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Temporal-Difference Learning Using Distributed Error Signals Jonas Guan
It is unclear whether distributed errors alone are sufficient for synapses to make coordinated updates to learn complex, nonlinear reward-based learning tasks.
Contextual Stochastic Bilevel Optimization
Multilevel Monte-Carlo (MLMC) technique and establish its sample and computational complexities.
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details.
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why?
Morris Alper and Hadar A verbuch-Elor Tel Aviv University Contents
Unless stated otherwise we use American English for transcriptions.
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs).
