arXiv research

Modeling solar ramping events with spatio-temporal point processes.

Automates detection of fast-ramped flexibility events for DSOs.

Study develops a machine learning-based ramp metering model to improve freeway efficiency.

In this paper we show all possible ramps where an object can move with constant speed under the effect of gravity and friction. The planar ramp are very easy to describe, just rotate a curve with velocity vector (tanh(as),sech(as)). Recall that tanh(as)^2+sech^2(as) = 1. Therefore, the solution of the planar constant s…

The paper finds that circles and logarithmic spirals are the only constant-speed ramps for a specific force field.

Automates phased release strategy to balance risk and speed.

In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract …

We study dynamic hedging of counterparty risk for a portfolio of credit derivatives. Our empirically driven credit model consists of interacting default intensities which ramp up and then decay after the occurrence of credit events. Using the Galtchouk-Kunita-Watanabe decomposition of the counterparty risk price paymen…

SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-base…

Study improves adversarial classification using distributionally robust models.

In this paper we propose a tractable quadratic programming formulation for calculating the equilibrium term structure of electricity prices. We rely on a theoretical model described in [21], but extend it so that it reflects actually traded electricity contracts, transaction costs and liquidity considerations. Our nume…

Seesaw optimizes training by balancing learning rate and batch size, accelerating model pretraining.

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based mod…

Paper establishes generalization bounds for RNNs and improves existing results.

Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1) using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2) using traffic during the first year of operations as the basis for measu…

It has been experimentally observed in recent years that multi-layer artificial neural networks have a surprising ability to generalize, even when trained with far more parameters than observations. Is there a theoretical basis for this? The best available bounds on their metric entropy and associated complexity measur…

We use surrogate losses to obtain several new regret bounds and new algorithms for contextual bandit learning. Using the ramp loss, we derive new margin-based regret bounds in terms of standard sequential complexity measures of a benchmark class of real-valued regression functions. Using the hinge loss, we derive an ef…

iGNN tackles inverse graph prediction using invertible neural networks.

Let $f^{\star}$ be a function on $\mathbb{R}^d$ with an assumption of a spectral norm $v_{f^{\star}}$. For various noise settings, we show that $\mathbb{E}\|\hat{f} - f^{\star} \|^2 \leq \left(v^4_{f^{\star}}\frac{\log d}{n}\right)^{1/3}$, where $n$ is the sample size and $\hat{f}$ is either a penalized lea…

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for a…

Microlocal analysis provides deep insight into singularity structures and is often crucial for solving inverse problems, predominately, in imaging sciences. Of particular importance is the analysis of wavefront sets and the correct extraction of those. In this paper, we introduce the first algorithmic approach to extra…

Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning problem can be reduced to a minimax statistical learning problem by intr…

In this paper, we systemally study the long time behavior of the curve shortening flow in a closed or non-compact complete locally Riemannian symmetric manifold. Assume that we have a global flow. Then we can exhibit a a limit for the global behavior of the flow. In particular, we show the following results. 1). Let $\…

Case study shows impact of co-optimizing energy and reserve for wind energy.

Structured learning is appropriate when predicting structured outputs such as trees, graphs, or sequences. Most prior work requires the training set to consist of complete trees, graphs or sequences. Specifying such detailed ground truth can be tedious or infeasible for large outputs. Our main contribution is a large m…

Develops a neural model to predict event occurrence and timing.

Paper proposes a new trading strategy using corporate event detection from news articles.

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…

AUC is unreliable in rare event settings but stable with moderate numbers of events.

Non-spanning identification of scheduled event risk in option pricing.

New approach predicts event probabilities for better event detection.

Study examines HTE estimation from time-to-event data with competing events.

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

GANs improve event generation in physics experiments.

New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.

REST framework predicts stock trends by considering stock-specific and related-stock events.

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement pred…

New deep learning method handles rare and imbalanced events in time series.

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the cor…

Combining multiple collider events improves machine learning performance.

The study uses financial events to predict stock market movements.

Proposes a model for predicting events from event streams.

LOBDIF predicts limit order book events using a diffusion model.

Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time co…

Neural network model predicts alternating event-free periods.

The plausibility of uncommon events and miracles based on testimony of such an event has been much discussed. When analyzing the probabilities involved, it has mostly been assumed that the common events can be taken as data in the calculations. However, we usually have only testimonies for the common events. While this…

New STH distance finds patterns in event timeseries without resampling.