arXiv:2602.06773v1 Announce Type: cross Abstract: Multicalibration gradient boosting has recently emerged as a scalable method that empirically produces approximately multicalibrated predictors and has been deployed at web scale. Despite this empirical success, its convergence properties are not well understood. In this paper, we bridge the gap by providing convergence guarantees for multicalibration gradient boosting in regression with squared-error loss. We show that the magnitude of successive prediction updates decays at $O(1/sqrt{T})$, which implies the same convergence rate bound […]

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Mumbai, India, Jan 5, 2026: India’s digital transformation journey has moved decisively from vision to execution. As enterprises and government bodies race to modernize operations, adopt AI at scale, and secure increasingly complex digital ecosystems, the need for real-world strategies, peer learning, and trusted platforms has never been greater. Answering this moment, Exito Media Concepts presents the 43rd Edition of the Digital Transformation Summit India 2026, a flagship, in-person gathering bringing together India’s most influential CIOs, CISOs, and […]

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We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transition matrix is obtained from which the optimal decision policy is formed. The classical maximum likelihood point estimate of the transition model does not reflect the uncertainty in the estimate of the transition model and the resulting policies may consequently lack a sufficient degree of exploration. We consider a Bayesian alternative that maintains a […]

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arXiv:2509.22459v2 Announce Type: replace Abstract: While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require many steps of iterative generation. Recent distillation methods address this by training efficient one-step generators under the guidance of a pre-trained teacher model. However, these methods are often constrained to only one specific framework, e.g., only to diffusion or only to flow models. Furthermore, these methods are naturally data-free, and to benefit from the […]

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In this interview, we speak with two representatives from Quantori, a data science and digital transformation company specializing in research and development in the biopharma industry. Yuriy Gankin is a co-founder and chief scientific officer at Quantori. He’s a life science researcher, serial entrepreneur, and inventor and holds a Ph.D. in analytical chemistry from Tufts University and an MBA from MIT Sloan. Maksim Kazanskii is Quantori’s engineering manager in machine learning. With a background in theoretical physics and […]

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Adapting large pretrained models to new tasks efficiently and continually is crucial for real-world deployment but remains challenging due to catastrophic forgetting and the high cost of retraining. While parameter-efficient tuning methods like low rank adaptation (LoRA) reduce computational demands, they lack mechanisms for strict continual learning and knowledge integration, without relying on data replay, or multiple adapters. We propose Share, a novel approach to parameter efficient continual finetuning that learns and dynamically updates a single, shared low-rank […]

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arXiv:2601.04423v1 Announce Type: cross Abstract: A Multinomial Logit (MNL) model is composed of a finite universe of items $[n]={1,…, n}$, each assigned a positive weight. A query specifies an admissible subset — called a slate — and the model chooses one item from that slate with probability proportional to its weight. This query model is also known as the Plackett-Luce model or conditional sampling oracle in the literature. Although MNLs have been studied extensively, a basic computational question […]

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arXiv:2603.05510v1 Announce Type: new Abstract: Developing and deploying AI applications in organizations is challenging when human decision authority and oversight are underspecified across the system lifecycle. Although Human-in-the-Loop (HITL) and Human-Centered AI (HCAI) principles are widely acknowledged, operational guidance for structuring roles, checkpoints, and feedback mechanisms remains fragmented. We report a multi-source qualitative study: a retrospective diary study of a customer-support chatbot and semi-structured interviews with eight AI experts from academia and industry. Through five-cycle thematic analysis of […]

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arXiv:2601.18938v1 Announce Type: cross Abstract: Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In […]

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