arXiv research

Ahpatron improves online kernel learning with tighter mistake bounds.

Simplifies online learning with consistent oracle to fewer mistakes.

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…

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

SkewSize detects model biases by analyzing mistakes across subgroups.

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

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

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

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.

Study the tradeoffs of bandit feedback in multiclass classification.

A technique to quickly fix mistakes in neural networks.

Modified Perceptron handles strategic agents with limited position changes.

Improved mistake bounds for transductive online learning.

LEAK learns from mistakes to improve point cloud segmentation.

Paper analyzes mistake and generalization of MNIC classifiers.

Study online learning of neural networks with margin condition.

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

Self-directed learners can minimize mistakes in online classification.

Improved mistake bound for group linear separable cases in online multiclass linear classification.

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…

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.

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…

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

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

Study on computable online learning with new conditions and complexities.

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 …

Study selective classification with limited feedback in online learning.

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…

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…

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…

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 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…

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…

Gaptron algorithm reduces mistakes in online multiclass classification.

Corrects a mistake in $hv$-block cross-validation consistency claim.

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.

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

Paper proposes online algorithms for multiclass classification with partial labels.

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…

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