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Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
RL is essentially distinct from and probably harder than that in standard offline RL.
Andrey Kuzmin, Markus Nagel, Mart van Baalen, Arash Behboodi, Tijmen Blankevoort Qualcomm AI Research
In this paper, we set out to answer the question on which is better: neural network quantization or pruning? By answering this question, we hope to inform design decisions made on neural network hardware going forward. We provide an extensive comparison between the two techniques for compressing deep neural networks.
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
In this work, we analyze stability as a property for reliable feature attribution methods.
Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu
Typically, preference optimization is approached as an offline supervised learning task using manually crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under-explored.
Benchmark Data Repositories for Better Benchmarking
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities.
