Unverified50% confidenceFactExact time
EL-F degenerates to a greedy variant when the empirical likelihood radius is 0, and the paper's quantitative guarantees also cover this special case.
1
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50%
Confidence
Long-term
Relevance
10/14/2026
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Related Claims
UnverifiedThe paper proves that the logarithmic factor appearing in previous risk bounds is not essential and can be completely eliminated, thereby improving the convergence rate to O(1/√N), consistent with the classical law of large numbers.65% similarUnverifiedThe residual structure makes F=0 the default option—as long as the F(x) weights are close to zero, the residual block degenerates into an identity mapping, thereby resolving the optimization difficulty of SGD in learning identity mappings.63% similarUnverifiedThe main result of the paper is that Q-Learning with zero initialization and constant step size can, with high probability, reduce the sup-norm error to within ε using Õ(H^4/(μ_min·ε^2)+h_star) state transitions (for 0<ε≤1).63% similarUnverifiedAblation experiments in the DiT paper show that AdaLN-Zero outperforms alternative approaches such as cross-attention and concatenation in terms of the balance between parameter efficiency and generation quality.62% similarUnverifiedThe applicable scenarios of EL-F mainly lie in real systems where the reward scale is unknown, the distribution may have sub-exponential tails, and the number of rounds is finite, such as online advertising bidding, cold-start exploration in recommender systems, and traffic allocation in A/B testing.62% similar
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