arXiv research

Machine learning predicts CO2 emissions in power grids, reducing uncertainty.

Model predicts and optimizes trading of electricity price spreads across multiple zones.

Exchange improves liquidity by using different bid and ask tick sizes.

New ML model predicts long-term power generation at large areas.

Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dua…

Proposes a multilingual email segmentation benchmark and model.

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

Brillouin zones were introduced by Brillouin in the thirties to describe quantum mechanical properties of crystals, that is, in a lattice in $\R^n$. They play an important role in solid-state physics. It was shown by Bieberbach that Brillouin zones tile the underlying space and that each zone has the same area. We gene…

Study option pricing in sideways markets and target zones.

Paper presents a WiFi-based indoor sensor localization technique.

Study shows critical width for rigidity of equatorial zones on spheres.

Paper introduces a new power-dominance axis in estimator design.

For a given lattice, we establish an equivalence involving a closed zone of the corresponding Voronoi polytope, a lamina hyperplane of the corresponding Delaunay partition and a quadratic form of rank 1 being an extreme ray of the corresponding L-type domain.

We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using…

The Zeeman-Hamilton operators of free charged particles are identified with the Laplacians of certain Riemannian manifolds, called Zeeman manifolds. The quantum Hilbert space decomposes into subspaces (Zeeman zones) which are invariant under the actions both of the Zeeman operator and the natural Heisenberg group repre…

The Zone of Avoidance makes it difficult for astronomers to catalogue galaxies at low latitudes to our galactic plane due to high star densities and extinction. However, having a complete sky map of galaxies is important in a number of fields of research in astronomy. There are many unclassified sources of light in the…

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…

New ML method detects incomplete bid-rigging cartels.

A new relaxed framework for pricing illiquid derivatives using bid-ask spreads.

Optimizes portfolio with two controls to minimize trades and maintain signal integrity.

Algorithm learns to bid in auctions with shilling, masking real bids.

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…

BiCB combines traffic prediction and bidding optimization for live advertising.

BUZz defends images from adversarial attacks using simple transformations.

CoDeQ simplifies joint model compression by integrating pruning and quantization.

Develops a new bidding system to maximize advertiser profit.

Optimizes bidding strategy for Maker Protocol auctions.

Optimizes bidding strategies for LinkedIn ads across multiple platforms.

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

In the present paper which a sequel to dg-ga/9511005 and dg-ga//9610013 a global Weierstrass representation of an arbitrary closed oriented surface of genus $\geq 1$ in the the three-space is constructed. The Weierstrass spectrum of a torus immersed into $R^3$ is introduced and finite-zone planes as well as finite-zone…

New bid shading algorithm reduces costs by 55%.

Models of auctions or tendering processes are introduced. In every round of bidding the players select their bid from a probability distribution and whenever a bid is unsuccessful, it is discarded and replaced. For simple models, the probability distributions evolve to a stationary power law with the exponent dependent…

Deep network optimizes ad bidding for first-price auctions.

Proposes a virtual bidding strategy for electricity markets using stochastic control.

We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-tradin…

Optimizes RTB bidding without exploration, improving performance under various budgets.

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

In this paper a finite discrete time market with an arbitrary state space and bid-ask spreads is considered. The notion of an equivalent bid-ask martingale measure (EBAMM) is introduced and the fundamental theorem of asset pricing is proved using (EBAMM) as an equivalent condition for no-arbitrage. The Cox-Ross-Rubinst…

Study reveals different drivers of electricity price volatility across Europe.

We study optimal trading in an Almgren-Chriss model with running and terminal inventory costs and general predictive signals about price changes. As a special case, this allows to treat optimal liquidation in "target zone models": asset prices with a reflecting boundary enforced by regulatory interventions. In this cas…

Deep learning detects bid-rigging cartels with high accuracy.

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …

We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, $H$, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of $H$, and 2) a large value of $…

Model predicts bid and ask price dynamics with spread-dependent intensities.

Quantum theory explains price dynamics in financial markets, capturing bid-ask spread and ergodicity.

Algorithm finds safe zones in policy Markov Decision Processes to limit trajectory escape.

Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.

This paper improves bidding price prediction for ancillary services markets, boosting revenues.