Online boosting method improves weak to strong learner.
problem Online learning of weak to strong learner.
method Extends batch GentleAdaBoost to online approach with line search.
result Online boosting performs better than other methods.
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48 results for “online learners”
Boosts weak online learners to strong ones with sublinear regret.
problem Online learning agnostic setting without strong guarantees.
method Reduction to online convex optimization, boosting via marginally-better-than-trivial regret guarantees.
result First agnostic online boosting algorithm with sublinear regret.
We study the task of online boosting--combining online weak learners into an online strong learner. While batch boosting has a sound theoretical foundation, online boosting deserves more study from the theoretical perspective. In this paper, we carefully compare the differences between online and batch boosting, and pr…
Improved online classification with accurate predictions.
problem Online classification challenges with limited data.
method Designing an online learner that uses predictions to reduce regret.
result Expected regret is better than worst-case analysis, especially with accurate predictions.
Study online learner attacks by manipulating labels, revealing critical thresholds.
problem Adversarial attacks on online learners, especially label manipulation.
method Control-theoretical perspective, theoretical analysis, empirical study.
result Discontinuous transition in learner's accuracy occurs at a critical threshold.
In this paper we propose a novel framework for decentralized, online learning by many learners. At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, the learner can select one of its own actions (which gives a reward and provides information) or reques…
New findings show limitations in converting private learning to online learning efficiently.
problem Limitations in converting private learning to online learning efficiently.
method Assuming one-way functions, we show an efficient conversion from pure-private learners to online learners is impossible.
result Efficient conversion from pure-private learners to online learners is impossible under certain assumptions.
We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}…
Online learners track optimal solutions with constant step-size.
problem Tracking optimal solutions in online learning settings.
method Established a link between steady-state performance and tracking performance using analogies with adaptive filters.
result Inferred tracking performance from steady-state expressions directly.
Study on tradeoffs between mistakes and ERM oracle calls in online and transductive learning.
problem Analyzing online and transductive learning with limited ERM and weak consistency oracle access.
method Proves lower bounds and upper bounds on mistakes and oracle calls, considering realizable and agnostic cases.
result Achieves optimal mistake bounds with weak consistency queries for certain concept classes.
Strategic feature manipulation helps learners identify meaningful variables in online regression settings.
problem Strategic feature manipulation poses a challenge for learners in online regression settings.
method Investigates how strategic feature manipulation by individuals can help learners recover meaningful features.
result Simple learner behavior can help accurately recover meaningful features and incentivize feature improvement.
New algorithms reduce dynamic regret in online MDPs with changing losses.
problem Online MDPs with adversarial loss changes and known transitions.
method Dynamic regret measure, novel ensemble algorithms for three models.
result Provably optimal dynamic regret bounds for episodic SSP, improved bounds for predictable environments.
Extends boosting to multiclass online agnostic classification.
problem Online multiclass classification with weak learners.
method Reduces multiclass online agnostic boosting to online convex optimization.
result First boosting algorithm for online agnostic multiclass classification.
Algorithm improves online learning in adversarial bandits.
problem Online learning in adversarial multi-armed bandits with non-uniform best arm distribution.
method Online-within-online setup, inner and outer learners, leveraging non-uniform empirical distribution of best arms.
result Improves regret bounds for non-uniform best arm distributions.
IE-GP framework learns sequentially arriving data with adaptive kernels.
problem Sequential data adaptation in Gaussian process models.
method Incremental ensemble of Gaussian process learners with adaptive weights.
result IE-GP framework effectively learns and adapts to sequential data.
Self-directed learners can minimize mistakes in online classification.
problem Minimizing mistakes in online classification with adaptive prediction order.
method Designing efficient self-directed learners for linear classification.
result Strong separation between worst-order and random-order learning for linear classification.
We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterpa…
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.
sGBM speeds up gradient boosting by parallelizing and adapting base learners.
problem Infeasibility of parallelizing GBM training and sub-optimal performance in online settings.
method Integrates multiple differentiable base learners, jointly optimizing them with linear speed-up.
result sGBM achieves higher time efficiency and better accuracy than traditional GBM.
Novel algorithms for online learning with uncertain feedback graphs reduce regret.
problem Uncertainty in feedback graphs hinders traditional online learning approaches.
method Developed novel online learning algorithms to handle uncertain feedback graphs.
result Proved sublinear regret under mild conditions for the proposed algorithms.
Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and…
We study online optimization in a setting where an online learner seeks to optimize a per-round hitting cost, which may be non-convex, while incurring a movement cost when changing actions between rounds. We ask: \textit{under what general conditions is it possible for an online learner to leverage predictions of futur…
HATT improves online decision tree ensembles by using a more eager splitting strategy.
problem Improving the efficiency of online decision tree ensembles.
method Replacing Hoeffding Tree's split strategy with HATT, which uses the Hoeffding Test for candidate splits.
result HATT outperforms Hoeffding Tree in online bagging and boosting ensembles, as shown by significant performance improvements in various testbenches.
Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.
problem Online learning with set-valued feedback, where labels are sets rather than single labels.
method Introduced new combinatorial dimensions (Set Littlestone and Measure Shattering) to characterize learnability.
result Characterized deterministic and randomized online learnability, and established bounds for various learning settings.
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…
Characterizes statistical complexity of realizable regression in PAC and online learning.
problem Understanding the statistical complexity of realizable regression in both PAC and online learning settings.
method Introduces minimax instance optimal learners, novel and combinatorial dimensions to characterize learnability.
result Characterizes which classes of real-valued predictors are learnable and provides necessary conditions for learnability.
New method for online meta-learning reduces dynamic regret in changing environments.
problem Learning new tasks quickly from limited data in dynamic settings.
method Established dynamic regret analysis using generalized adaptive gradient methods.
result Logarithmic local dynamic regret with dependence on total iterations and learner parameters.
New framework captures long-term decision dependence in online learning.
problem Long-term dependence on past decisions in online learning.
method Introduces Online Convex Optimization with Unbounded Memory (OCO-UMB) and -effective memory capacity.
result Proves upper bound on policy regret and matching lower bound.
We study the problem of online learning with non-convex losses, where the learner has access to an offline optimization oracle. We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of in this setting. This improves upon the previous best-known regret rate of…
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propose an unbiased estimate of the loss using a randomized prediction, allowing the model to update its weak learners with limited information. …
Slow Learners are Fastmath.OC
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequential in their design which prevents them from taking advantage of modern multi-core architectures. In this paper we prove that online learning …
We study the problem of online learning in a class of Markov decision processes known as linearly solvable MDPs. In the stationary version of this problem, a learner interacts with its environment by directly controlling the state transitions, attempting to balance a fixed state-dependent cost and a certain smooth cost…
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
Study on computational aspects of replicable learning, bridging statistical and algorithmic perspectives.
problem Understanding the computational connections between replicability and various learning paradigms.
method Design of replicable learners, lifting framework, and transformation techniques.
result Efficient replicable learners for specific learning problems under various distributions.
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Gamescs.LG
Algorithm improves RL model selection for repeated games with utility maximization.
problem Optimal policy learning in repeated games with unknown opponent strategy.
method Proposes MRBEAR for average reward RL, applying to utility maximization in repeated games.
result Regret bound shows linear dependence on number of model classes in average reward RL.
Meta-learning improves performance across similar tasks in adversarial bandit settings.
problem Improving performance across multiple similar tasks in adversarial bandit scenarios.
method Designing meta-algorithms that combine outer learners to tune hyperparameters of inner learners for MAB and BLO.
result Meta-algorithms improve task-averaged regret for MAB and BLO, showing direct relationship with action space-dependent measures.
Study online learning with off-policy feedback in adversarial bandit problems.
problem Learning with limited direct feedback in sequential decision making.
method Proposed algorithms that adapt pessimistic reward estimators to handle unknown behavior policy.
result Guaranteed regret bounds scaling with policy mismatch, improving performance against well-covered comparators.
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algori…
Efficient boosting method for regression with limited feedback.
problem Online boosting for regression tasks with noisy multi-point bandit feedback.
method Efficient regret minimization method with online boosting algorithm and projection-free online convex optimization.
result Improved state-of-the-art guarantees in efficiency.
Continuous-time algorithms improve online learning performance.
problem Online learning with sequential data and minimizing overall regret.
method Extending discrete-time algorithms to continuous-time models for online linear optimization, adversarial bandit, and adversarial linear bandit.
result Optimal regret bounds are proven for continuous-time settings.
Bayesian algorithms improve online learning with adversaries over infinite action spaces.
problem Online learning with adversaries over infinite action spaces.
method Developed a Thompson sampling algorithm for online learning with an adversary's prior over the space of actions.
result Thompson sampling over a Gaussian process prior achieves a rate of against a -bounded -Lipschitz adversary.
Study the tradeoffs of bandit feedback in multiclass classification.
problem The price of using bandit feedback in multiclass classification.
method Mistake bound model, analysis of variants, and comparison of learners and adversaries.
result The optimal mistake bound under bandit feedback is at most times higher than in full information, with a tight bound of .
New algorithms for efficient learning with partial information, reducing regret.
problem Online learning with partial observability and semi-bandit feedback.
method Implicit exploration strategy for near-optimal regret guarantees.
result First algorithms with near-optimal regret guarantees without knowing the observation system.
Paper proves tight lower bounds for online multicalibration, separating it from marginal calibration.
problem Proving lower bounds for online multicalibration in relation to marginal calibration.
method Information-theoretic approach, constructing group families from orthonormal bases.
result Establishes tight lower bounds for online multicalibration, matching upper bounds up to logarithmic factors.
Capacity-Constrained Online Convex Optimization with Delayed Feedback
problem Online learning with delayed feedback under a hard capacity constraint
method Reduction to a delayed and weighted OCO problem using a scheduler
result First regret guarantees for capacity-constrained OCO under convex and strongly convex losses
Algorithm for online decision making with unknown dynamics and aggregate feedback.
problem Online decision making with unknown dynamics and aggregate bandit feedback.
method Developed an algorithm based on online mirror descent with a self-concordant barrier regularization and an increasing learning rate schedule.
result Achieved regret for the online Markov Decision Process with episodes.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
The paper improves competitive and dynamic regret bounds for smoothed online learning.
problem Smoothed online learning with hitting and switching costs.
method Optimization problems to minimize hitting cost, dynamic regret modification of existing algorithms.
result Improved competitive and dynamic regret bounds for various function classes.