Staff at WiseTech have been waiting months to be told if they are among the employees the company is to cut due to advances in AI. Staff at WiseTech have been waiting months to be told if they are among the employees the company is to cut due to advances in AI. WiseTech begins redundancies - but omits'AI' from emails to Chinese employees, workers say WiseTech has begun informing staff that they will lose their jobs as part of redundancies the company has said is due to artificial intelligence advancements - although an email to staff in China omitted the word "AI" after a court case against another company in the country. Staff at WiseTech have been waiting almost three months to be told if they are among the 2,000 people the logistics software company is to cut due to advances in AI. The Australian Stock Exchange-listed company announced in late February it would lay off almost 30% of its 7,000-strong workforce across 40 countries.

It's now one of the 10 countries where (supervised) FSD is now available. Tesla's Full Self-Driving (FSD) driver-assistance system is now available in China . In a post on X, Tesla listed China as one of the 10 countries wherein FSD is already available. The company is, of course, talking about the supervised version of the technology, seeing as unsupervised FSD is still not available to the public. On Tesla's Chinese website, the company lists intelligent assisted driving as an add-on option for Tesla Model 3. It will cost customers in the country a one-time fee of 64,000 yuan ($9,410) on top of the vehicle itself.

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South Korea's stock market soars as Samsung union calls off planned strike South Korea's stock market has rallied following a last-minute deal to avert a strike that had threatened to disrupt the global supply of memory chips. Samsung Electronics and its union on Wednesday night announced a tentative agreement to settle a months-long standoff over pay, avoiding a planned 18-day walkout by some 48,000 employees. South Korea's benchmark KOSPI on Thursday soared more than 8 percent, continuing a remarkable run that has seen the index rise more than 80 percent since the start of the year. Samsung Electronics, South Korea's biggest firm by market capitalisation, jumped more than 7.5 percent. SK Hynix, the main rival of Samsung Electronics in memory chips, surged more than 11 percent.

This week, with air raid warnings wailing in the distance, Kyiv held a funeral for two sisters. They had already lost their father who had been fighting on the front line. Their grieving mother is now the family's sole survivor. This is the human cost of the largest sustained Russian aerial assault so far - with 1,500 drones and 56 missiles fired at Ukraine within 48 hours. But the loss of life could have been even higher.

Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert availability, and shifting expert distribution. We introduce the first online L2D algorithm for multiclass classification with bandit feedback and a dynamically varying pool of experts. Our method achieves regret guarantees of $O((n+n_e)T^{2/3})$ in general and $O((n+n_e)\sqrt{T})$ under a low-noise condition, where $T$ is the time horizon, $n$ is the number of labels, and $n_e$ is the number of distinct experts observed across rounds. The analysis builds on novel $\mathcal{H}$-consistency bounds for the online framework, combined with first-order methods for online convex optimization. Experiments on synthetic and real-world datasets demonstrate that our approach effectively extends standard Learning-to-Defer to settings with varying expert availability and reliability.

Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalent daily dose (LEDD) as a measure of medication needs, standard uncertainty quantification often fails to communicate the reliability of these predictions, treating high and low confidence clinical decisions identically. We introduce CASCADE (Calibrated Adaptive Scaling via Conformal And Distributional Estimation), a novel conformal prediction framework that propagates epistemic uncertainty from a screening classifier to adapt downstream predictions. Unlike standard conformal methods that rely on auxiliary residual regression, we leverage epistemic uncertainty from a primary classification task (identifying whether a medication change is needed) to dynamically scale the prediction intervals of a secondary regression task (predicting how much change). By mapping Venn-Abers multi-probabilistic uncertainty directly to non-conformity scores, our framework achieves continuous risk adaptation. We demonstrate that this ``cascade effect'' produces highly efficient intervals for confident patients (38.9% narrower than standard conformal baselines) while automatically expanding intervals to ensure robust coverage for uncertain cases, bridging the gap between discrete clinical decision-making and continuous dose forecasting in PD.

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret would not scale poorly with the number of nodes. In particular, we introduce the notion of an effective dimension, which is small in real-world graphs, and propose two algorithms for solving our problem that scale linearly in this dimension. Our experiments on real-world content recommendation problem show that a good estimator of user preferences for thousands of items can be learned from just tens nodes evaluations.

We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve small static regret against the best point satisfying all constraints while also controlling cumulative constraint violation ($\mathsf{CCV}$). For strongly convex losses, state-of-the-art algorithms achieve $O(\log T)$ regret and $O(\sqrt{T \log T})$ $\mathsf{CCV}.$ The corresponding best-known bounds for convex losses is $O(\sqrt{T})$ regret and $O(\sqrt{T} \log T)$ $\mathsf{CCV}$. In this paper, we give a simple projection-based algorithm that simultaneously achieves $O(\log T)$ regret and $O(\log T)$ $\mathsf{CCV}$ for strongly-convex losses, yielding an exponential improvement in the $\mathsf{CCV}$. For the convex losses, our algorithm improves the $\mathsf{CCV}$ to $O(\sqrt{T})$ while maintaining the optimal $O(\sqrt{T})$ regret. The key to our improvement is a recent geometric result for self-contracted curves, which may be of independent interest.

Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA under a federated learning setting, enabling collaborative fine-tuning across clients while preserving parameter efficiency. We focus on a highly heterogeneous regime in which clients share only partial structure and a substantial subset may be contaminated. We propose Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR), a contamination-aware framework that relies only on preliminary local estimators. Its formulation applies broadly, from linear regression to neural network and LLM modules, whenever local adaptation can be represented by matrix-valued updates. CLAIR recovers the shared LoRA subspace and detects contaminated clients via a structured low-rank plus block-sparse decomposition. We prove exact recovery of the shared LoRA subspace in the noiseless case, stable recovery under preliminary estimation error, and consistent collaborative-set recovery under mild separation conditions. We further quantify the gain from CLAIR refinement: it reduces off-subspace estimation error through cross-client averaging while preserving client-specific variation within the shared LoRA subspace, thus improves over local fine-tuning whenever this oracle gain outweighs the costs of subspace estimation and benign-client heterogeneity. Empirically, we demonstrate the benefits of CLAIR by fine-tuning a Transformer architecture on a text-copying task. The results show accurate contamination detection and improved benign-client performance compared with local fine-tuning and non-robust federated averaging.