arXiv research

Open problem seeks an online learning algorithm for binary classification.

Paper analyzes mistake and generalization of MNIC classifiers.

We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model. We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane using a number …

Ahpatron improves online kernel learning with tighter mistake bounds.

A deterministic apple tasting learner is developed, confirming a conjecture and providing tight bounds for mistake bounds.

This note corrects one serious mistake and several smaller mistakes from arXiv:math/0502404. The main results of that paper are unchanged.

We retract the scalar curvature rigidity theorem as there is a mistake in the proof. We thank S. Montiel for pointing out the mistake.

For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and varied, but inevitably amongst the reasons given is that the systems designed often do not perform as expected by their designers. The reasons…

Improved mistake bounds for transductive online learning.

Simplifies online learning with consistent oracle to fewer mistakes.

The paper shows how sublinear biLipschitz equivalences affect Morse boundaries of metric spaces.

The paper studies how noisy labels impact decision-making in machine learning.

Self-directed learners can minimize mistakes in online classification.

Study the tradeoffs of bandit feedback in multiclass classification.

Study on tradeoffs between mistakes and ERM oracle calls in online and transductive learning.

SkewSize detects model biases by analyzing mistakes across subgroups.

Thompson Sampling is at most twice as bad as any other policy in Bayesian bandit models.

Corrected a mistake in a paper about minimal surfaces.

Using Jeff Holman's comments in Quantitative Finance to illustrate 4 critical errors students should learn to avoid: 1) Mistaking tails (4th moment) for volatility (2nd moment), 2) Missing Jensen's Inequality, 3) Analyzing the hedging wihout the underlying, 4) The necessity of a numeraire in finance.

Modified Perceptron handles strategic agents with limited position changes.

We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we characterize (up to constant factors) the optimal placement of queries so to minimize the mistakes made on the non-queried nodes. Our query se…

Sublinear LSVI via LSH reduces runtime to sublinear in actions.

Study apple tasting feedback in online binary classification, providing new insights into minimax expected mistakes.

The aim of this paper is to introduce the sublinear Higson corona and show that the sublinear Higson corona of Euclidean cone of P and X is decomposed into the product of P and that of X. Here P is a compact metric space and X is unbounded proper metric space. For example, the sublinear Higson corona of n-dimensional E…

LEAK learns from mistakes to improve point cloud segmentation.

Study on computable online learning with new conditions and complexities.

Study online learning of neural networks with margin condition.

Online learning makes sequence of decisions with partial data arrival where next movement of data is unknown. In this paper, we have presented a new technique as multiple times weight updating that update the weight iteratively forsame instance. The proposed technique analyzed with popular state-of-art algorithms from …

New model for Knightian uncertainty with jumps.

We give a proof of the sublinear tracking property for sample paths of random walks on various groups acting on spaces with hyperbolic-like properties. As an application, we prove sublinear tracking in Teichmueller distance for random walks on mapping class groups, and on Cayley graphs of a large class of finitely gene…

A technique to quickly fix mistakes in neural networks.

Study on learning to predict dynamical systems without assuming their structure.

This note corrects the mistakes in the splicing formulas of the paper "Floer homology and splicing knot complements". The mistakes are the result of the incorrect assumption that for a knot $K$ inside a homology sphere $Y$, the involution on the knot Floer homology of $K$ which corresponds to moving the basepoints by o…

John Morgan and G,Tian pointed out a mistake in the concluding argument for our paper entitled "$C_1$ in [2] is zero", which was recently published in arXiv:1512.02098. We hereby acknowledge this mistake and correct the computation, leading to the conclusion that $C_1$ is non-zero and that their reference [2] does inde…

We provide a general construction of time-consistent sublinear expectations on the space of continuous paths. It yields the existence of the conditional G-expectation of a Borel-measurable (rather than quasi-continuous) random variable, a generalization of the random G-expectation, and an optional sampling theorem that…

Traders underestimated risk-free rates, leading to poor investments.

This paper has been withdrawn by the authors, due a crucial mistake in Lemma 2

New algorithm tackles multiclass transductive online learning with unbounded labels.

We study the multiclass online learning problem where a forecaster makes a sequence of predictions using the advice of $n$ experts. Our main contribution is to analyze the regime where the best expert makes at most $b$ mistakes and to show that when $b = o(\log_4{n})$, the expected number of mistakes made by the optima…

We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of $K$ classes and lie in the $d$-dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linear…

Gaptron algorithm reduces mistakes in online multiclass classification.

This paper contains a correction of a mistake made in arXiv:1405.1324

This article has been withdrawn due to a mistake which is explained in version 2.

New sublinear sketches improve ANN and KDE for massive data streams.

Survey of algorithms to correct past mistakes in prediction.

Sublinear functionals of random variables are known as sublinear expectations; they are convex homogeneous functionals on infinite-dimensional linear spaces. We extend this concept for set-valued functionals defined on measurable set-valued functions (which form a nonlinear space), equivalently, on random closed sets. …

Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock markets and option markets with uncertain prior distribution are established by Peng's G-s…

The paper develops methods for time-varying constrained online convex optimization.