arXiv research

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 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.

Open problem seeks an online learning algorithm for binary classification.

Modified Perceptron handles strategic agents with limited position changes.

Paper analyzes mistake and generalization of MNIC classifiers.

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…

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

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 …

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…

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.

Survey of algorithms to correct past mistakes in prediction.

This paper has been withdrawn by the authors, due to a mistake pointed out by Lenny Ng and Josh Sabloff.

We consider the online multiclass linear classification under the bandit feedback setting. Beygelzimer, Pál, Szörényi, Thiruvenkatachari, Wei, and Zhang [ICML'19] considered two notions of linear separability, weak and strong linear separability. When examples are strongly linearly separable with margin $γ$, they prese…

This paper has been withdrawn by the author due to a mistake in the section 4.

This paper has been withdrawn since it is identical to the paper math.QA/0601267. It was posted by mistake.

This paper has some inconsistent results, i.e., we made some failed claims because we did some mistakes for using the test criterion for a series. Precisely, our claims on the convergence rate of $\mathcal{O}(1/t)$ of SGD presented in Theorem 1, Corollary 1, Theorem 2 and Corollary 2 are wrongly derived because they ar…

The purpose of this erratum is to correct a mistake in the proof of Theorem 4.1 of our paper \cite{CF}.

We point out a mistake in the main statement of \cite{liu} and suggest and proof a correct statement.

We correct a mistake on the citation of JSJ theory in \cite{Ni}. Some arguments in \cite{Ni} are also slightly modified accordingly.

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recen…

Improved Markov models learn from their mistakes and adapt to problem complexity.

We propose a voted dual averaging method for online classification problems with explicit regularization. This method employs the update rule of the regularized dual averaging (RDA) method, but only on the subsequence of training examples where a classification error is made. We derive a bound on the number of mistakes…