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.
Study robustness of global feature effect explanations in machine learning models.
Income redistribution is the transfer of income from some individuals to others directly or indirectly by means of social mechanisms, such as taxation, public services and so on. Employing a spatial public goods game, we study the influence of income redistribution on the evolution of cooperation. Two kinds of evolutio…
Analyzes error sources in global feature effect estimation methods.
Study examines how balancing methods affect model behavior in imbalanced classification problems.
FedSel uses local differential privacy to protect data privacy in federated SGD.
GADGET framework decomposes global feature effects using recursive partitioning.
This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror des…
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gat…
Improved statistical computation through efficient matrix sampling.
Wealth inequality is an important matter for economic theory and policy. Ongoing debates have been discussing recent rise in wealth inequality in connection with recent development of active financial markets around the world. Existing literature on wealth distribution connects the origins of wealth inequality with a v…
The paper establishes criteria for spacetime inextendibility using asymptotic volume-distance-ratio analysis.
In this paper we consider sparse approximation problems, that is, general minimization problems with the -"norm" of a vector being a part of constraints or objective function. In particular, we first study the first-order optimality conditions for these problems. We then propose penalty decomposition (PD) me…
New framework uses simplicial and categorical methods to detect market inconsistencies.
Study compares price limit and circuit breaker effects in stock markets.
In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects,…
New data accumulation prevents model collapse in generative models.
Proves boundedness of log Fano cone singularities with bounded local volumes.
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unifie…
AdaX improves Adam by exponentially accumulating past gradients, leading to better performance in machine learning tasks.
We critically review recent claims that financial crashes can be predicted using the idea of log-periodic oscillations or by other methods inspired by the physics of critical phenomena. In particular, the October 1997 `correction' does not appear to be the accumulation point of a geometric series of local minima.
VCoTTA uses variational Bayesian methods to adapt models under continuous domain shifts.
We study local rigidity and multiplicity of constant scalar curvature metrics in arbitrary products of compact manifolds. Using (equivariant) bifurcation theory we determine the existence of infinitely many metrics that are accumulation points of pairwise non homothetic solutions of the Yamabe problem. Using local rigi…
Proposes a new method for interpreting feature importance and effects in dependent feature models.
Flexible model captures commodity skews with maturity effects.
FetchSGD reduces communication in federated learning with sketching.
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to an…
Study confirms boundedness of certain singularities in log Fano geometry.
MACQ method explains deep learning models by analyzing feature contributions across prediction levels.
Accumulated stock returns exhibit tempered skew t-distribution.
This paper presents a model of capital accumulation for a large number of heterogenous producer-consumers in an exchange space in which interactions depend on agents' positions. Each agent is described by his production, consumption, stock of capital, as well as the position he occupies in this abstract space. Each age…
Study finds bifurcation and local rigidity points for solutions to the Yamabe problem on Aloff-Wallach Spaces.
Bias correction needed after deep learning regression training.
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex. The global objective is to find a common solution that minimizes the average of all cost functions. Assuming agents only have access to unbiase…
The paper translates economic models into a field formalism to study capital accumulation and its fluctuations.
A2D2E designs optimal evaluations for estimating main effects in black-box models.
The paper develops a new method to interpret deep neural networks using Cohen's f2.
This note presents a kind of the strong law of large numbers for an insurance risk caused by a single catastrophic event rather than by an accumulation of independent and identically distributed risks. We derive this result by a large diversification effect resulting from optimal allocation of the risk to many reinsure…
A new method for MARL with partial observations reduces communication overhead.
This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a pa…
New method uses randomized sparse neural networks to solve time-dependent PDEs more accurately and efficiently.
We generalize to the finite-state case the notion of the extreme effect variable that accumulates all the effect of a variant variable observed in changes of another variable . We conduct theoretical analysis and turn the problem of finding of an effect variable into a problem of a simultaneous decomposition…
Unfolding paths in Outer space accumulate on a simplex, not converge.
In this paper we consider regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer.…
Machines, not humans, are the world's dominant knowledge accumulators but humans remain the dominant decision makers. Interpreting and disseminating the knowledge accumulated by machines requires expertise, time, and is prone to failure. The problem of how best to convey accumulated knowledge from computers to humans i…
We study an agent-based model of evolution of wealth distribution in a macro-economic system. The evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth and interactions between agents. We are mainly interested in interactions increasing wealth inequality that is in a …
New approach interprets machine learning models through feature space transformations.
Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming…
Model compression has been widely adopted to obtain light-weighted deep neural networks. Most prevalent methods, however, require fine-tuning with sufficient training data to ensure accuracy, which could be challenged by privacy and security issues. As a compromise between privacy and performance, in this paper we inve…