super-replication price coincide with the model free super-replication price. Namely, the knowledge of the model does not reduce the super-replication price. We provide two families of fully incomplete models: stochast…

The paper analyzes trading strategies in a competitive market with incomplete information.

By the classical Martingale Representation Theorem, replication of random vectors can be achieved via stochastic integrals or solutions of stochastic differential equations. We introduce a new approach to replication of random vectors via adapted differentiable processes generated by a controlled ordinary differential …

Algorithm learns optimal coordination for strategic agents in uncertain settings.

Randomised classifiers outperform deterministic ones in strategic classification.

We study super--replication of contingent claims in markets with fixed transaction costs. This can be viewed as a stochastic impulse control problem with a terminal state constraint. The first result in this paper reveals that in reasonable continuous time financial market models the super--replication price is prohibi…

Study one-shot strategic classification under unknown costs, improving worst-case accuracy.

Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals…

Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first …

We consider a symmetric multi-players zero-sum game with two strategic variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…

Adaptive replication improves stochastic function optimization.

Replicable clustering algorithms for k-medians, k-means, and k-centers are proposed.

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulle…

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as lo…

In this paper we propose a new robust algorithm to find the optimal static replicating portfolios for general nonlinear payoff functions and give the estimate of the rate of convergence that is absent in the literature. We choose the static replication by minimizing the error bound between the nonlinear payoff function…

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

Strategic feature manipulation helps learners identify meaningful variables in online regression settings.

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with l…

We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that h…

Paper evaluates synthetic retail data for fidelity, utility, and privacy.