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.
Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain fu…
Deep FPF approximates gain function for high-dimensional particle filtering.
Eluder dimension and information gain are equivalent for reproducing kernel Hilbert spaces.
Study optimal stopping problems with finite-time horizon and proves continuity and strict monotonicity of the boundary.
Introduces relative information gain for improving Gaussian process regression rates.
Unified framework for portfolio optimization using gain PDF.
Investment horizon approach has been used to analyze indexes of Polish stock market.Optimal time horizon for each return value is evaluated by fitting appropriate function form of the distribution. Strong asymmetry of gain-loss curves is observed for WIG index, whereas gain and loss curves look similar for WIG20 and fo…
Expands Bayesian experiment design framework to account for model discrepancies.
DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.
Meta-active learning optimizes control of safety-critical systems by efficiently learning dynamics and configurations.
Paper calculates greeks for DeFi LPs and introduces Impermanent Gain.
The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that enables us to reduce the solution space of submodular optimization problems without performance loss. We introduce the concept of reducibility…
We have developed a model for a life insurance policy. In this model the net gain is calculated by computer simulation for a particular type of lifetime distribution function. We observed that the net gain becomes maximum for a particular value of upper age of last premium. This paper is dedicated to Professor Dietrich…
Study calculates arbitrage gains between two markets with limited liquidity.
This paper examines an optimal investment problem in a continuous-time (essentially) complete financial market with a finite horizon. We deal with an investor who behaves consistently with principles of Cumulative Prospect Theory, and whose utility function on gains is bounded above. The well-posedness of the optimisat…
Bayesian SDOE method estimates QoIs from expensive black-box functions efficiently.
Bayesian optimal design of experiments (BODE) has been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE method…
Proposes EPIG for active learning to improve predictive performance.
ActiveCQ improves causal quantity estimation with active learning and Gaussian Processes.
Enhances robustness in experimental design through Generalised Bayesian inference.
Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility. However, by exploring the modeling structures, we find these "sacrifices" do not always require more computational efforts. To shed light on such a "free-lunch" phenomenon, we study the…
We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of t…
Develops a new framework for robust regression with EGM.
We present Rotated Adaptive Tetra-iterated Quantizer (RATQ), a fixed-length quantizer for gradients in first order stochastic optimization. RATQ is easy to implement and involves only a Hadamard transform computation and adaptive uniform quantization with appropriately chosen dynamic ranges. For noisy gradients with al…
In this paper we study the problem of stopping a Brownian bridge in order to maximise the expected value of an exponential gain function. In particular, we solve the stopping problem which was posed by Ernst and Shepp in their paper [Commun. Stoch. Anal., 9 (3), 20…
Efficiently identifies key input variables for expensive functions using active learning.
Study of historic stock returns distributions, highlighting asymmetry and outliers.
New method uses random decompositions for high-dimensional Bayesian optimization.
Cooperation is a persistent behavioral pattern of entities pooling and sharing resources. Its ubiquity in nature poses a conundrum. Whenever two entities cooperate, one must willingly relinquish something of value to the other. Why is this apparent altruism favored in evolution? Classical solutions assume a net fitness…
Optimistic search speeds up change point detection in large datasets.
The gain-loss ratio is known to enjoy very good properties from a normative point of view. As a confirmation, we show that the best market gain-loss ratio in the presence of a random endowment is an acceptability index and we provide its dual representation for general probability spaces. However, the gain-loss ratio w…
We study how convergence of an observer whose state lives in a copy of the given system's space can be established using a Riemannian metric. We show that the existence of an observer guaranteeing the property that a Riemannian distance between system and observer solutions is nonincreasing implies that the Lie derivat…
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such a…
Ensembles of classification and regression trees remain popular machine learning methods because they define flexible non-parametric models that predict well and are computationally efficient both during training and testing. During induction of decision trees one aims to find predicates that are maximally informative …
PC-GAIN improves GAIN's imputation by incorporating category information.
New guarantees for adaptive combinatorial maximization with various objectives.
The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over , and it is the result of the non-Pearson type auto-correlations in the index. These non-Pearson type correlations can be viewed also as functionally dependent daily volatilities, ext…
A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.
Study examines how liquidity constraints impact optimal retirement decisions.
In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to exp…
It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact that the space of permutations is not smooth. In this paper, we examine the class of rank-linear objective functions, which includes popular…
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We present new online algorithms for path learning with non-additive count-based gains for the three settings of full information, semi-bandit and…
We show how an ensemble of -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the -learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experimen…
Bayesian optimization selects experiments for causal structure learning in Gaussian process networks.
Experimental design is crucial for inference where limitations in the data collection procedure are present due to cost or other restrictions. Optimal experimental designs determine parameters that in some appropriate sense make the data the most informative possible. In a Bayesian setting this is translated to updatin…
Previous research has shown that for stock indices, the most likely time until a return of a particular size has been observed is longer for gains than for losses. We establish that this so-called gain/loss asymmetry is present also for individual stocks and show that the phenomenon is closely linked to the well-known …
Bayesian approach improves Shapley value estimation efficiency.
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.