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Found 16 papers, 0 papers with code
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
Optimization of convex functions under stochastic zeroth-order feedback has been a major and challenging question in online learning.
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics
Auto-regressive large language models (LLMs) show impressive capacities to solve many complex reasoning tasks while struggling with some simple logical reasoning tasks such as inverse search: when trained on ''A is B'', LLM fails to directly conclude ''B is A'' during inference, which is known as the ''reversal curse'' (Berglund et al., 2023).
Data Acquisition via Experimental Design for Decentralized Data Markets
Acquiring high-quality training data is essential for current machine learning models.
Towards Optimal Statistical Watermarking
Key to our formulation is a coupling of the output tokens and the rejection region, realized by pseudo-random generators in practice, that allows non-trivial trade-offs between the Type I error and Type II error.
On Representation Complexity of Model-based and Model-free Reinforcement Learning
To the best of our knowledge, this work is the first to study the circuit complexity of RL, which also provides a rigorous framework for future research.
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
We consider a fundamental setting in which the objective function is quadratic, and provide the first tight characterization of the optimal Hessian-dependent sample complexity.
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
This creates a tension between the principal (the FL platform designer) who cares about global performance and the agents (the data collectors) who care about local performance.
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e. g., Bellman-completeness) and the data coverage (e. g., all-policy concentrability).
InstaHide’s Sample Complexity When Mixing Two Private Images
Inspired by InstaHide challenge [Huang, Song, Li and Arora'20], [Chen, Song and Zhuo'20] recently provides one mathematical formulation of InstaHide attack problem under Gaussian images distribution.
Towards General Function Approximation in Zero-Sum Markov Games
In the {coordinated} setting where both players are controlled by the agent, we propose a model-based algorithm and a model-free algorithm.
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
While the theory of RL has traditionally focused on linear function approximation (or eluder dimension) approaches, little is known about nonlinear RL with neural net approximations of the Q functions.
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
This work considers a large family of bandit problems where the unknown underlying reward function is non-concave, including the low-rank generalized linear bandit problems and two-layer neural network with polynomial activation bandit problem.
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
These can often be accounted for via regularized RL, which augments the target value function with a structure-promoting regularizer.
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Nevertheless, training analysis of neural networks in FL is non-trivial for two reasons: first, the objective loss function we are optimizing is non-smooth and non-convex, and second, we are even not updating in the gradient direction.
Solving SDP Faster: A Robust IPM Framework and Efficient Implementation
This paper introduces a new robust interior point method analysis for semidefinite programming (SDP).
Optimization and Control Data Structures and Algorithms
InstaHide's Sample Complexity When Mixing Two Private Images
On the current InstaHide challenge setup, where each InstaHide image is a mixture of two private images, we present a new algorithm to recover all the private images with a provable guarantee and optimal sample complexity.
