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.
New formula calculates loss from arbitrage in blockchain liquidity pools.
Study shows AMM liquidity providers lose more than they earn, with varying profitability across pairs.
Paper proposes new method for time series confidence intervals using LSTM.
This study links blockchain design to cryptos' distributional characteristics.
Paper finds sharpness differences in transformer blocks accelerating LLM training.
SympFormer accelerates attention blocks using inertial dynamics on density spaces.
The paper explores IL and LVR in AMMs, identifying three regimes and the effect of fees.
The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dat…
A novel bandit problem with context-dependent rewards and blocking.
We consider convex SGD updates with a block-cyclic structure, i.e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution. This situation arises, e.g., in Federated Learning where the mobile devices available for updates at different times d…
JKO-iFlow uses neural ODEs to improve generative models with reduced memory and training complexity.
In this article, we develop a general framework to study optimal execution and to price block trades. We prove existence of optimal liquidation strategies and we provide regularity results for optimal strategies under very general hypotheses. We exhibit a Hamiltonian characterization for the optimal strategy that can b…
Bayesian models predict Collatz stopping times with high accuracy.
This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source bl…
New model captures time series dependence across and within blocks.
HaKAN uses Hahn-KAN blocks to forecast multivariate time series.
A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced. Members of the test family are called Block-tests or B-tests, since the test statistic is an average over MMDs computed on subsets of the samples. The choice of block size allows control over the tradeoff between test power and computatio…
Sharp pseudospectral bounds prevent transient amplification in coupled gradient descent.
Detects synchronized behavior in streaming data.
Study on efficiency of Dutch auctions on blockchains considering various parameters.
The performance of sparse signal recovery from noise corrupted, underdetermined measurements can be improved if both sparsity and correlation structure of signals are exploited. One typical correlation structure is the intra-block correlation in block sparse signals. To exploit this structure, a framework, called block…
DiffusionBlocks trains neural networks by breaking them into independent blocks, reducing memory usage.
Blockchain MEV is unaffected by ordering changes.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
Coordinate ascent variational inference is an important algorithm for inference in probabilistic models, but it is slow because it updates only a single variable at a time. Block coordinate methods perform inference faster by updating blocks of variables in parallel. However, the speed and stability of these algorithms…
This paper proposes a new method to improve VI approximations by capturing dependence between blocks using vector copulas.
New algorithms learn graph structures privately, matching best results.
This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same…
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classificat…
Block Coordinate Update (BCU) methods enjoy low per-update computational complexity because every time only one or a few block variables would need to be updated among possibly a large number of blocks. They are also easily parallelized and thus have been particularly popular for solving problems involving large-scale …
A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constrain…
N-BEATS-MOE improves time series forecasting by adapting to series characteristics.
The desire to map neural networks to varying-capacity devices has led to the development of a wealth of compression techniques, many of which involve replacing standard convolutional blocks in a large network with cheap alternative blocks. However, not all blocks are created equally; for a required compute budget there…
The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks…
Residual networks (ResNets) are a deep learning architecture that substantially improved the state of the art performance in certain supervised learning tasks. Since then, they have received continuously growing attention. ResNets have a recursive structure where is a neural network cal…
New model predicts network events better than existing ones.
We consider the problem of estimating the location of a single change point in a dynamic stochastic block model. We propose two methods of estimating the change point, together with the model parameters. The first employs a least squares criterion function and takes into consideration the full structure of the stochast…
New method for estimating financial covariance matrices efficiently.
A novel MCMC method clusters data faster and more accurately.
Efficient private algorithms for estimating block models and mixture models.
The latent Dirichlet allocation (LDA) model is a widely-used latent variable model in machine learning for text analysis. Inference for this model typically involves a single-site collapsed Gibbs sampling step for latent variables associated with observations. The efficiency of the sampling is critical to the success o…
The method of block coordinate gradient descent (BCD) has been a powerful method for large-scale optimization. This paper considers the BCD method that successively updates a series of blocks selected according to a Markov chain. This kind of block selection is neither i.i.d. random nor cyclic. On the other hand, it is…
Develops algorithms to optimize machine replacement schedules using operational data.
The paper reduces the complexity of financial market correlation matrices to a 2x2 matrix.
A new quantization strategy reduces Transformer model size and inference time.
New AI governance framework tackles risks in finance.
Researchers solve the realization of Jordan-Kronecker invariants in Lie algebras.
The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper…