For continuous state-action space scenarios, classical reinforcement learning (RL) theory predominantly focuses on low-rank Markov decision processes (MDPs), which provide sample-efficient guarantees at the expense of restrictive structural assumptions. Kernel smoothing model-based approaches offer a promising alternative paradigm that instead leverages the smoothness of the MDP and employs non-parametric kernel smoothing estimates of transition dynamics. This paper proposes a new kernel-smoothing model-based approach for online reinforcement learning in finite-horizon settings under Lipschitz continuity assumptions on the MDP. By incorporating a Bernstein-style exploration bonus into the kernel smoothing framework, our method achieves a regret bound which improves upon the state-of-the-art regret bound in its dependence on the horizon. The theoretical advancement relies on a delicate analysis of the synergy between Bernstein-style bonuses and kernel smoothing, where a new tight Bernstein-type concentration inequality for martingales may be of independent interest.

Tensor regression is an important tool for tensor data analysis, but existing works have not considered the impact of outliers, making them potentially sensitive to such data points. This paper proposes a low tubal rank robust regression method for analyzing high-dimensional tensor data with heavy-tailed random noise. The proposed method is based on a nonconvex relaxation of the tensor tubal rank within a general optimization framework, which allows for nonconvexity in both the loss and penalty functions. We develop an implementable estimation algorithm and establish its global convergence under some mild assumptions. Furthermore, we provide general statistical theories regarding stationary point, including the rates of convergence and bounds on the prediction error. These theoretical results cover many important models, such as linear models, generalized linear models, and Huber regression, and even encompass some nonconvex losses like correntropy and minimum distance criterion-induced losses. Supportive numerical evidence is provided through simulations and application studies.

Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority voting (MV), a test-time technique called self-consistency. When we truncate a CoT partway through and regenerate the remainder, we observe that traces with correct answers reproduce their original answer more often than traces with wrong answers. We use this difference as a reliability signal, prefix consistency, that weights each candidate answer by how often it reappears under regeneration. It requires no access to token log-probabilities or self-rating prompts. Across five reasoning models and four math and science benchmarks, prefix consistency is the best correctness predictor in most settings, and reweighting votes by it reaches Standard MV plateau accuracy at up to 21x fewer tokens (median 4.6x). Our code is available at https://github.com/naoto-iwase/prefix-consistency.

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($Δν$) and the frequency at maximum power ($ν_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($ΔΠ_{1}$), in addition to $Δν$ and $ν_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $Δν$ and $ν_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $Δν$ for only about 23% of the stars. Additionally, we get reliable $ΔΠ_{1}$ inferences for about 200 young red-giants from K2. For these $ΔΠ_{1}$ inferences, we see a good match with the well known $Δν-ΔΠ_{1}$ degenerate sequence observed in Kepler red-giants.

Death of Alabama woman, 22, 'accidentally' shot in chest by boyfriend's dad is ruled a HOMICIDE Two small airlines join forces to create America's newest budget carrier after Spirit collapse leaves millions scrambling Horrifying final days of killer dad Chris Watts' pregnant wife before she was slaughtered alongside their daughters. Read all the chilling texts and receipts in full for first time: 'My eyes burn from crying' I'm a pastor who attended a secret UFO disclosure meeting. We saw images of'translucent beings' that chilled me to the bone... the files could fulfil a dark biblical prophecy Former NFL player Josh Mauro's tragic cause of death revealed after league was left'devastated' by ex-Cardinals and Giants man's sudden passing at 35 Cheerful Christian mom is pillar of Florida community and loves going on TV... but she has a childhood secret so evil that she stuttered with shock when confronted with it Taxpayers to foot Trump's $1.7 BILLION bill as President sues his own government: 'I'm paying myself' How I lost 3 STONE in 3 WEEKS. I've reversed pre-diabetes and no longer need a knee op: DONAL MACINTYRE's extraordinary investigation Popular megachurch in crisis as senior pastor suddenly quits... as bosses furiously DENY sex scandal Husband of doomed dive group leader says'something must have happened down there' as mystery surrounds why the five attempted to explore'cave so deep even divers with best equipment don't try' Greeks savage Kimberly Guilfoyle as Trump's ambassador opens McDonald's in country celebrated for world-class food Trump touts'fantastic' China trade win on Air Force One... but Wall Street is punishing the President I'm godfather to Candace Owens' daughter and Charlie Kirk was my friend... so I know the real reason she's attacking Erika - and I'll never publicly condemn her Wealthy dad'snarled the worst thing a parent could say' to younger daughter before he allegedly executed wife outside their gated community home during nightmare divorce Reese Witherspoon and Ryan Phillippe reunite for son's NYU graduation... as Kate Hudson cheers on her boy at same ceremony with Goldie Hawn and Kurt Russell'How do you live with that?' Disgraced Eric Swalwell's'blindsided' wife dresses for revenge... as friends reveal brutal toll sex assault scandal has had on young mom Judge declares another mistrial in disgraced Hollywood mogul Harvey Weinstein's rape case Can't lose weight no matter what you do? These are the 7 surprising reasons why, including'healthy' hacks actually making you put on pounds.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' At least three people have been killed in Russian attacks on Ukraine in the past 24 hours despite a three-day ceasefire announced by US President Donald Trump that came into effect on May 9. Regional authorities on Sunday reported one death each in Ukraine's Zaporizhia, Dnipropetrovsk, and Kherson regions. Governor Oleksandr Prokudin confirmed the death on Telegram, saying the woman had been struck while walking down the street. Seven people, including a child, have also been injured across the region in drone or artillery attacks since early Saturday, he added. Ivan Fedorov, the governor of the southeastern Zaporizhia region, said one person had been killed and three others injured by artillery and drone attacks in the past 24 hours. In the northeastern Kharkiv region, Governor Oleh Syniehubov said eight people, including two children, were injured in drone attacks on the city of Kharkiv and nearby settlements.

How well do you know Iran? A fragile ceasefire in the US-Israel war on Iran is coming under growing strain as several Gulf countries have reported drone attacks. Qatar said on Sunday that a drone struck a cargo ship in Qatari waters, sparking a fire, while Kuwait and the United Arab Emirates said they repelled drone attacks. Qatar's Ministry of Defence said the freighter had been arriving in the country's waters from the UAE capital, Abu Dhabi, and was hit by a drone northeast of the port of Mesaieed. "The vessel continued its journey toward Mesaieed Port after the fire was brought under control," the ministry said. The United Kingdom Maritime Trade Operations (UKMTO) said a bulk carrier reported being struck by an "unknown projectile", and a small fire had been extinguished, but there were no casualties from the incident.

A member of NOPO, Iran's counter-terrorism special force, stands guard under a billboard of Iran's late supreme leader, Ayatollah Ali Khamenei, in Tehran, on April 23. Doha - A drone struck a commercial vessel in Qatari waters on Sunday, the country's defense ministry said, after Iran's Islamic Revolutionary Guards threatened to target U.S. vessels in the region. Arch-foes the United States and Iran have been clashing in the Gulf and trading accusations in recent days, as Washington waits for Tehran to respond to its latest negotiating position. A commercial cargo vessel in the country's territorial waters -- northeast of Mesaieed Port -- coming from Abu Dhabi, was targeted by a drone on Sunday morning. The incident resulted in a limited fire on board the vessel, with no reported injuries, the Qatari ministry said on X.

Your cell phone can handle basic language translation, but bespoke tools can offer a much more immersive experience. Hans Christian Andersen once said, "To travel is to live," and while that's a romantic notion, he probably wasn't careening through Gyeongju, South Korea, at midnight in the back of a taxi with a driver who didn't speak a lick of English. Today's world traveler has it awfully easy when it comes to understanding the local lingo, as even a basic modern cell phone app can offer a pretty good translation of common phrases delivered in everything from Abkhaz to Zulu. Type or speak a sentence or two into the app, tap a button, and out it returns in the language of your choice. Tap another button, and your phone can even speak those sentences aloud.

Paige Spiranac hits bombs at Truist pro-am after years of being shunned, fighter jets interrupt golf & MEAT! Disney's big mistake with Star Wars was turning Luke Skywalker into Mark Hamill: miserable, pathetic and sad WWE US Champion Tiffany Stratton takes her new belt for a celebratory ride on a jet ski, moose delay & MEAT! Nick Bosa's model girlfriend starts summer in a pink bikini on a tennis court, crazy Mark Hamill & plandemic! Best friend booted from wedding for bride's bachelorette cheating, sugar daddy has money troubles & Reno Ruth Taylor Sheridan's hit CIA/military series gets major update ahead of new season premiere Smokin' Charley Hull is back to promoting nicotine after giving up the cigs, Mets booth mess & steak tacos! Hayden Panettiere has a very important message to share with everyone, she's into women too Cameron Brink explores the jungle in a bikini before WNBA tip, Italian PM posts some thirst & woke Star Wars! Perez Hilton heaps praise on Ivanka Trump, takes swipe at Kardashians during appearance on Tomi Lahren's show I don't buy that Iran has a'divided government,' US Navy captain says Democratic congressman blames Trump for disruption of world's oil supply Putin is'really worried' about Ukrainian drone strikes: National security expert OH, DEER!: Nursing home receives unexpected visitor Does the U.S. Still Need NATO?