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Found 10 papers, 1 papers with code
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
We study the asymptotic generalization of an overparameterized linear model for multiclass classification under the Gaussian covariates bi-level model introduced in Subramanian et al.~'22, where the number of data points, features, and classes all grow together.
Generalization for multiclass classification with overparameterized linear models
Via an overparameterized linear model with Gaussian features, we provide conditions for good generalization for multiclass classification of minimum-norm interpolating solutions in an asymptotic setting where both the number of underlying features and the number of classes scale with the number of training points.
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective
We find that the learned model is susceptible to adversaries in an intermediate regime where classification generalizes but regression does not.
On the Impossibility of Convergence of Mixed Strategies with No Regret Learning
We study the limiting behavior of the mixed strategies that result from optimal no-regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game.
Classification vs regression in overparameterized regimes: Does the loss function matter?
We compare classification and regression tasks in an overparameterized linear model with Gaussian features.
Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design
We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed.
Learning Physical-Layer Communication with Quantized Feedback
A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for non-stationary signal distributions.
Harmless interpolation of noisy data in regression
A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data is noisy and there are more parameters than data points.
Best of many worlds: Robust model selection for online supervised learning
We introduce algorithms for online, full-information prediction that are competitive with contextual tree experts of unknown complexity, in both probabilistic and adversarial settings.
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication
Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for compatibility and efficiency.
Multi-agent Reinforcement Learning
reinforcement-learning
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