no code implementations • 29 Oct 2024 • HANLIN ZHANG, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean Foster, Sham Kakade

Training large-scale models under given resources requires careful design of parallelism strategies.

no code implementations • 16 Oct 2024 • Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu, Mikhail Belkin

While transformers are capable of both context-scaling and task-scaling, we empirically show that standard Multi-Layer Perceptrons (MLPs) with vectorized input are only capable of task-scaling.

no code implementations • 12 Jun 2024 • Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett

The typical training of neural networks using large stepsize gradient descent (GD) under the logistic loss often involves two distinct phases, where the empirical risk oscillates in the first phase but decreases monotonically in the second phase.

no code implementations • 12 Jun 2024 • Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee

Empirically, large-scale deep learning models often satisfy a neural scaling law: the test error of the trained model improves polynomially as the model size and data size grow.

no code implementations • 24 Feb 2024 • Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

We consider gradient descent (GD) with a constant stepsize applied to logistic regression with linearly separable data, where the constant stepsize $\eta$ is so large that the loss initially oscillates.

no code implementations • 22 Feb 2024 • Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett

We study the \emph{in-context learning} (ICL) ability of a \emph{Linear Transformer Block} (LTB) that combines a linear attention component and a linear multi-layer perceptron (MLP) component.

no code implementations • 23 Nov 2023 • Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

Additionally, when our analysis is specialized to linear regression in the strongly convex setting, it yields a tighter bound for bias error than the best-known result.

no code implementations • 12 Oct 2023 • Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett

Transformers pretrained on diverse tasks exhibit remarkable in-context learning (ICL) capabilities, enabling them to solve unseen tasks solely based on input contexts without adjusting model parameters.

no code implementations • NeurIPS 2023 • Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman

For single-round FFE, it is known that count sketching is nearly information-theoretically optimal for achieving the fundamental accuracy-communication trade-offs [Chen et al., 2022].

no code implementations • 17 Mar 2023 • Haoran Li, Jingfeng Wu, Vladimir Braverman

We consider a continual learning (CL) problem with two linear regression tasks in the fixed design setting, where the feature vectors are assumed fixed and the labels are assumed to be random variables.

no code implementations • 3 Mar 2023 • Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

On the other hand, we provide some negative results for stochastic gradient descent (SGD) for ReLU regression with symmetric Bernoulli data: if the model is well-specified, the excess risk of SGD is provably no better than that of GLM-tron ignoring constant factors, for each problem instance; and in the noiseless case, GLM-tron can achieve a small risk while SGD unavoidably suffers from a constant risk in expectation.

no code implementations • 3 Aug 2022 • Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

Our bounds suggest that for a large class of linear regression instances, transfer learning with $O(N^2)$ source data (and scarce or no target data) is as effective as supervised learning with $N$ target data.

no code implementations • 7 Mar 2022 • Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

Stochastic gradient descent (SGD) has achieved great success due to its superior performance in both optimization and generalization.

no code implementations • 12 Oct 2021 • Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

In this paper, we provide a problem-dependent analysis on the last iterate risk bounds of SGD with decaying stepsize, for (overparameterized) linear regression problems.

1 code implementation • 11 Aug 2021 • Jingfeng Wu, Vladimir Braverman, Lin F. Yang

In particular, for an unknown finite-horizon Markov decision process, the algorithm takes only $\widetilde{\mathcal{O}} (1/\epsilon \cdot (H^3SA / \rho + H^4 S^2 A) )$ episodes of exploration, and is able to obtain an $\epsilon$-optimal policy for a post-revealed reward with sub-optimality gap at least $\rho$, where $S$ is the number of states, $A$ is the number of actions, and $H$ is the length of the horizon, obtaining a nearly \emph{quadratic saving} in terms of $\epsilon$.

no code implementations • NeurIPS 2021 • Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade

Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice, which has been hypothesized to play an important role in the generalization of modern machine learning approaches.

no code implementations • 17 Apr 2021 • Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman

In practice and due to computational constraints, most SR methods crudely approximate the importance matrix by its diagonal.

no code implementations • 23 Mar 2021 • Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

More specifically, for SGD with iterate averaging, we demonstrate the sharpness of the established excess risk bound by proving a matching lower bound (up to constant factors).

1 code implementation • NeurIPS 2021 • Jingfeng Wu, Vladimir Braverman, Lin F. Yang

We formalize this problem as an episodic learning problem on a Markov decision process, where transitions are unknown and a reward function is the inner product of a preference vector with pre-specified multi-objective reward functions.

Multi-Objective Reinforcement Learning reinforcement-learning

no code implementations • ICLR 2021 • Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

Understanding the algorithmic bias of \emph{stochastic gradient descent} (SGD) is one of the key challenges in modern machine learning and deep learning theory.

1 code implementation • ICML 2020 • Jingfeng Wu, Vladimir Braverman, Lin F. Yang

In sum, we obtain adjustable regularization for free for a large class of optimization problems and resolve an open question raised by Neu and Rosasco.

1 code implementation • ICML 2020 • Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning.

no code implementations • ICLR 2019 • Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics.

1 code implementation • CVPR 2019 • Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu

The proposed TNAR is composed by two complementary parts, the tangent adversarial regularization (TAR) and the normal adversarial regularization (NAR).

1 code implementation • ICLR 2019 • Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

Along this line, we study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics.

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