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Found 17 papers, 3 papers with code
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
In particular, we prove near-optimal SQ lower bounds for NGCA under the moment-matching condition only.
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
The rewards in this game are chosen such that if the learner achieves large reward, then the learner's actions can be used to simulate solving a variant of 3-SAT, where (a) each variable shows up in a bounded number of clauses (b) if an instance has no solutions then it also has no solutions that satisfy more than (1-$\epsilon$)-fraction of clauses.
Coresets for Data Discretization and Sine Wave Fitting
The goal (e. g., for anomaly detection) is to approximate the $n$ points received so far in $P$ by a single frequency $\sin$, e. g. $\min_{c\in C}cost(P, c)+\lambda(c)$, where $cost(P, c)=\sum_{i=1}^n \sin^2(\frac{2\pi}{N} p_ic)$, $C\subseteq [N]$ is a feasible set of solutions, and $\lambda$ is a given regularization function.
Computational-Statistical Gaps in Reinforcement Learning
In this work, we make progress on this open problem by presenting the first computational lower bound for RL with linear function approximation: unless NP=RP, no randomized polynomial time algorithm exists for deterministic transition MDPs with a constant number of actions and linear optimal value functions.
Boosting in the Presence of Massart Noise
Our upper and lower bounds characterize the complexity of boosting in the distribution-independent PAC model with Massart noise.
Bounded Memory Active Learning through Enriched Queries
The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select informative examples in order to lower prohibitively expensive labeling costs.
Highway: Efficient Consensus with Flexible Finality
We propose Highway, a new consensus protocol that is safe and live in the classical partially synchronous BFT model, while at the same time offering practical improvements over existing solutions.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Robustly Learning any Clusterable Mixture of Gaussians
The key ingredients of this proof are a novel use of SoS-certifiable anti-concentration and a new characterization of pairs of Gaussians with small (dimension-independent) overlap in terms of their parameter distance.
Noise-tolerant, Reliable Active Classification with Comparison Queries
With the explosion of massive, widely available unlabeled data in the past years, finding label and time efficient, robust learning algorithms has become ever more important in theory and in practice.
Private Testing of Distributions via Sample Permutations
In this paper, we use the framework of property testing to design algorithms to test the properties of the distribution that the data is drawn from with respect to differential privacy.
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA.
vqSGD: Vector Quantized Stochastic Gradient Descent
In this work, we present a family of vector quantization schemes \emph{vqSGD} (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization.
The Optimal Approximation Factor in Density Estimation
We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e. g.\ a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal.
Robust polynomial regression up to the information theoretic limit
We consider the problem of robust polynomial regression, where one receives samples $(x_i, y_i)$ that are usually within $\sigma$ of a polynomial $y = p(x)$, but have a $\rho$ chance of being arbitrary adversarial outliers.
Testing Bayesian Networks
This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models.
Robust Learning of Fixed-Structure Bayesian Networks
We investigate the problem of learning Bayesian networks in a robust model where an $\epsilon$-fraction of the samples are adversarially corrupted.
Robust Estimators in High Dimensions without the Computational Intractability
We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the samples.
