I sound Korean--because I am Korean. Can AI make me sound American? It all began, as these things often do, with an Instagram ad . "No one tells you this if you're an immigrant, but accent discrimination is a real thing," said a woman in the video. Her own accent is faintly Eastern European--so subtle it took me a few playbacks to notice.

In Russia's'blitz' of Ukraine, the question of appeasement is back Following another week of intensive and lethal Russian bombardment of Ukraine's cities, a composite image has been doing the rounds on Ukrainian social media. Underneath an old, black-and-white photo of Londoners queuing at a fruit and vegetable stall surrounded by the bombed-out rubble of the Blitz, a second image - this time in colour - creates a striking juxtaposition. Taken on Saturday, it shows shoppers thronging to similar stalls in a northern suburb of the Ukrainian capital, Kyiv, while a column of black smoke rises ominously in the background. Bombs can't stop markets, reads the caption linking the two images. The night before, as the city's sleep was interrupted once again by the now all-too-familiar booms of missile and drone strikes, two people were killed and nine others injured.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russian drone attacks on the Ukrainian capital, Kyiv, early on Sunday killed at least three people and wounded 29 others, according to Ukrainian Minister of Internal Affairs Ihor Klymenko. The wounded included seven children, Klymenko said.

Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods rely on computationally expensive min-max problems that limit their application in practice. We propose a novel formulation of adversarial training in reproducing kernel Hilbert spaces, shifting from input to feature-space perturbations. This reformu-lation enables the exact solution of inner maximization and efficient optimization. It also provides a regularized estimator that naturally adapts to the noise level and the smoothness of the underlying function. We establish conditions under which the feature-perturbed formulation is a relaxation of the original problem and propose an efficient optimization algorithm based on iterative kernel ridge regression. We provide generalization bounds that help to understand the properties of the method. We also extend the formulation to multiple kernel learning. Empirical evaluation shows good performance in both clean and adversarial settings.

To understand feature learning dynamics in neural networks, recent theoretical works have focused on gradient-based learning of Gaussian single-index models, where the label is a nonlinear function of a latent one-dimensional projection of the input. While the sample complexity of online SGD is determined by the information exponent of the link function, recent works improved this by performing multiple gradient steps on the same sample with different learning rates -- yielding a non-correlational update rule -- and instead are limited by the (potentially much smaller) generative exponent. However, this picture is only valid when these learning rates are sufficiently large. In this paper, we characterize the relationship between learning rate(s) and sample complexity for a broad class of gradient-based algorithms that encapsulates both correlational and non-correlational updates. We demonstrate that, in certain cases, there is a phase transition from an "information exponent regime" with small learning rate to a "generative exponent regime" with large learning rate. Our framework covers prior analyses of one-pass SGD and SGD with batch reuse, while also introducing a new layer-wise training algorithm that leverages a two-timescales approach (via different learning rates for each layer) to go beyond correlational queries without reusing samples or modifying the loss from squared error. Our theoretical study demonstrates that the choice of learning rate is as important as the design of the algorithm in achieving statistical and computational efficiency.

A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, like Q-learning, allow learners to make optimal decisions based on past experiences. This paper studies off-policy learning from biased data in complex and high-dimensional domains where \emph{unobserved confounding} cannot be ruled out a priori. Building on the well-celebrated Deep Q-Network (DQN), we propose a novel deep reinforcement learning algorithm robust to confounding biases in observed data. Specifically, our algorithm attempts to find a safe policy for the worst-case environment compatible with the observations. We apply our method to twelve confounded Atari games, and find that it consistently dominates the standard DQN in all games where the observed input to the behavioral and target policies mismatch and unobserved confounders exist.

Small subsets of data with disproportionate influence on model outcomes can have dramatic impacts on conclusions, with a few data points sometimes overturning key findings. While recent work has developed methods to identify these most influential sets, no formal theory exists to determine when their influence reflects genuine problems rather than natural sampling variation. We address this gap by developing a principled framework for assessing the statistical significance of most influential sets. Our theoretical results characterize the extreme value distributions of maximal influence and enable rigorous hypothesis tests for excessive influence, replacing current ad-hoc sensitivity checks. We demonstrate the practical value of our approach through applications across economics, biology, and machine learning benchmarks.

Entitled son, 21, of top lawyer mows down police with his Mercedes G-Wagen...as he smiles in his mugshot Trump'humiliates' speaker Mike Johnson in private conversation as government shutdown rumbles on Tupac's humiliating intimate disfigurement revealed... and how his lies to cover it up led to his murder'I'm Madeline - and this is what I have to say to Lily Allen': Read world exclusive reveal of mother who had affair with star's husband David Harbour, how it started and how she feels about THOSE texts being exposed Loved up Katy Perry holds hands with Justin Trudeau as they officially confirm romance while celebrating the singer's birthday in Paris Furrow-browed boyfriend'strangled girlfriend and set her house on fire while newborn baby was inside' I've uncovered my husband's filthy Viagra habit: But, warns DEAR JANE, one thing YOU are doing is making it so much worse I've started having heart palpitations. Jackie Kennedy's revenge romance with American political icon: Revealed for first time in titillating love letters, the man who helped her cope with JFK's cheating The night that haunted a Wisconsin town forever... and the little girl whose trick-or-treat next door ended in horror Why going gray may save you from CANCER... as scientists make bombshell breakthrough Brazen demands for flying private REVEALED by the woman paid to fulfill them: 'Answer is always yes' They sneered at Trump's'eagle graveyards' - but now Biden's hated windmills crippling an American legend are haunting the US military Kim Kardashian's just been caught in a despicable lie. She can cry all she wants... there's no hiding the truth now: CAROLINE BULLOCK Tua Tagovailoa's swollen eye sparks concern after Dolphins QB woke up with mystery illness on day of Falcons game JD Vance's wife is given secret role in Trump's deal-making inner circle: 'I'll have Usha look at it' The Biden blunder that allowed an alleged October 7 'monster' to become a restaurant worker in Louisiana How I reversed my hair loss and lost 8 stone aged 45 - without weight-loss jabs. Urgent warning over cannabis as UK's top psychiatrist warns it isn't safe for young brains still developing It may seem like a relatively harmless right of passage. But cannabis isn't safe for young brains still developing, the UK's top psychiatrist has warned.

Entitled son, 21, of top lawyer mows down police with his Mercedes G-Wagen...as he smiles in his mugshot Tupac's humiliating intimate disfigurement revealed... and how his lies to cover it up led to his murder Trump'humiliates' speaker Mike Johnson in private conversation as government shutdown rumbles on'I'm Madeline - and this is what I have to say to Lily Allen': Read world exclusive reveal of mother who had affair with star's husband David Harbour, how it started and how she feels about THOSE texts being exposed Loved up Katy Perry holds hands with Justin Trudeau as they officially confirm romance while celebrating the singer's birthday in Paris Furrow-browed boyfriend'strangled girlfriend and set her house on fire while newborn baby was inside' I've uncovered my husband's filthy Viagra habit: But, warns DEAR JANE, one thing YOU are doing is making it so much worse I've started having heart palpitations. Jackie Kennedy's revenge romance with American political icon: Revealed for first time in titillating love letters, the man who helped her cope with JFK's cheating The night that haunted a Wisconsin town forever... and the little girl whose trick-or-treat next door ended in horror Why going gray may save you from CANCER... as scientists make bombshell breakthrough Brazen demands for flying private REVEALED by the woman paid to fulfill them: 'Answer is always yes' They sneered at Trump's'eagle graveyards' - but now Biden's hated windmills crippling an American legend are haunting the US military Kim Kardashian's just been caught in a despicable lie. She can cry all she wants... there's no hiding the truth now: CAROLINE BULLOCK Tua Tagovailoa's swollen eye sparks concern after Dolphins QB woke up with mystery illness on day of Falcons game JD Vance's wife is given secret role in Trump's deal-making inner circle: 'I'll have Usha look at it' The Biden blunder that allowed an alleged October 7 'monster' to become a restaurant worker in Louisiana How I reversed my hair loss and lost 8 stone aged 45 - without weight-loss jabs. A growing network of at least 5,000 data centers across the US is becoming a hidden public health threat, scientists have warned. That is because the energy-hungry backbone of artificial intelligence pumps out dangerous pollutants that can cause asthma, cancer and even death.

Three Chinese nationals have been arrested in Georgia on suspicion of attempting to illegally purchase 2kg of uranium. Lasha Maghradze, deputy head of the nation's State Security Service (SSG), told a news briefing the group planned to pay $400,000 (£300,570) for the nuclear material in the capital, Tblisi, before transporting it to China via Russia. The alleged plot was unearthed by intelligence agents while one member of the group was attempting to buy the radioactive substance on the black market, he said. The three pleaded not guilty at a court in Tblisi and have been placed in custody to prevent them fleeing the country, according to public broadcaster Georgia Today. They face up to five years in prison under a provision of Georgia's criminal code banning the purchasing of nuclear material.