arXiv research

Improved mistake bounds for transductive online learning.

Simplifies online learning with consistent oracle to fewer mistakes.

Ahpatron improves online kernel learning with tighter mistake bounds.

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

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

LEAK learns from mistakes to improve point cloud segmentation.

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.

Self-directed learners can minimize mistakes in online classification.

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…

Study online learning of neural networks with margin condition.

Open problem seeks an online learning algorithm for binary classification.

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.

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 on learning to predict dynamical systems without assuming their structure.

Study on computable online learning with new conditions and complexities.

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.

A technique to quickly fix mistakes in neural networks.

Modified Perceptron handles strategic agents with limited position changes.

Study the tradeoffs of bandit feedback in multiclass classification.

SkewSize detects model biases by analyzing mistakes across subgroups.

New algorithm tackles multiclass transductive online learning with unbounded labels.

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 selective classification with limited feedback in online learning.

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…

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

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

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…

Survey of algorithms to correct past mistakes in prediction.

LSVM with EBT reduces quasar detection errors by 10x.

Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake. It always involves a defending side, usually a classifier, and an atta…

Paper analyzes mistake and generalization of MNIC classifiers.

Study online learning of quantum processes, showing feasibility for certain types.

Online learning is the process of answering a sequence of questions based on the correct answers to the previous questions. It is studied in many research areas such as game theory, information theory and machine learning. There are two main components of online learning framework. First, the learning algorithm also kn…

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 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 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…

Corrected a mistake in a paper about minimal surfaces.

New framework models echo chamber learning, proving tight bounds on algorithm performance.

I have submitted a new version to arXiv:1910.13806. I forget to choose to replace the old version, but submitted a new one. It's my mistake.

Efficient algorithm for online learning with Massart noise achieves near-optimal mistake bound.

Study robust online learning with adversarial perturbations.

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…

New robust algorithms improve learning with feature feedback.

New bounds show simple predictors can learn complex concepts online.

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…