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Found 12 papers, 3 papers with code
A Sinkhorn-type Algorithm for Constrained Optimal Transport
We derive the corresponding entropy regularization formulation and introduce a Sinkhorn-type algorithm for such constrained OT problems supported by theoretical guarantees.
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
To achieve possibly super-exponential convergence, we present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine.
DPZero: Private Fine-Tuning of Language Models without Backpropagation
The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy.
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while significantly underperforming them on outlier data.
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
We provide a general framework for solving differentially private stochastic minimax optimization (DP-SMO) problems, which enables the practitioners to bring their own base optimization algorithm and use it as a black-box to obtain the near-optimal privacy-loss trade-off.
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
We also provide a direct single-loop algorithm, using the LPD method, that achieves the iteration complexity of $O(\sqrt{\frac{L_x}{\varepsilon}} + \frac{\|A\|}{\sqrt{\mu_y \varepsilon}} + \sqrt{\frac{L_y}{\varepsilon}})$.
Sample Efficient Linear Meta-Learning by Alternating Minimization
We show that, for a constant subspace dimension MLLAM obtains nearly-optimal estimation error, despite requiring only $\Omega(\log d)$ samples per task.
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Further, instead of a PO if we only have a linear minimization oracle (LMO, a la Frank-Wolfe) to access the constraint set, an extension of our method, MOLES, finds a feasible $\epsilon$-suboptimal solution using $O(\epsilon^{-2})$ LMO calls and FO calls---both match known lower bounds, resolving a question left open since White (1993).
Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimization problems $\min_x \max_y g(x, y)$ where $g(\cdot,\cdot)$ is smooth and $g(x,\cdot)$ is concave for each $x$.
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct properties of the target distribution.
Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains.
Robustness of Conditional GANs to Noisy Labels
When the distribution of the noise is known, we introduce a novel architecture which we call Robust Conditional GAN (RCGAN).
