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.

Improved mistake bounds for transductive online learning.

Study the tradeoffs of bandit feedback in multiclass classification.

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

Paper analyzes mistake and generalization of MNIC classifiers.

Study online learning of neural networks with margin condition.

Study on computable online learning with new conditions and complexities.

Modified Perceptron handles strategic agents with limited position changes.

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

Self-directed learners can minimize mistakes in online classification.

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

Gaptron algorithm reduces mistakes in online multiclass classification.

New algorithm tackles multiclass transductive online learning with unbounded labels.

Paper proposes online algorithms for multiclass classification with partial 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 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…

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…

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 …

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…

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…

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

Study robust online learning with adversarial perturbations.

We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We present an algorithm based on a specialist approach; we develop the machinery of cluster specialists which probabilistically exploits the cluster structure in the graph. Our algorithm has two va…

Paper tackles noisy bandit feedback for multiclass classification.

The seminal paper of Caponnetto and de Vito (2007) provides minimax-optimal rates for kernel ridge regression in a very general setting. Its proof, however, contains an error in its bound on the effective dimensionality. In this note, we explain the mistake, provide a correct bound, and show that the main theorem remai…

Improved bounds for continuous functions in online learning.

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

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

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

We use surrogate losses to obtain several new regret bounds and new algorithms for contextual bandit learning. Using the ramp loss, we derive new margin-based regret bounds in terms of standard sequential complexity measures of a benchmark class of real-valued regression functions. Using the hinge loss, we derive an ef…

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…

New bounds show simple predictors can learn complex concepts online.

Simplifies online learning with consistent oracle to fewer mistakes.

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

For a conformally compact manifold that is hyperbolic near infinity and of dimension $n+1$, we complete the proof of the optimal $O(r^{n+1})$ upper bound on the resonance counting function, correcting a mistake in the existing literature. In the case of a compactly supported perturbation of a hyperbolic manifold, we es…

Paper defines a new dimension to measure self-directed learning complexity.

We investigate the problem of sequentially predicting the binary labels on the nodes of an arbitrary weighted graph. We show that, under a suitable parametrization of the problem, the optimal number of prediction mistakes can be characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the g…

We give an online algorithm and prove novel mistake and regret bounds for online binary matrix completion with side information. The mistake bounds we prove are of the form $\tilde{O}(D/γ^2)$. The term $1/γ^2$ is analogous to the usual margin term in SVM (perceptron) bounds. More specifically, if we assume that there i…

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.

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

LEAK learns from mistakes to improve point cloud segmentation.

Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…