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.
Optimal trading strategy with predictor and costs, derived equations and shape.
AM-PPI uses multiple predictors to reduce label cost in healthcare AI.
Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its naive form, it can only deal with stochastic predictors while such predictors are rarely used and deterministic predictors often performs well in practice. To fill this gap, we develop…
New method minimizes regret in AMDP with high probability.
Optimal multi-asset trading with Markovian predictors is well understood in the case of quadratic transaction costs, but remains intractable when these costs are . We present a mean-field approach that reduces the multi-asset problem to a single-asset problem, with an effective predictor that includes a risk avers…
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discret…
WeakNAS uses a set of weaker predictors to find top architectures with fewer samples.
Study evaluates 31 performance predictors in NAS, recommending best for different settings.
Reducing ICD-10 code granularity improves cost model accuracy and stability.
We consider the problem of the optimal trading strategy in the presence of linear costs, and with a strict cap on the allowed position in the market. Using Bellman's backward recursion method, we show that the optimal strategy is to switch between the maximum allowed long position and the maximum allowed short position…
Derives bounds for deterministic predictors using smooth loss functions.
Study shows competition feedback can make ML predictors biased towards specific user groups.
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the selected variables, is well suited to small sample sizes, and is scalable to high dimensions. We establish asymptotic bounds f…
Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requires both abundant and high-quality pairs of architectures and their accuracy, while it is costly to evaluate an architecture and obtain its …
Derandomizing PAC-Bayes bounds for smooth loss functions
We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-tradin…
NPENAS improves neural architecture search efficiency and accuracy.
New framework improves adversarial robustness in one-stage L2D.
A new IL framework estimates invariant predictors with single domain data.
Modified Perceptron handles strategic agents with limited position changes.
New algorithms for multi-class classification with abstention.
APQ jointly optimizes neural architecture, pruning, and quantization for efficient inference.
This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and engineer…
Efficiently selects predictors in sparse regression without approximations.
Proposes a new method for decision-aware learning in optimization.
New method learns to encode predictions within interpretations, improving evaluation.
New method reduces training cost by using approximate gradients.
Paper proposes consistent estimators for learning to defer decisions to experts.
Using an artificial neural network (ANN), a fixed universe of approximately 1500 equities from the Value Line index are rank-ordered by their predicted price changes over the next quarter. Inputs to the network consist only of the ten prior quarterly percentage changes in price and in earnings for each equity (by quart…
New method converts LVAs into linear projections for better understanding of complex models.
A framework for ranking with abstention, offering theoretical guarantees and practical effectiveness.
Bayesian neural networks can be partially stochastic without losing predictive power.
We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given…
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We for…
As algorithmic prediction systems have become widespread, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. With the goal of understanding fundamental principles that underpin the growing number of approaches to mitigating algorithmic discrimination, we …
A significant hurdle for analyzing large sample data is the lack of effective statistical computing and inference methods. An emerging powerful approach for analyzing large sample data is subsampling, by which one takes a random subsample from the original full sample and uses it as a surrogate for subsequent computati…
MELO predicts electricity loads by adapting to shifts without external indicators.
Deciding what and when to observe is critical when making observations is costly. In a medical setting where observations can be made sequentially, making these observations (or not) should be an active choice. We refer to this as the active sensing problem. In this paper, we propose a novel deep learning framework, wh…
Optimal trading is a recent field of research which was initiated by Almgren, Chriss, Bertsimas and Lo in the late 90's. Its main application is slicing large trading orders, in the interest of minimizing trading costs and potential perturbations of price dynamics due to liquidity shocks. The initial optimization frame…
The problem of forecasting conditional probabilities of the next event given the past is considered in a general probabilistic setting. Given an arbitrary (large, uncountable) set C of predictors, we would like to construct a single predictor that performs asymptotically as well as the best predictor in C, on any data.…
A new screening method for high-dimensional data reduces computational cost.
A hybrid Convolutional VAE predicts crypto volatility surfaces, outperforming single-symbol approaches.
This study compares various superlearner and deep learning architectures (machine-learning-based and neural-network-based) for classification problems across several simulated and industrial datasets to assess performance and computational efficiency, as both methods have nice theoretical convergence properties. Superl…
FOLKLORE algorithm speeds up online multiclass logistic regression.
In spite of several notable efforts, explaining the generalization of deterministic non-smooth deep nets, e.g., ReLU-nets, has remained challenging. Existing approaches for deterministic non-smooth deep nets typically need to bound the Lipschitz constant of such deep nets but such bounds are quite large, may even incre…
Improves transfer learning by weighting importance based on test-over-training density.
This paper improves SGMs by using a predictor-corrector scheme to converge faster.
We consider the problem of -class classification (), where the classifier can choose to abstain from making predictions at a given cost, say, a factor of the cost of misclassification. Designing consistent algorithms for such -class classification problems with a `reject option' is the main goal of t…