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.
EDU method finds diverse optimal solutions for expensive simulators.
DPA aligns LLMs with multi-objective rewards for diverse user preferences.
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable compar…
Strongly log-concave (SLC) distributions are a rich class of discrete probability distributions over subsets of some ground set. They are strictly more general than strongly Rayleigh (SR) distributions such as the well-known determinantal point process. While SR distributions offer elegant models of diversity, they lac…
Unified policy controls diverse agents through modular neural networks.
This paper shows how diverse tasks can make inefficient exploration in MTRL efficient.
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a reinforcement model to learn a policy for each task sequentially due to catastrophic forgetting. Specifically, the model would forget knowledge …
New ACE cost function encourages diversity in neural networks.
Two proofs of Kalman Theorem using flows of vector fields.
Fine-tunes diffusion models to generate diverse samples with high genuine rewards.
Unified framework for portfolio optimization using multiple hypotheses.
Proposes method to discover diverse near-optimal policies in reinforcement learning.
Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Goog…
Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possib…
In an adaptive population which models financial markets and distributed control, we consider how the dynamics depends on the diversity of the agents' initial preferences of strategies. When the diversity decreases, more agents tend to adapt their strategies together. This change in the environment results in dynamical…
New method designs joint initial noises for diffusion models to improve diversity and alignment.
Condorcet's Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet's Jury Theorem to the mean partitio…
The paper tackles data-driven optimal control of unknown nonlinear systems using RKHS.
FinFlowRL learns from experts to optimize financial control in changing markets.
Anticipatory model generates music with control over events.
Transformers learn to use induction heads or shortcuts based on data diversity.
Statistical test evaluates if personalizing interventions is cost-effective.
s-RBFN integrates multiple hypotheses for efficient and diverse prediction.
Presents STRIPE model for probabilistic forecasting of non-stationary time series.
Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instances and complex problems that require different low-level approaches. However, ensemble methods are relatively less popular in reinforcement …
Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from ob…
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input…
FinFlowRL combines imitation and reinforcement learning for better financial control.
Since governments give stimulus to firms and expect the spillover effect by fiscal policies, it is important to know the effectiveness that they can control the economy. To clarify the controllability of the economy, we investigate a firm production network observed exhaustively in Japan and what firms should be direct…
Paper proposes a new method to optimize robot body structure and control policy.
Survey of theoretical foundations for policy optimization in control.
Deep reinforcement learning controls drones without model knowledge.
State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in appl…
The paper improves recommendation systems by ensuring their outputs are reliable.
Generative Fractional Diffusion Models improve image diversity and quality.
Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers propo…
The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process --- creating audio samples from a score and instrument information --- is modeled using generative neural networks. This paper describes a neural music synthesis model with fl…
An ensemble of randomized NNs improves time series forecasting accuracy.
This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection. Although quality can improve …
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent s…
New algorithm solves mean-field control problems using actor-critic learning with moment neural networks.
DreamerV3 learns diverse tasks with a single configuration.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
Develops a new model for controllable and realistic traffic simulation.
PhysVarMix predicts diverse urban trajectories with physics constraints.
TASC improves synthetic control for time-series data with trends.
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specif…
Enhances FDR control in variable selection using neural networks.