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no code implementations • ICML 2020 • Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

Theoretically, we show that the small networks pruned using our method achieve provably lower loss than small networks trained from scratch with the same size.

no code implementations • NeurIPS 2021 • Sitan Chen, Adam Klivans, Raghu Meka

While the problem of PAC learning neural networks from samples has received considerable attention in recent years, in certain settings like model extraction attacks, it is reasonable to imagine having more than just the ability to observe random labeled examples.

no code implementations • 27 Nov 2020 • Surbhi Goel, Adam Klivans, Pasin Manurangsi, Daniel Reichman

We are also able to obtain lower bounds on the running time in terms of the desired additive error $\epsilon$.

no code implementations • 22 Oct 2020 • Aravind Gollakota, Sushrut Karmalkar, Adam Klivans

We consider the problem of distribution-free learning for Boolean function classes in the PAC and agnostic models.

no code implementations • NeurIPS 2020 • Surbhi Goel, Adam Klivans, Frederic Koehler

Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult.

no code implementations • NeurIPS 2020 • Surbhi Goel, Aravind Gollakota, Adam Klivans

We give the first statistical-query lower bounds for agnostically learning any non-polynomial activation with respect to Gaussian marginals (e. g., ReLU, sigmoid, sign).

no code implementations • ICML 2020 • Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans

Our lower bounds hold for broad classes of activations including ReLU and sigmoid.

1 code implementation • 3 Mar 2020 • Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

This differs from the existing methods based on backward elimination, which remove redundant neurons from the large network.

no code implementations • NeurIPS 2019 • Surbhi Goel, Sushrut Karmalkar, Adam Klivans

Let $\mathsf{opt} < 1$ be the population loss of the best-fitting ReLU.

no code implementations • 8 Mar 2018 • Adam Klivans, Pravesh K. Kothari, Raghu Meka

We give the first polynomial-time algorithm for performing linear or polynomial regression resilient to adversarial corruptions in both examples and labels.

no code implementations • ICML 2018 • Surbhi Goel, Adam Klivans, Raghu Meka

We give the first provably efficient algorithm for learning a one hidden layer convolutional network with respect to a general class of (potentially overlapping) patches.

no code implementations • 18 Sep 2017 • Surbhi Goel, Adam Klivans

We give a polynomial-time algorithm for learning neural networks with one layer of sigmoids feeding into any Lipschitz, monotone activation function (e. g., sigmoid or ReLU).

no code implementations • NeurIPS 2017 • Surbhi Goel, Adam Klivans

In this work we show that a natural distributional assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields polynomial-time algorithms in the non-realizable setting for expressive classes of networks (e. g. feed-forward networks of ReLUs).

no code implementations • 20 Jun 2017 • Adam Klivans, Raghu Meka

Our main application is an algorithm for learning the structure of t-wise MRFs with nearly-optimal sample complexity (up to polynomial losses in necessary terms that depend on the weights) and running time that is $n^{O(t)}$.

1 code implementation • ICLR 2018 • Elad Hazan, Adam Klivans, Yang Yuan

In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.

no code implementations • ICML 2017 • Erik M. Lindgren, Alexandros G. Dimakis, Adam Klivans

We require that the number of fractional vertices in the LP relaxation exceeding the optimal solution is bounded by a polynomial in the problem size.

no code implementations • 30 Nov 2016 • Surbhi Goel, Varun Kanade, Adam Klivans, Justin Thaler

These results are in contrast to known efficient algorithms for reliably learning linear threshold functions, where $\epsilon$ must be $\Omega(1)$ and strong assumptions are required on the marginal distribution.

no code implementations • NeurIPS 2014 • Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans

We give an algorithm for exactly reconstructing f given random examples from the uniform distribution on $\{-1, 1\}^n$ that runs in time polynomial in $n$ and $2s$ and succeeds if the function satisfies the unique sign property: there is one output value which corresponds to a unique set of values of the participating parities.

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