arXiv research

Develops auction theory for real-life applications with positive valuations.

We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

Study optimizes auction pricing for strategic bidders in repeated auctions.

A Q-learning approach optimizes RTB ad campaigns for mobile app installs.

Deep learning predicts M&A events in industry networks.

Study learns optimal auctions from corrupted or perturbed bidder valuation samples.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

Study shows priority access in ELA auctions is less valuable due to volatility risks.

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the mini…

Study the Mexican stock market's interdependency structure from 2000-2019.

RPN 2 improves function learning by modeling data interdependence.

Model shows how financial contagion spreads through complex interdependencies.

Bidders in day-ahead electricity markets want to sell/buy electricity when their bids generate positive surplus and not to take an action when the reverse holds. However, non-convexities in these markets cause conflicts between the actions that the bidders want to take and the actual market results. In this work, we in…

New measure shows how LSTM models compose hierarchical representations.

Study non-stationary online auctions with semi-bandit feedback.

This paper develops a federated approach to learn Granger causality in interdependent industrial clients.

Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive …

For the first time ever, we analyze a unique public procurement database, which includes information about a number of bidders for a contract, a final price, an identification of a winner and an identification of a contracting authority for each of more than 40,000 public procurements in the Czech Republic between 2006…

Analyzes global economic sectors' interdependence using Google matrix analysis.

Paper tackles RCA in complex networks with unknown interdependencies.

CAUSE learns Granger causality from event sequences, outperforming existing methods.

This paper studies an environment of simultaneous, separate, first-price auctions for complementary goods. Agents observe private values of each good before making bids, and the complementarity between goods is explicitly incorporated in their utility. For simplicity, a model is presented with two first-price auctions …

Paper compares largest claim amounts from two interdependent portfolios.

This paper tackles optimal bidding strategies in adversarial first-price auctions.

To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the infor…

Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.

We generalize the scale-free network model of Barabàsi and Albert [Science 286, 509 (1999)] by proposing a class of stochastic models for scale-free interdependent networks in which interdependent nodes are not randomly connected but rather are connected via preferential attachment (PA). Each network grows through the …

In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective …

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

Extends Bayesian theory to handle complex interdependencies in multidimensional event spaces.

A new random forest algorithm uncovers feature interdependencies better than traditional methods.

Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.

Federated learning interprets temporal dynamics across clients with graph attention.

Combinatorial auctions are formulated as frustrated lattice gases on sparse random graphs, allowing the determination of the optimal revenue by methods of statistical physics. Transitions between computationally easy and hard regimes are found and interpreted in terms of the geometric structure of the space of solution…

Transformer model forecasts electricity price spread for virtual bidding.

Study optimal auction formats for maximizing MEV on Ethereum.

Paper analyzes bidding strategies in smart grid PDAs, proposing a new method that outperforms existing strategies.

Study maps interdependence of SDGs, finds complex, dynamic linkages.

Paper compares smallest claim amounts from two interdependent portfolios.

Empirical study on hyperparameters affecting RL generalization.

In this paper, we propose a new framework to study the generalization property of classifier chains trained over observations associated with multiple and interdependent class labels. The results are based on large deviation inequalities for Lipschitz functions of weakly dependent sequences proposed by Rio in 2000. We …

New approach for causal inference with interdependent, time-varying latent confounders.

The paper proposes a new method to forecast winning prices in real-time bidding.

Develops a support-aware framework for reserve-policy selection in advertising markets.

Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.