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 policy combines Thompson sampling with best challenger rule for best arm identification.
Method learns cell interaction rules from individual trajectories.
Optimal best-arm identification in linear bandits reduces sampling budget.
Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed th…
This paper derives a robust on-line equity trading algorithm that achieves the greatest possible percentage of the final wealth of the best pairs rebalancing rule in hindsight. A pairs rebalancing rule chooses some pair of stocks in the market and then perpetually executes rebalancing trades so as to maintain a target …
New concept of proper-calibeating extends classic calibrated forecasts to proper scoring rules.
The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to efficiently process information at the edge. A key challenge, however, is to understand which learning rules are best suited for specific t…
EB-TCε identifies the best arm with ε confidence in stochastic bandits.
The paper proposes multicalibration to improve matching in graphs with imperfect predictors.
Optimal strategy found for identifying best arm in bandits with small gap.
PLANS synthesizes programs from noisy inputs using neural specs and filtering.
We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the `Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optim…
Develops anytime-valid stopping rules for SGD based on observed trajectory.
A new screening rule improves lasso solving speed.
Safe sample screening improves RSVM performance without sacrificing accuracy.
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop…
Paper tackles unknown variances in best-arm identification.
This paper prices and replicates the financial derivative whose payoff at is the wealth that would have accrued to a $\$1$ deposit into the best continuously-rebalanced portfolio (or fixed-fraction betting scheme) determined in hindsight. For the single-stock Black-Scholes market, Ordentlich and Cover (1998) only p…
We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of arbitrary training classifiers. If the classifiers are consistent, then so is the aggregation rule. Moreover, asymptotically the aggregation rule b…
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
New method discovers time series motifs under DTW, significantly reducing computations.
Improved BAI under DP reduces gap to constant.
Study optimizes best-arm identification with minimax and Bayes strategies.
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification. We further propose a variant of TTTS called Top-Two Transportation Cost (T3C), which disposes of the computational burden of TTTS. As our …
One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Single Nucleotide Polymorphisms (SNPs). Genome-wide association studies (GWAS) use single-locus analysis where each SNP is independently tested…
As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we p…
Algorithm identifies best arm with biased proxy and selective ground truth audits.
Study best arm identification with contextual info, achieving optimal misidentification probability.
Stochastic gradient descent (SGD) is the workhorse of modern machine learning. Sometimes, there are many different potential gradient estimators that can be used. When so, choosing the one with the best tradeoff between cost and variance is important. This paper analyzes the convergence rates of SGD as a function of ti…
Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local contr…
TransINT embeds KGs by preserving implication rules, outperforming existing methods.
A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their…
Optimal Volt/VAR control rules designed using deep learning.
We study T. Cover's rebalancing option (Ordentlich and Cover 1998) under discrete hindsight optimization in continuous time. The payoff in question is equal to the final wealth that would have accrued to a $\$1$ deposit into the best of some finite set of (perhaps levered) rebalancing rules determined in hindsight. A r…
Although technical trading rules have been widely used by practitioners in financial markets, their profitability still remains controversial. We here investigate the profitability of moving average (MA) and trading range break (TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from May 21, 1992 th…
ASTRA uses unlabeled data and weak rules to train deep models effectively.
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal detection can lead to an enhanced understanding of the underlying causes and influencing the assessment…
Proposes measures for uncertainty quantification using proper scoring rules.
New algorithms identify best policies in discounted linear MDPs efficiently.
Improving Bayesian filtering with strictly proper scoring rules
This paper explores how to choose scoring rules for estimating properties with parametric assumptions.
The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting classifier. Motivated by …
We propose a method to extract interpretable rules from tree ensembles.
FIRE extracts interpretable rules from tree ensembles.
Sharp bounds found on expert error in binary advice aggregation.
Tabular Q-learning outperforms advanced RL methods in monetary policy.