Quivering Karmelo Anthony is convicted of murdering Austin Metcalf, 17... but now prosecutors have granted him Hail Mary that could see him jailed for as little as TWO YEARS She's always by Trump's side, trusted with the White House's biggest secrets... and she influences millions Inside Travis Kelce's plan to become'the Shaq of the NFL' after wedding Taylor Swift Leaked transcript of UNAIRED 60 Minutes interview exposes REAL reason'callous' CBS star Scott Pelley'deserved to be fired' Woke Canadian lawmakers fly into hilarious rage after conservative asks country's top scientist to define a woman I watched footage of the race crime that split America. Eva Longoria reunites with ex Tony Parker 15 years after cheating scandal split... as shocked fans react Caitlyn Jenner biographer and Robin Riker's ex William Hasley found dead on hiking trail at 78 My compulsive bathroom habit that so many are guilty of left me in excruciating pain. DR STUART reveals early signs... cures that work in days... and when to worry Epstein's massage fixer looks PETRIFIED as she's dragged into explosive congressional grilling - and reveals jaw-dropping'blackmail' theory Zodiac killer case takes bombshell turn as unsolved cipher is CRACKED... and America's top codebreakers say evidence is all pointing to one man Shamed ex mayor Misty Roberts is sentenced to 90 DAYS as she's branded a'predator with hair extensions' by enraged mother of 17-year-old sex assault victim Trump ERUPTS behind closed doors as top Republican pleads with him to axe Tulsi Gabbard's spy-chief replacement Trump's $70B immigration crackdown passes the House as sneaky loophole allows $1.8B weaponization'slush fund' to survive China's Nostradamus issues chilling warning about Trump's UFO file release: 'Atrocities are coming' A professor dubbed ' China's Nostradamus' has made a chilling prediction after the Trump administration released previously classified UFO files. Jiang Xueqin, a Chinese-Canadian educator and political commentator, earned the nickname after making a series of geopolitical predictions that supporters say later came true. Among them were forecasts that Donald Trump would return to the White House in 2024 and that the United States and Israel would become involved in a conflict with Iran under his administration.

The use of pessimism, when reasoning about datasets lacking exhaustive exploration, has recently gained prominence in offline reinforcement learning. Despite the robustness it adds to the algorithm, overly pessimistic reasoning can be equally damaging in precluding the discovery of good policies, which is an issue for the popular bonus-based pessimism. In this paper, we introduce the notion of Bellmanconsistent pessimism for general function approximation: instead of calculating a point-wise lower bound for the value function, we implement pessimism at the initial state over the set of functions consistent with the Bellman equations. Our theoretical guarantees only require Bellman closedness as standard in the exploratory setting, in which case bonus-based pessimism fails to provide guarantees. Even in the special case of linear function approximation where stronger expressivity assumptions hold, our result improves upon a recent bonus-based approach by O(d) in its sample complexity when the action space is finite and small. Remarkably, our algorithms automatically adapt to the best bias-variance tradeoff in the hindsight, whereas most prior approaches require tuning extra hyperparameters a priori.

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a diverse array of questions across five domains and eight question categories, reflecting varied entity popularity from popular to long-tail, and temporal dynamisms ranging from years to seconds. Our evaluation on this benchmark highlights the gap to fully trustworthy QA.

We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.

Autoencoder is a powerful unsupervised learning framework to learn latent representations by minimizing reconstruction loss of the input data [1]. Autoencoders have been widely used in unsupervised learning tasks such as representation learning [1] [2], denoising [3], and generative model [4][5]. Most autoencoders are under-complete autoencoders, for which the latent space is smaller than the input data [2]. Over-complete autoencoders have latent space larger than input data.

Define = log (M2H / ).

Then, we consider sufficient assumptions under which learning good policies requires polynomial number of episodes.

It has several appealing benefits that prior arts donothave.

Most of the existing acquisition policies are only one-step optimal, that is, optimal if the decision horizonwereone.