A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Review and compare sorting model selection methods for preference disaggregation.
The paper evaluates various forecasting methods for inflation, finding ML models superior.
New framework for interpreting disaggregated fairness evaluations using causal models.
SureMap estimates model performance across subpopulations efficiently.
Paper proposes a novel optimization method for disaggregating smart meter data.
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architectur…
As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation. According to previous studies, device level consumption could lead to significant freshwater conservation. Existing water disaggregation methods focus on learning the signatures for appliances; how…
We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common m…
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
This paper addresses the energy disaggregation problem, i.e. decomposing the electricity signal of a whole home to its operating devices. First, we cast the problem as a dictionary learning (DL) problem where the key electricity patterns representing consumption behaviors are extracted for each device and stored in a d…
tempdisagg transforms low-frequency data into high-frequency estimates.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We…
Develops algorithm to reduce real-world inequality.
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combin…
A new deep learning method for energy disaggregation.
Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models…
A new method improves AI fairness assessment by estimating performance across intersectional subgroups.
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic…
CTGAN synthesizes population data for travel behavior simulation.
Novel approach uses Gaussian processes to estimate conflict trends.
Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy…
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…
The S&P500 daily values and log-returns fail to conform to Benford's laws, revealing underlying trends.
Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.
Multidimensional data have become ubiquitous and are frequently encountered in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location. We often have access to multiple aggregated views of the same data, each aggregated i…
State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. The choice of aggregation map often depends on the data analysts' knowledge and is largely ad hoc. In this paper, we propos…
New method for disaggregate electricity demand forecasting at household level.
Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partiall…
We present an approach to estimate the severity of traffic related accidents in aggregated (area-level) and disaggregated (point level) data. Exploring spatial features, we measure complexity of road networks using several area level variables. Also using temporal and other situational features from open data for New Y…
Optimizes molecular generation for chemist preferences.
New method adapts to user preferences dynamically, improving recommendation models.
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
Enhances preference learning by incorporating response times into binary choices.
Bayesian optimization learns DM preferences for multi-outcome experiments.
New study shows personalized content recommendations can lead to polarization of user preferences.
Bayesian optimization agent learns user preferences from pairwise comparisons.
New RLHF framework handles general preference oracles without reward functions.
This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…
Study on identifying most preferred policy in bandits with vector-valued rewards.
Stable and consistent model alignment for language models without assuming human preference models.
Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.
Paper explores limits and possibilities of aligning LLMs with human preferences.
Paper improves parameter estimation of continuous distributions using preference feedback.
Paper investigates monotonicity issues in AI preference learning.