no code implementations • 25 Mar 2024 • John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur

We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments.

1 code implementation • 2 Nov 2023 • Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions.

no code implementations • 8 Feb 2022 • Alnur Ali, Maxime Cauchois, John C. Duchi

The statistical machine learning community has demonstrated considerable resourcefulness over the years in developing highly expressive tools for estimation, prediction, and inference.

no code implementations • 7 Jan 2021 • Karan Chadha, Gary Cheng, John C. Duchi

We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch and accelerated setting.

no code implementations • NeurIPS 2020 • Hilal Asi, John C. Duchi

We study and provide instance-optimal algorithms in differential privacy by extending and approximating the inverse sensitivity mechanism.

no code implementations • NeurIPS 2020 • Hilal Asi, Karan Chadha, Gary Cheng, John C. Duchi

In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions.

no code implementations • NeurIPS 2020 • John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye

We present an extension of the conditional gradient method to problems whose feasible sets are convex cones.

1 code implementation • NeurIPS 2020 • Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $\chi^2$ divergence uncertainty sets.

no code implementations • 10 Aug 2020 • Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi

One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations.

no code implementations • 24 Jun 2020 • Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

We design an algorithm which finds an $\epsilon$-approximate stationary point (with $\|\nabla F(x)\|\le \epsilon$) using $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector products, matching guarantees that were previously available only under a stronger assumption of access to multiple queries with the same random seed.

no code implementations • 16 May 2020 • Hilal Asi, John C. Duchi

We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities.

no code implementations • 5 Dec 2019 • Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake Woodworth

We lower bound the complexity of finding $\epsilon$-stationary points (with gradient norm at most $\epsilon$) using stochastic first-order methods.

no code implementations • 25 Sep 2019 • Sang Michael Xie*, Aditi Raghunathan*, Fanny Yang, John C. Duchi, Percy Liang

Empirically, data augmentation sometimes improves and sometimes hurts test error, even when only adding points with labels from the true conditional distribution that the hypothesis class is expressive enough to fit.

no code implementations • NeurIPS 2019 • Daniel Levy, John C. Duchi

We study the impact of the constraint set and gradient geometry on the convergence of online and stochastic methods for convex optimization, providing a characterization of the geometries for which stochastic gradient and adaptive gradient methods are (minimax) optimal.

no code implementations • ICML Workshop Deep_Phenomen 2019 • Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary).

4 code implementations • NeurIPS 2019 • Yair Carmon, aditi raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi

We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.

1 code implementation • 20 Mar 2019 • Hilal Asi, John C. Duchi

Standard stochastic optimization methods are brittle, sensitive to stepsize choices and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives.

no code implementations • 7 Mar 2019 • Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian

We show that a simple randomized sketch of the matrix multiplicative weight (MMW) update enjoys (in expectation) the same regret bounds as MMW, up to a small constant factor.

no code implementations • 10 Jan 2019 • Alon Kipnis, John C. Duchi

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator.

no code implementations • NeurIPS 2018 • Yair Carmon, John C. Duchi

We provide convergence rates for Krylov subspace solutions to the trust-region and cubic-regularized (nonconvex) quadratic problems.

no code implementations • 12 Oct 2018 • Hilal Asi, John C. Duchi

We develop model-based methods for solving stochastic convex optimization problems, introducing the approximate-proximal point, or aProx, family, which includes stochastic subgradient, proximal point, and bundle methods.

1 code implementation • 11 Apr 2018 • Tatsunori B. Hashimoto, Steve Yadlowsky, John C. Duchi

We develop an algorithm for minimizing a function using $n$ batched function value measurements at each of $T$ rounds by using classifiers to identify a function's sublevel set.

1 code implementation • NeurIPS 2017 • Tatsunori B. Hashimoto, John C. Duchi, Percy Liang

Our goal is to extract meaningful transformations from raw images, such as varying the thickness of lines in handwriting or the lighting in a portrait.

no code implementations • 2 Aug 2017 • Alon Kipnis, John C. Duchi

We study the squared error risk in this estimation as a function of the number of samples and one-bit measurements $n$.

Statistics Theory Statistics Theory

no code implementations • ICML 2017 • Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

We develop and analyze a variant of Nesterov’s accelerated gradient descent (AGD) for minimization of smooth non-convex functions.

no code implementations • ICML 2017 • Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi

Standard forms of coordinate and stochastic gradient methods do not adapt to structure in data; their good behavior under random sampling is predicated on uniformity in data.

no code implementations • NeurIPS 2016 • Hongseok Namkoong, John C. Duchi

We develop efficient solution methods for a robust empirical risk minimization problem designed to give calibrated confidence intervals on performance and provide optimal tradeoffs between bias and variance.

1 code implementation • NeurIPS 2016 • Aman Sinha, John C. Duchi

We extend the randomized-feature approach to the task of learning a kernel (via its associated random features).

no code implementations • NeurIPS 2015 • Sorathan Chaturapruek, John C. Duchi, Christopher Ré

We show that asymptotically, completely asynchronous stochastic gradient procedures achieve optimal (even to constant factors) convergence rates for the solution of convex optimization problems under nearly the same conditions required for asymptotic optimality of standard stochastic gradient procedures.

1 code implementation • 4 Aug 2015 • John C. Duchi, Sorathan Chaturapruek, Christopher Ré

We show that asymptotically, completely asynchronous stochastic gradient procedures achieve optimal (even to constant factors) convergence rates for the solution of convex optimization problems under nearly the same conditions required for asymptotic optimality of standard stochastic gradient procedures.

no code implementations • 5 May 2014 • John C. Duchi, Michael. I. Jordan, Martin J. Wainwright, Yuchen Zhang

Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines.

no code implementations • 7 Dec 2013 • John C. Duchi, Michael. I. Jordan, Martin J. Wainwright, Andre Wibisono

We consider derivative-free algorithms for stochastic and non-stochastic convex optimization problems that use only function values rather than gradients.

no code implementations • 22 May 2013 • Yuchen Zhang, John C. Duchi, Martin J. Wainwright

We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression.

no code implementations • NeurIPS 2012 • Andre Wibisono, Martin J. Wainwright, Michael. I. Jordan, John C. Duchi

We consider derivative-free algorithms for stochastic optimization problems that use only noisy function values rather than gradients, analyzing their finite-sample convergence rates.

no code implementations • NeurIPS 2012 • Yuchen Zhang, Martin J. Wainwright, John C. Duchi

The first algorithm is an averaging method that distributes the $N$ data samples evenly to $m$ machines, performs separate minimization on each subset, and then averages the estimates.

no code implementations • NeurIPS 2012 • John C. Duchi, Michael. I. Jordan, Martin J. Wainwright

We study statistical risk minimization problems under a privacy model in which the data is kept confidential even from the learner.

no code implementations • 19 Sep 2012 • Yuchen Zhang, John C. Duchi, Martin Wainwright

We analyze two communication-efficient algorithms for distributed statistical optimization on large-scale data sets.

no code implementations • 7 Apr 2012 • John C. Duchi, Lester Mackey, Michael. I. Jordan

With these negative results as motivation, we present a new approach to supervised ranking based on aggregation of partial preferences, and we develop $U$-statistic-based empirical risk minimization procedures.

no code implementations • NeurIPS 2011 • Alekh Agarwal, John C. Duchi

We analyze the convergence of gradient-based optimization algorithms whose updates depend on delayed stochastic gradient information.

no code implementations • NeurIPS 2010 • Alekh Agarwal, Martin J. Wainwright, John C. Duchi

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication.

no code implementations • NeurIPS 2009 • Yoram Singer, John C. Duchi

We derive concrete and very simple algorithms for minimization of loss functions with $\ell_1$, $\ell_2$, $\ell_2^2$, and $\ell_\infty$ regularization.

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