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List Sample Compression and Uniform Convergence
In classical PAC learning, both uniform convergence and sample compression satisfy a form of `completeness': whenever a class is learnable, it can also be learned by a learning rule that adheres to these principles.
The Dimension of Self-Directed Learning
Understanding the self-directed learning complexity has been an important problem that has captured the attention of the online learning theory community since the early 1990s.
Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
We demonstrate that the optimal mistake bound under bandit feedback is at most $O(k)$ times higher than the optimal mistake bound in the full information case, where $k$ represents the number of labels.
A Trichotomy for Transductive Online Learning
We present new upper and lower bounds on the number of learner mistakes in the `transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997).
Efficient Agnostic Learning with Average Smoothness
While the recent work of Hanneke et al. (2023) established tight uniform convergence bounds for average-smooth functions in the realizable case and provided a computationally efficient realizable learning algorithm, both of these results currently lack analogs in the general agnostic (i. e. noisy) case.
Universal Rates for Multiclass Learning
Pseudo-cubes are a structure, rooted in the work of Daniely and Shalev-Shwartz (2014), and recently shown by Brukhim, Carmon, Dinur, Moran, and Yehudayoff (2022) to characterize PAC learnability (i. e., uniform rates) for multiclass classification.
Adversarial Resilience in Sequential Prediction via Abstention
We study the problem of sequential prediction in the stochastic setting with an adversary that is allowed to inject clean-label adversarial (or out-of-distribution) examples.
Limits of Model Selection under Transfer Learning
Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task.
Multiclass Online Learning and Uniform Convergence
We argue that the best expert has regret at most Littlestone dimension relative to the best concept in the class.
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomized Littlestone dimension, which is the largest $d$ for which there exists a tree shattered by $\mathcal{H}$ whose average depth is $2d$.
Adversarial Rewards in Universal Learning for Contextual Bandits
We show that optimistic universal learning for contextual bandits with adversarial rewards is impossible in general, contrary to all previously studied settings in online learning -- including standard supervised learning.
Contextual Bandits and Optimistically Universal Learning
Lastly, we consider the case of added continuity assumptions on rewards and show that these lead to universal consistency for significantly larger classes of data-generating processes.
On Optimal Learning Under Targeted Data Poisoning
In this work we aim to characterize the smallest achievable error $\epsilon=\epsilon(\eta)$ by the learner in the presence of such an adversary in both realizable and agnostic settings.
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
We present a minimax optimal learner for the problem of learning predictors robust to adversarial examples at test-time.
Fine-Grained Distribution-Dependent Learning Curves
We solve this problem in a principled manner, by introducing a combinatorial dimension called VCL that characterizes the best $d'$ for which $d'/n$ is a strong minimax lower bound.
Adversarially Robust PAC Learnability of Real-Valued Functions
We study robustness to test-time adversarial attacks in the regression setting with $\ell_p$ losses and arbitrary perturbation sets.
Universally Consistent Online Learning with Arbitrarily Dependent Responses
This work provides an online learning rule that is universally consistent under processes on (X, Y) pairs, under conditions only on the X process.
Robustly-reliable learners under poisoning attacks
Remarkably, we provide a complete characterization of learnability in this setting, in particular, nearly-tight matching upper and lower bounds on the region that can be certified, as well as efficient algorithms for computing this region given an ERM oracle.
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
This shows that there is a significant benefit in semi-supervised robust learning even in the worst-case distribution-free model, and establishes a gap between the supervised and semi-supervised label complexities which is known not to hold in standard non-robust PAC learning.
Universal Online Learning with Unbounded Losses: Memory Is All You Need
We resolve an open problem of Hanneke on the subject of universally consistent online learning with non-i. i. d.
Transductive Robust Learning Guarantees
We study the problem of adversarially robust learning in the transductive setting.
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
This open problem asks whether there exists an online learning algorithm for binary classification that guarantees, for all target concepts, to make a sublinear number of mistakes, under only the assumption that the (possibly random) sequence of points X allows that such a learning algorithm can exist for that sequence.
A Theory of PAC Learnability of Partial Concept Classes
In fact we exhibit easy-to-learn partial concept classes which provably cannot be captured by the traditional PAC theory.
Robust learning under clean-label attack
We study the problem of robust learning under clean-label data-poisoning attacks, where the attacker injects (an arbitrary set of) correctly-labeled examples to the training set to fool the algorithm into making mistakes on specific test instances at test time.
Adversarially Robust Learning with Unknown Perturbation Sets
We study the problem of learning predictors that are robust to adversarial examples with respect to an unknown perturbation set, relying instead on interaction with an adversarial attacker or access to attack oracles, examining different models for such interactions.
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games
More precisely, given any concept class C and any hypothesis class H, we provide nearly tight bounds (up to a log factor) on the optimal mistake bounds for online learning C using predictors from H. Our bound yields an exponential improvement over the previously best known bound by Chase and Freitag (2020).
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not alter the resulting classifier.
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
We study the problem of reducing adversarially robust learning to standard PAC learning, i. e. the complexity of learning adversarially robust predictors using access to only a black-box non-robust learner.
A No-Free-Lunch Theorem for MultiTask Learning
A perplexing fact remains in the evolving theory on the subject: while we would hope for performance bounds that account for the contribution from multiple tasks, the vast majority of analyses result in bounds that improve at best in the number $n$ of samples per task, but most often do not improve in $N$.
Proper Learning, Helly Number, and an Optimal SVM Bound
It has been recently shown by Hanneke (2016) that the optimal sample complexity of PAC learning for any VC class C is achieved by a particular improper learning algorithm, which outputs a specific majority-vote of hypotheses in C. This leaves the question of when this bound can be achieved by proper learning algorithms, which are restricted to always output a hypothesis from C. In this paper we aim to characterize the classes for which the optimal sample complexity can be achieved by a proper learning algorithm.
On the Value of Target Data in Transfer Learning
We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy.
Universal Bayes consistency in metric spaces
This is the first learning algorithm known to enjoy this property; by comparison, the $k$-NN classifier and its variants are not generally universally Bayes-consistent, except under additional structural assumptions, such as an inner product, a norm, finite dimension, or a Besicovitch-type property.
VC Classes are Adversarially Robustly Learnable, but Only Improperly
We study the question of learning an adversarially robust predictor.
Agnostic Sample Compression Schemes for Regression
For the $\ell_2$ loss, does every function class admit an approximate compression scheme of polynomial size in the fat-shattering dimension?
Sample Compression for Real-Valued Learners
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016).
A New Lower Bound for Agnostic Learning with Sample Compression Schemes
We establish a tight characterization of the worst-case rates for the excess risk of agnostic learning with sample compression schemes and for uniform convergence for agnostic sample compression schemes.
Actively Avoiding Nonsense in Generative Models
A generative model may generate utter nonsense when it is fit to maximize the likelihood of observed data.
Testing Piecewise Functions
We also identify the optimal dependence on the number of pieces in the query complexity of passive testing in the special case of piecewise constant functions.
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
We are then interested in the question of whether there exist learning rules guaranteed to be universally consistent given only the assumption that universally consistent learning is possible for the given data process.
Learning with Changing Features
In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with respect to an output variable in an agnostic (supervised) learning setting.
Statistical Learning under Nonstationary Mixing Processes
Under these conditions, we propose a learning method, and establish that for bounded VC subgraph classes, the cumulative excess risk grows sublinearly in the number of predictions, at a quantified rate.
Refined Error Bounds for Several Learning Algorithms
This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors.
The Optimal Sample Complexity of PAC Learning
This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case.
Learning with a Drifting Target Concept
Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks
We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior.
Minimax Analysis of Active Learning
This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models.
A Compression Technique for Analyzing Disagreement-Based Active Learning
We introduce a new and improved characterization of the label complexity of disagreement-based active learning, in which the leading quantity is the version space compression set size.
Surrogate Losses in Passive and Active Learning
Specifically, it presents an active learning algorithm based on an arbitrary classification-calibrated surrogate loss function, along with an analysis of the number of label requests sufficient for the classifier returned by the algorithm to achieve a given risk under the 0-1 loss.
