Search Results for author: Tianjian Huang

Found 7 papers, 4 papers with code

Optimal Differentially Private Learning with Public Data

1 code implementation26 Jun 2023 Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

What is the optimal (worst-case) error of a DP model trained over a private data set while having access to side public data?

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

1 code implementation24 Feb 2022 Daniel Lundstrom, Tianjian Huang, Meisam Razaviyayn

Attribution methods address the issue of explainability by quantifying the importance of an input feature for a model prediction.

Robustness through Data Augmentation Loss Consistency

1 code implementation21 Oct 2021 Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami

Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.

Multi-domain Dialogue State Tracking Visual Question Answering

A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems

no code implementations10 Jun 2021 Babak Barazandeh, Tianjian Huang, George Michailidis

Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs).

Alternating Direction Method of Multipliers for Quantization

no code implementations8 Sep 2020 Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn

For such optimization problems, we study the performance of the Alternating Direction Method of Multipliers for Quantization ($\texttt{ADMM-Q}$) algorithm, which is a variant of the widely-used ADMM method applied to our discrete optimization problem.


Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances

no code implementations15 Jun 2020 Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong

The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games.

Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods

1 code implementation NeurIPS 2019 Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn

In this paper, we study the problem in the non-convex regime and show that an \varepsilon--first order stationary point of the game can be computed when one of the player's objective can be optimized to global optimality efficiently.

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