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Found 20 papers, 3 papers with code
From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces
We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the space of actions.
Online Learning and Solving Infinite Games with an ERM Oracle
In this setting, we provide learning algorithms that only rely on best response oracles and converge to approximate-minimax equilibria in two-player zero-sum games and approximate coarse correlated equilibria in multi-player general-sum games, as long as the game has a bounded fat-threshold dimension.
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples.
Learning and Testing Latent-Tree Ising Models Efficiently
We provide time- and sample-efficient algorithms for learning and testing latent-tree Ising models, i. e. Ising models that may only be observed at their leaf nodes.
EM's Convergence in Gaussian Latent Tree Models
Our results for the landscape of the log-likelihood function in general latent tree models provide support for the extensive practical use of maximum likelihood based-methods in this setting.
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems
In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model.
Smoothed Online Learning is as Easy as Statistical Learning
We then prove a lower bound on the oracle complexity of any proper learning algorithm, which matches the oracle-efficient upper bounds up to a polynomial factor, thus demonstrating the existence of a statistical-computational gap in smooth online learning.
Statistical Estimation from Dependent Data
We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioned on their feature vectors, but dependent, capturing settings where e. g. these observations are collected on a spatial domain, a temporal domain, or a social network, which induce dependencies.
Majorizing Measures, Sequential Complexities, and Online Learning
We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity.
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample.
A bounded-noise mechanism for differential privacy
We present an asymptotically optimal $(\epsilon,\delta)$ differentially private mechanism for answering multiple, adaptively asked, $\Delta$-sensitive queries, settling the conjecture of Steinke and Ullman [2020].
Learning Ising models from one or multiple samples
As corollaries of our main theorem, we derive bounds when the model's interaction matrix is a (sparse) linear combination of known matrices, or it belongs to a finite set, or to a high-dimensional manifold.
PAC learning with stable and private predictions
For $\epsilon$-differentially private prediction we give two new algorithms: one using $\tilde O(d/(\alpha^2\epsilon))$ samples and another one using $\tilde O(d^2/(\alpha\epsilon) + d/\alpha^2)$ samples.
Interaction is necessary for distributed learning with privacy or communication constraints
Our main result is an exponential lower bound on the number of samples necessary to solve the standard task of learning a large-margin linear separator in the non-interactive LDP model.
Learning from weakly dependent data under Dobrushin's condition
Indeed, we show that the standard complexity measures of Gaussian and Rademacher complexities and VC dimension are sufficient measures of complexity for the purposes of bounding the generalization error and learning rates of hypothesis classes in our setting.
Optimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regression
In this paper, we study two problems: (1) estimation of a $d$-dimensional log-concave distribution and (2) bounded multivariate convex regression with random design with an underlying log-concave density or a compactly supported distribution with a continuous density.
Space lower bounds for linear prediction in the streaming model
We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting.
A Better Resource Allocation Algorithm with Semi-Bandit Feedback
We study a sequential resource allocation problem between a fixed number of arms.
Detecting Correlations with Little Memory and Communication
We study the problem of identifying correlations in multivariate data, under information constraints: Either on the amount of memory that can be used by the algorithm, or the amount of communication when the data is distributed across several machines.
Twenty (simple) questions
An optimal strategy for the "20 questions" game is given by a Huffman code for $\pi$: Bob's questions reveal the codeword for $x$ bit by bit.
