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.
Layer-wise preconditioning methods improve neural network optimization and feature learning.
Two novel methods identify influential features in CMABs for better reward distribution.
Develops NPSVC++ to improve NPSVC performance through representation learning.
Improved LoRA+ adapts large models more efficiently.
Paper identifies problematic baselines in Shapley value explanations and proposes a reweighting mechanism.
New study shows exponential lower bound for RL even with constant suboptimality gap.
Two entropy measures quantify suboptimal portfolio performance.
Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims …
Motivated by an abstract notion of low-level edge detector filters, we propose a simple method of unsupervised feature construction based on pairwise statistics of features. In the first step, we construct neighborhoods of features by regrouping features that correlate. Then we use these subsets as filters to produce n…
Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data. Because of this, we believe SVMs are an excellent candidate to guide the development of an analytic feature selection algorithm, as opposed to the more commonly used heuristic methods. We propose a filter-based feature…
Sparse feature selection has been demonstrated to be effective in handling high-dimensional data. While promising, most of the existing works use convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we expand a nonconvex paradigm to sparse group …
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which…
Novel IRL method identifies suboptimal medical decisions in ICU data.
A graph neural network detects beneficial feature interactions for recommender systems.
Time series data constitutes a distinct and growing problem in machine learning. As the corpus of time series data grows larger, deep models that simultaneously learn features and classify with these features can be intractable or suboptimal. In this paper, we present feature learning via long short term memory (LSTM) …
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated a…
Scalable feature selection improves GBDT model training speed.
New suboptimal algorithm for best subset selection in high-dimensional data.
This paper introduces a new property of estimators of the strength of statistical association, which helps characterize how well an estimator will perform in scenarios where dependencies between continuous and discrete random variables need to be rank ordered. The new property, termed the estimator response curve, is e…
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn opt…
We develop a robust RL algorithm for off-dynamics environments with improved suboptimality bounds and computational efficiency.
DFIV uses deep features for IV regression, achieving optimal rates.
The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where MP conver…
Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the statistical properties of random Fourier features are still not well understood. In this paper we take steps toward filling this gap. Specifica…
Strategic feature selection in high-stakes domains like healthcare.
Rotation invariant algorithms fail on sparse problems even with noise.
The Lasso is suboptimal in sparse linear regression due to design matrix constraints.
Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped- regularization wa…
This paper shows feature importance remains valid even in low-performing models.
ERM performs well in feature learning with minimal feature maps.
Paper explores limits of imitation learning in MDPs, setting new suboptimality bounds.
New bounds on adaptivity cost in stochastic optimization.
CatGCN improves GCNs by modeling feature interactions for categorical node features.
Practical model building processes are often time-consuming because many different models must be trained and validated. In this paper, we introduce a novel algorithm that can be used for computing the lower and the upper bounds of model validation errors without actually training the model itself. A key idea behind ou…
Enhances reinforcement learning from sparse data.
New algorithm reduces suboptimality in imitation learning to nearly optimal levels.
The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored. This kind of A/B testing is ubiquitous, but suboptimal, as the monitoring requires heavy human intervention, is not gu…
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variables involved. Much work has sim…
ProtoNAM models tabular data with neural networks, making predictions transparent.
Slow feature analysis (SFA) is an unsupervised-learning algorithm that extracts slowly varying features from a multi-dimensional time series. A supervised extension to SFA for classification and regression is graph-based SFA (GSFA). GSFA is based on the preservation of similarities, which are specified by a graph struc…
New algorithms for model selection in linear contextual bandits without feature diversity conditions.
Least Squares Estimators are suboptimal for 5D convex functions.
Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i.e., where there is a mismatch …
Training a source model optimally for its own task is suboptimal for downstream transfer.
DFSOS improves sparse discriminant analysis for high-dimensional data.
A new measure helps compute suboptimality in entropy-regularized methods.
A new algorithm for offline RL with trajectory-wise reward reduces bias and variance errors.
Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima. Despite recent progress, the reason…