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Found 38 papers, 1 papers with code
Online Learning with Dependent Stochastic Feedback Graphs
A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner.
Fast and Effective GNN Training with Linearized Random Spanning Trees
We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory.
Data-Driven Online Model Selection With Regret Guarantees
We consider model selection for sequential decision making in stochastic environments with bandit feedback, where a meta-learner has at its disposal a pool of base learners, and decides on the fly which action to take based on the policies recommended by each base learner.
Faster Margin Maximization Rates for Generic and Adversarially Robust Optimization Methods
To address this limitation, we present a series of state-of-the-art implicit bias rates for mirror descent and steepest descent algorithms.
Adversarial Online Collaborative Filtering
We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once.
Leveraging User-Triggered Supervision in Contextual Bandits
We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context.
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object.
Best of Both Worlds Model Selection
We study the problem of model selection in bandit scenarios in the presence of nested policy classes, with the goal of obtaining simultaneous adversarial and stochastic ("best of both worlds") high-probability regret guarantees.
Fast Rates in Pool-Based Batch Active Learning
We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle.
Nonstochastic Bandits with Composite Anonymous Feedback
We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over the subsequent rounds in an adversarial way.
Online Active Learning with Surrogate Loss Functions
We derive a novel active learning algorithm in the streaming setting for binary classification tasks.
Batch Active Learning at Scale
The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources.
Adapting to Misspecification in Contextual Bandits
Given access to an online oracle for square loss regression, our algorithm attains optimal regret and -- in particular -- optimal dependence on the misspecification level, with no prior knowledge.
On Learning to Rank Long Sequences with Contextual Bandits
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses.
Neural Active Learning with Performance Guarantees
We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which we make no assumptions whatsoever.
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
Finally, unlike recent efforts in model selection for linear stochastic bandits, our approach is versatile enough to also cover cases where the context information is generated by an adversarial environment, rather than a stochastic one.
Online Model Selection: a Rested Bandit Formulation
Unlike known model selection efforts in the recent bandit literature, our algorithm exploits the specific structure of the problem to learn the unknown parameters of the expected loss function so as to identify the best arm as quickly as possible.
Adaptive Region-Based Active Learning
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels.
Flattening a Hierarchical Clustering through Active Learning
We investigate active learning by pairwise similarity over the leaves of trees originating from hierarchical clustering procedures.
Online Reciprocal Recommendation with Theoretical Performance Guarantees
A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e. g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists.
On Pairwise Clustering with Side Information
We measure performance not based on the recovery of the hidden similarity function, but instead on how well we classify each item.
Boltzmann Exploration Done Right
Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL).
Online Learning with Abstention
In the stochastic setting, we first point out a bias problem that limits the straightforward extension of algorithms such as UCB-N to time-varying feedback graphs, as needed in this context.
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information.
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i. e., the positive or negative nature of the social relationships).
Graph Clustering Bandits for Recommendation
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users.
Delay and Cooperation in Nonstochastic Bandits
We introduce \textsc{Exp3-Coop}, a cooperative version of the {\sc Exp3} algorithm and prove that with $K$ actions and $N$ agents the average per-agent regret after $T$ rounds is at most of order $\sqrt{\bigl(d+1 + \tfrac{K}{N}\alpha_{\le d}\bigr)(T\ln K)}$, where $\alpha_{\le d}$ is the independence number of the $d$-th power of the connected communication graph $G$.
Collaborative Filtering Bandits
Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users.
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback
This naturally models several situations where the losses of different actions are related, and knowing the loss of one action provides information on the loss of other actions.
Online Clustering of Bandits
We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies.
From Bandits to Experts: A Tale of Domination and Independence
We consider the partial observability model for multi-armed bandits, introduced by Mannor and Shamir.
A Gang of Bandits
Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems.
A Linear Time Active Learning Algorithm for Link Classification
We provide a theoretical analysis within this model, showing that we can achieve an optimal (to whithin a constant factor) number of mistakes on any graph $G = (V, E)$ such that $|E|$ is at least order of $|V|^{3/2}$ by querying at most order of $|V|^{3/2}$ edge labels.
On Multilabel Classification and Ranking with Partial Feedback
We present a novel multilabel/ranking algorithm working in partial information settings.
See the Tree Through the Lines: The Shazoo Algorithm
Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet.
Linear Classification and Selective Sampling Under Low Noise Conditions
Using the so-called Tsybakov low noise condition to parametrize the instance distribution, we show bounds on the convergence rate to the Bayes risk of both the fully supervised and the selective sampling versions of the basic algorithm.
On higher-order perceptron algorithms
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combines second-order statistics about the data with the logarithmic behavior" of multiplicative/dual-norm algorithms.
