Transforms uniform learners to work under arbitrary distributions efficiently.
problem Learning under arbitrary distributions from uniform learners.
method Black-box transformation using decision tree decomposition.
result Efficient transformation with runtime scaling with distribution complexity.
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.
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.
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016). In extending this technique to real-valued hypotheses, we also obtain an efficient regression-to-bounded sample compression converter. To our knowledge, this is the first general compressed regre…
New approach for testable learning using moment matching and Rademacher complexity.
problem Replacing hard-to-verify distributional assumptions with testable ones.
method Moment matching and metric distances in probability.
result Improved sample complexity bounds for various concept classes and distributions.
Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based meta-learning or optimization-based meta-learning category, both of which have achieved successes in the simplified "k-shot N-way" image c…
A universal learner achieves best rates for all distributions.
problem Improving learning algorithm rates under various settings.
method Simple extension of Levin's universal search.
result Achieves best-possible rates for all distributions.
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weight…
New findings on PAC learning and marginal distribution estimation.
problem Understanding how PAC learning relates to marginal distribution estimation under distributional constraints.
method Revisited the connection between PAC learning, uniform convergence, and density estimation, considering a known family of marginal distributions.
result PAC learning is sandwiched between two refined models of density estimation, differing only in whether the learner knows the set of well-estimated events in H.
New insights into learning from only positive examples.
problem Characterizing proper learning from positive-only samples.
method Introducing a new combinatorial condition for proper positive-only learning.
result Proper positive-only learning is characterized by finite VC dimension and uniform exterior separability.
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.
Study exact community recovery in noisy SBM with limited queries.
problem Community recovery in noisy stochastic block models with limited queries.
method Balanced uniform querying, two-stage adaptive strategy, sublinear queries, subsampled graph.
result Adaptive querying can improve exact recovery limits in noisy SBM.
A bandit algorithm reduces regret in noisy, communication-constrained feedback.
problem Distributed stochastic multi-armed bandit with noisy, communication-constrained feedback.
method Proposes a multi-phase bandit algorithm, UE-UCB++, that matches an information-theoretic lower bound.
result Matches an information-theoretic lower bound of Ω(√(KT/σ²)) on the minimax regret.
Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampl…
New algorithm optimally identifies best arm in both stochastic and adversarial settings.
problem Best arm identification in stochastic and adversarial reward scenarios.
method Parameter-free algorithm designed to be robust to adversarial rewards and optimal in stochastic problems.
result Algorithm's error rate matches optimal bounds in stochastic problems and is robust to adversarial rewards.
New algorithm reduces clustering cost in bandit feedback.
problem Discovering hidden groupings in stochastic bandit feedback.
method ACB algorithm for active clustering with non-asymptotic lower bound.
result ACB algorithm's budget matches lower bound in most regimes.
New algorithms improve agnostic learning for triangles and polygons, reducing time complexity.
problem Efficient agnostic learning for geometric concept classes.
method Data structures and algorithms from computational geometry, probabilistic combinatorics.
result Optimal time complexity improvements for agnostic learning of triangles and polygons.
New framework improves learning across multiple distributions.
problem Modeling uncertainty in sensitive machine learning applications.
method Inspired by multi-armed bandits, provides distribution-dependent guarantees.
result Enhanced dependence on suboptimality gaps and sample size.
Active learning can't improve over passive in certain settings.
problem Active learning vs. passive learning in nonparametric settings.
method Analyzing margin conditions and their effects on active learning performance.
result Nuances in margin conditions determine whether active learning can outperform passive learning.
Ensemble learning is a statistical paradigm built on the premise that many weak learners can perform exceptionally well when deployed collectively. The BART method of Chipman et al. (2010) is a prominent example of Bayesian ensemble learning, where each learner is a tree. Due to its impressive performance, BART has rec…
New bounds for learning polynomial surrogates with L∞ guarantees.
problem Learning polynomial surrogates for bounded binary functions with L∞ error guarantees. method Characterized minimax sample complexity for two classes of polynomials under subgaussian noise.
result Sample complexity rates differ from noiseless case, scaling as nd+1 for degree d polynomials and ns2 for sparse polynomials. New learning rule for quantum measurement classes overcomes uniform convergence issues.
problem Characterizing learnability of POVM hypothesis classes in quantum settings.
method Introduced a new learning rule called denoised ERM to address uniform convergence issues.
result Characterized learnability conditions and sample complexity bounds for POVM classes.
No free lunch theorems suggest inductive biases are needed, but we show neural networks prefer low-complexity data.
problem The need for inductive biases in machine learning.
method Analysis of Kolmogorov complexity and neural network behavior on various datasets.
result Neural networks prefer low-complexity data, suggesting inductive biases are not always necessary.
Investigates principles of generalization in list learning, refutes sample compression conjecture.
problem Determining applicability of classical principles in list PAC learning.
method Examines uniform convergence and sample compression in list PAC learning.
result Sample compression fails in list PAC learning, refutes conjecture.
This work characterizes optimal multiclass learning with regularization.
problem The empirical risk minimization (ERM) algorithm fails in multiclass learning settings.
method Using one-inclusion graphs (OIGs), the work introduces optimal learning algorithms that relax structural risk minimization and incorporate unsupervised learning.
result An optimal learner is introduced that uses a local regularization function and an unsupervised learning stage to learn the regularizer.
The construction by Du et al. (2019) implies that even if a learner is given linear features in Rd that approximate the rewards in a bandit with a uniform error of ε, then searching for an action that is optimal up to O(ε) requires examining essentially all actions. We use the Kiefer-Wolfowitz theorem to…
Existing strategies for finite-armed stochastic bandits mostly depend on a parameter of scale that must be known in advance. Sometimes this is in the form of a bound on the payoffs, or the knowledge of a variance or subgaussian parameter. The notable exceptions are the analysis of Gaussian bandits with unknown mean and…
Optimal algorithm converts weak to strong learner with less data.
problem Constructing a strong learner from a weak learner with minimal data.
method New algorithm that uses less training data than AdaBoost.
result Optimal sample complexity for converting weak to strong learner.
Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner's goal is to match the teacher's demonstrated behavior. In this paper, we consider the setting where the learner has its own preferences th…
Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.
problem Causal inference with intermediate outcomes and treatment effect heterogeneity.
method Proposes a novel doubly cross-fit doubly robust machine learner to efficiently learn conditional principal causal effects under principal ignorability.
result Demonstrates informative patterns of treatment effect heterogeneity within the always-survivor subpopulation in an acute lung injury trial.
New PAC bound for meta-learning improves generalization guarantees.
problem Provide strong generalization guarantees in meta-learning.
method PAC-Bayes and uniform stability frameworks applied to gradient-based meta-learning.
result Derives a tighter PAC bound for gradient-based meta-learning.
Paper proves L2 regression can learn k-juntas without distributional assumptions.
problem Learning k-juntas using L2 regression without distributional restrictions.
method L2 polynomial regression and minimum mean square estimation (MMSE).
result Agnostic PAC learning of k-juntas using L2 polynomial regression.
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.
New meta-learners estimate time-varying treatment effects without model assumptions.
problem Estimating treatment effects over time in personalized medicine.
method Model-agnostic meta-learners for weighted pseudo-outcome regressions.
result Comprehensive theoretical analysis and practical insights for choosing meta-learners.
New survival learners estimate heterogeneous treatment effects from time-to-event data.
problem Estimating HTEs from time-to-event data with censoring outcomes.
method Orthogonal survival learners with theoretical guarantees and custom weighting functions.
result Orthogonal survival learners provide robust and model-agnostic HTE estimation.
This paper develops a new theory for ensemble learning beyond variance reduction.
problem Ensemble learning's effectiveness for stable estimators is not fully explained by variance reduction.
method Develops a general weighting theory for ensemble learning, formalizing ensembles as linear operators and introducing geometric and spectral constraints.
result Structured weights can outperform uniform averaging by reshaping approximation geometry and redistributing spectral complexity.
Proposes a method to integrate learner models robustly against misspecifications.
problem Misspecifications in learner models and parameter sharing patterns degrade prediction accuracy.
method Sequentially incorporates additional learners based on user-specified parameter sharing patterns.
result Data-adaptively selects the most suitable way of parameter sharing to enhance predictive performance.
VTIRT speeds up IRT inference for dynamic learner proficiency.
problem Expensive and slow inference algorithms for dynamic IRT models.
method Variational Temporal IRT (VTIRT) for fast, accurate inference.
result Orders of magnitude speedup in inference runtime with accurate results.
Method selects the best deep learner for time-series prediction using Bayesian networks.
problem Selecting the most effective deep learning model for time-series prediction.
method Bayesian network selects deep learners based on input variables and cluster training data.
result Threshold value determines which deep learners predict time-series data robustly.
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents un…
In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to …
pystacked combines machine learning models for improved predictions.
problem Improving machine learning model performance through stacking.
method Stacked generalization using Python's scikit-learn with various base learners.
result Enhanced predictive models through combining multiple machine learning algorithms.
MTL2L learns to adapt optimisation rules for unseen data.
problem Learners need to adapt to unseen data domains.
method Introduces MTL2L, a context-aware neural optimiser.
result MTL2L can adapt optimisation rules for unseen data.
Teaches sequential learners with changing inner states to improve future performance.
problem Teaching sequential learners with evolving inner states.
method Introduces an optimal control approach for multi-agent learning.
result Optimal control approach improves future performance of learners.
Theorem ensures superior learning outcomes for authorized learners with quantum label encoding.
problem Ensuring data security for authorized learners in machine learning.
method Quantum label encoding and PAC learning framework.
result Authorized learners achieve superior learning outcomes while eavesdroppers do not.
Study experiment planning with function approximation in contextual bandit problems.
problem Designing effective data collection strategies in settings with limited rewards.
method Proposes two experiment planning strategies compatible with function approximation.
result Eluder planning and sampling procedure achieves optimality guarantees.
Meta-learners improve causal effect estimation in small samples.
problem Estimating causal effects using machine learning methods.
method Sample-splitting and cross-fitting to reduce overfitting bias.
result Meta-learners' performance depends on sample size and estimation procedure.
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.