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.
Algorithm extsc{Pedel} learns near-optimal policies efficiently on specific problems.
Mathematical Reinforcement Learning faces a 'Two-Hump' problem due to sparse rewards and a scarcity of intermediate 'hard-but-solvable' instances.
CLOPS improves deep learning for continuous physiological data.
New CTRL algorithm adapts to varying problem difficulty.
Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.
New method estimates optimal Q-values with better accuracy for specific problems.
This work embeds annotations into a multidimensional space to measure classification difficulty.
Learning shrinks hard tail, improving inference performance.
The research proposes a stopping rule for reinforcement learning algorithms based on instance-dependent confidence.
Gradient descent with specific initialization and step size achieves optimal sparse signal recovery.
Algorithm generates adaptive confidence sets for instance segmentation with guaranteed coverage.
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this paper, we propose the -IRT model, which models continuous responses and can generate a much enriched family of Item Characteristic Cur…
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
New algorithms improve contextual bandit performance by adapting to problem difficulty.
Matching one set of objects to another is a ubiquitous task in machine learning and computer vision that often reduces to some form of the quadratic assignment problem (QAP). The QAP is known to be notoriously hard, both in theory and in practice. Here, we investigate if this difficulty can be mitigated when some addit…
Note on the computational complexity of Gromov-Wasserstein distance.
Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems. To evaluate a MAX-SAT solver, it is convenient to generate hard MAX-SAT instances with known solutions. Here, we propose a method of generating weighted MAX-2-SAT insta…
Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previous analyses the learning rate was carefully tuned to obtain optimal worst-case performance, leading to suboptimal performance on easy instances, for example when there ex…
Adaptive compute allocation improves model performance by prioritizing harder queries.
New methods detect targets from imprecisely labeled hyperspectral data.
New method selects features for sequential decision making.
CORES2 removes noisy labels by sieving out corrupted examples.
In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different r…
This paper poses some basic questions about instances (hard to find) of a special problem in 3-manifold topology. "Important though the general concepts and propositions may be with the modern industrious passion for axiomatizing and generalizing has presented us...nevertheless I am convinced that the special problems …
Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifier's competence, the manner in which the pool is generated could affect the choice of the best cla…
New research shows that binary classification can be done with noisy data, but only if there are clean samples available.
Proposes an IRT-based ensemble method to improve machine learning accuracy.
Adaptive algorithms minimize regret in matching markets with contextual arm preferences.
Not only the Dirac operator, but also the spinor bundle of a pseudo-Riemannian manifold depends on the underlying metric. This leads to technical difficulties in the study of problems where many metrics are involved, for instance in variational theory. We construct a natural finite dimensional bundle, from which all th…
Study task-guided exploration in linear dynamical systems, improving sample complexity.
As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models diff…
This paper analyzes the difficulty of unsupervised domain adaptation using information theory.
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make ca…
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
Active learning method reduces labeling cost for regression models with aggregated data.
Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not advantageous, for instance, when tasks are considerably dissimilar or change over time. We use the connection between gradient-based me…
A new approach for instance-optimal learning that bypasses impossibility results.
Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play t…
Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have…
New algorithms optimize private convex optimization with faster rates for functions with κ-growth.
New IRT method identifies useful datasets for ML classifier evaluation.
Generates synthetic data for benchmarking unsupervised outlier detection.
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal…
In general, the clustering problem is NP-hard, and global optimality cannot be established for non-trivial instances. For high-dimensional data, distance-based methods for clustering or classification face an additional difficulty, the unreliability of distances in very high-dimensional spaces. We propose a distance-ba…
Inserting label noise can improve model accuracy and fairness.
We investigate the initial value problem for the Einstein-Euler equations of general relativity under the assumption of Gowdy symmetry on T3, and we construct matter spacetimes with low regularity. These spacetimes admit, both, impulsive gravitational waves in the metric (for instance, Dirac mass curvature singularitie…
NASirt automates CNN architecture design for spectral data.
The paper tackles fair sequential decision making with biased linear bandit feedback.