Search Results for author:
Found 16 papers, 6 papers with code
Learning with Plasticity Rules: Generalization and Robustness
Here we hypothesize that (a) Brains employ synaptic plasticity rules that serve as proxies for GD; (b) These rules themselves can be learned by GD on the rule parameters; and (c) This process may be a missing ingredient for the development of ANNs that generalize well and are robust to adversarial perturbations.
Socially Fair k-Means Clustering
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e. g., demographic groups).
Robustly Clustering a Mixture of Gaussians
We give an efficient algorithm for robustly clustering of a mixture of two arbitrary Gaussians, a central open problem in the theory of computationally efficient robust estimation, assuming only that the the means of the component Gaussians are well-separated or their covariances are well-separated.
Biologically Plausible Neural Networks via Evolutionary Dynamics and Dopaminergic Plasticity
Artificial neural networks (ANNs) lack in biological plausibility, chiefly because backpropagation requires a variant of plasticity (precise changes of the synaptic weights informed by neural events that occur downstream in the neural circuit) that is profoundly incompatible with the current understanding of the animal brain.
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Our main result is an exact polynomial-time algorithm for the two-criterion dimensionality reduction problem when the two criteria are increasing concave functions.
The Price of Fair PCA: One Extra Dimension
This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B.
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
We give an application to recovering assemblies of neurons.
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds
We give an agnostic learning guarantee for GD: starting from a randomly initialized network, it converges in mean squared loss to the minimum error (in $2$-norm) of the best approximation of the target function using a polynomial of degree at most $k$.
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.
On the Complexity of Learning Neural Networks
Moreover, this hard family of functions is realizable with a small (sublinear in dimension) number of activation units in the single hidden layer.
Chi-squared Amplification: Identifying Hidden Hubs
We give a polynomial-time algorithm to identify all the hidden hubs with high probability for $k \ge n^{0. 5-\delta}$ for some $\delta >0$, when $\sigma_1^2>2\sigma_0^2$.
Agnostic Estimation of Mean and Covariance
We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type.
Cortical Computation via Iterative Constructions
We study the rate of convergence, finding that while linear convergence to the correct function can be achieved for any threshold using a fixed set of primitives, for quadratic convergence, the size of the primitives must grow as the threshold approaches 0 or 1.
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization
Stochastic convex optimization, where the objective is the expectation of a random convex function, is an important and widely used method with numerous applications in machine learning, statistics, operations research and other areas.
Efficient Representations for Life-Long Learning and Autoencoding
Specifically, we consider the problem of learning many different target functions over time, that share certain commonalities that are initially unknown to the learning algorithm.
Fourier PCA and Robust Tensor Decomposition
Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution. We make this method algorithmic by developing a tensor decomposition method for a pair of tensors sharing the same vectors in rank-$1$ decompositions.
