Guangyu Wang (73 results)

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Hardback or Cased Book. Condition: New. Soundscape in Urban Forests. Book.

Condition: New.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New.

Taschenbuch. Condition: Neu. Neuware - What geometry does a learning system discover in data-and what does it impose through architecture, objectives, and representation How should we compare representations across models or modalities, and when do claims about manifolds, symmetry, topology, or emergent structure have mathematical content rather than visual appeal Geometric Foundations of AI (II) develops the object-side geometry of modern machine learning. It begins with group actions, representation theory, and equivariant architectures, then builds toward information bottlenecks, contrastive and self-supervised representation geometry, non-Euclidean and hyperbolic embeddings, manifold hypotheses and intrinsic dimension, representation diagnostics, alignment and transfer, topological data analysis, and the geometry of foundation-model representations. A recurring theme is that a geometric claim becomes meaningful only after the object, metric, symmetry, scale, and estimator have been specified. Written for advanced undergraduates, graduate students, and researchers in machine learning, statistics, geometry, and applied mathematics, the book combines exact mathematics with carefully delimited model-dependent and empirical claims. It is both a technical foundation for geometric and representation learning and a guide to deciding which geometric interpretations are justified by the evidence.…

Taschenbuch. Condition: Neu. Neuware - Continuous-time mathematics has become a common language across modern artificial intelligence. Yet the same words-gradient flow, diffusion, entropy, sampling, control, and equilibrium-often refer to different mathematical objects, metrics, and regimes. This book is built around making those distinctions precise. Stochastic Dynamics for AI develops deterministic and stochastic dynamics from first principles and then uses them to connect Fokker-Planck equations and Wasserstein gradient flows; diffusion and flow-based generative models; Langevin, Hamiltonian, and non-reversible sampling; particle and variational inference; stochastic-gradient dynamics and the edge of stability; entropy-regularized control and continuous-time reinforcement learning; mean-field limits and games; and path-space relative entropy and Schrödinger bridges. Rather than presenting these subjects as isolated toolkits, the book asks a harder question: when does an idea transfer from one domain to another Each major connection is separated into what is exact, what requires a limiting argument or a change of representation, and what fails outside its stated assumptions. Scope boxes, counterexamples, comparison tables, and four tiers of exercises make the hypotheses-and the boundaries of the conclusions-part of the mathematics. Designed for advanced undergraduates, graduate students, and researchers in mathematics, statistics, machine learning, and related fields, this volume offers a rigorous guide to the continuous-time structures that underlie learning, generation, sampling, optimization, control, and interacting AI systems.…

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Taschenbuch. Condition: Neu. Neuware - What changes when a learning system does more than predict-when it acts, changes what it will observe next, remembers a history, and must control a process over time Sequential Decision Making for AI develops the mathematical language for that setting. Beginning with decision theory, Markov decision processes, bandits, and dynamic programming, it builds toward reinforcement learning under exploration, function approximation, offline data, partial observability, planning, constraints, and multi-agent interaction. The unifying theme is interaction: once actions influence future data, the objects that govern learning change. The book separates familiar ideas from the assumptions that make them valid. Representability is distinguished from learnability; Bellman closure from mere function-class membership; offline sample size from policy coverage; belief-state sufficiency from minimal memory; and statistical information from computational access. Upper bounds, lower bounds, and algorithmic guarantees are tied to the observation model, interaction protocol, horizon, and resource being counted. The same framework is connected to modern foundation-model post-training and agent design: behavioural cloning, preference and reward modelling, KL-regularised optimisation, offline evaluation, context and recurrent memory, test-time planning, tool use, constrained control, and multi-agent interaction. These are interpreted through theorem-native quantities such as interaction rounds, independent samples, effective horizon, coverage, planning depth, memory bits, and computation. Written for graduate students and researchers in machine learning, statistics, applied mathematics, control, and AI, this volume is not an algorithm catalogue. It is a resource-aware guide to what sequential-learning theorems establish, what they leave open, and what additional assumptions are required before they become claims about real AI agents. …

Taschenbuch. Condition: Neu. Neuware - Uncertainty is useful only when we know exactly what it is uncertainty about-and what its guarantees actually mean. Bayesian Inference and Uncertainty Quantification for AI develops a rigorous, unified framework for reasoning about uncertainty across the modern AI pipeline. Rather than treating Bayesian posteriors, calibration, conformal prediction, and deployment risk as separate topics, the book connects them through a common question: which probability statement is being made, under what assumptions, and which parts of that statement survive approximation, distribution shift, and decision making Beginning with Bayesian models, priors, exchangeability, hierarchical structure, and posterior asymptotics, the book moves through Monte Carlo, gradient-based sampling, variational and amortised inference, proper scoring rules, calibration, conformal prediction, selective prediction, and distribution shift. It then brings these ideas into contemporary AI through function-space uncertainty, Bayesian deep learning, pretrained-model adaptation, retrieval and generation systems, multi-step agents, and scientific inverse problems with learned generative priors. Throughout, theorem statements are paired with explicit assumptions, failure modes, rate interpretations, and 'guarantee cards' that identify what is random, what is held fixed, and what is not claimed. Engineering lenses translate mathematical results into quantities that matter in practice-calibration data, sampling budgets, minibatch noise, abstention costs, and long-horizon reliability. Designed for advanced master's and early PhD students, researchers, and quantitatively trained practitioners, this volume offers a compact mathematical foundation for building, evaluating, and deploying AI systems whose uncertainty claims can be interpreted-and trusted-for the right reasons.…

Taschenbuch. Condition: Neu. Neuware - Modern AI systems are built from continuous optimization, but many of the objects they manipulate are fundamentally discrete: graphs, token sequences, matchings, cuts, codes, constraints, subsets, plans, and combinatorial search spaces. Understanding these structures is essential for knowing not only what AI systems can compute, but also where their guarantees stop. Discrete Structures and Combinatorics for AI develops the discrete mathematics that underlies contemporary machine learning and connects it directly to the design and analysis of AI systems. The book moves from counting, extremal combinatorics, and the probabilistic method to graph structure, spectral graph theory, random graphs, Boolean functions, and coding. It then develops symmetry and the Weisfeiler-Leman hierarchy, polyhedral relaxations, matroids and submodularity, factor graphs and message passing, before turning to graph neural networks, learned combinatorial solvers, algorithm discovery, multi-agent computation, tokenisation, and discrete diffusion. Throughout, classical results are separated carefully from modern, model-dependent, and open claims. The text repeatedly asks five questions of every transfer from mathematics to AI: What is the object What metric is controlled Under which quantifier In which regime For which model class This discipline clarifies distinctions that are often blurred in the literature-between representability and learnability, expressivity and generalisation, heuristic performance and certified approximation, or a discovered construction and a proof of optimality. Written for graduate students and researchers in mathematics, statistics, computer science, and machine learning, the book combines rigorous proofs with operational readings tied to real AI pipelines, including data deduplication, mixture-of-experts routing, retrieval and context selection, graph representation learning, constrained inference, learned optimisation, agent and tool orchestration, tokeniser design, and discrete generation. The result is both a graduate-level introduction to discrete mathematics for AI and a framework for reasoning precisely about the capabilities, limitations, and failure modes of learned algorithms.…

Taschenbuch. Condition: Neu. Neuware - When AI acts through a physical system, an error is no longer only an incorrect output. It becomes a force, a trajectory, a collision risk, or a decision whose consequences may arrive before a human can intervene. Safety of Embodied AI and Autonomous Systems develops the mathematical and engineering foundations needed to reason about those consequences. The book moves from hybrid safety contracts, hazard sets, physical loss, reachability, and viability to Lyapunov methods, control barrier functions, constrained action selection, stochastic reach-avoid analysis, chance constraints, and risk measures. It then extends the framework to learning-enabled systems through belief-space safety, safe exploration under uncertainty, and learned components operating off distribution. A unifying theme is the intervention margin: the time or state-space room available before a hazard becomes unavoidable, measured against sensing, computation, communication, actuation, and human-response delays. This perspective carries through runtime assurance and backup controllers, shared autonomy, fault-tolerant operation, degraded modes, and the final verification and safety-case framework. Designed for graduate students, researchers, and engineers in AI safety, robotics, control, and autonomous systems, the text combines formal results with engineering interpretation, computational labs, structured evidence, and safety-case templates-showing how control, learning, verification, and runtime assurance must fit together when AI decisions have physical consequences.…

Taschenbuch. Condition: Neu. Neuware - AI systems will fail. The central engineering question is what remains controllable once they do. Resilience of AI Systems develops a rigorous framework for reasoning about AI after failure: when an incident first becomes detectable, how long detection takes, how containment limits the blast radius, what can and cannot be rolled back, how degraded operation should be structured, and what evidence is required before unrestricted operation can safely resume. Rather than treating recovery as a single metric, the book decomposes incident damage into distinct stages and shows which parts are determined by instrumentation, which are affected by response policy, and which remain outside the reach of any post-incident intervention. Across detection theory, graph-based containment, hysteresis and review queues, degraded-safe modes, rollback and compensation, redundancy, belief-based re-entry, incident reconstruction, and assurance, the text connects mathematical structure to concrete operational decisions. A running deployment case, explicit provenance labels, boundary statements, CPU-only computational labs, and proof-oriented exercises make the book suitable for graduate study as well as technical design and review. For students, researchers, safety engineers, and system architects, this volume provides a unified way to ask not merely whether an AI system can recover, but what recovery can actually guarantee. …

Taschenbuch. Condition: Neu. Neuware - How can humans reliably oversee AI systems whose outputs, reasoning, or capabilities extend beyond what a human evaluator can directly verify Scalable oversight is often presented as a catalogue of protocols-debate, critique, decomposition, weak supervision, panels, and recursive assistance. This book takes a different approach: it asks what an oversight procedure can actually justify. Scalable Oversight of AI Systems develops a mathematical framework for analysing verification asymmetry, discriminating power, robust soundness, conditional completeness, capability gaps, distribution shift, and the limits of recursive supervision. It studies when decomposition preserves a defect, when process supervision helps or hurts, when weak supervision learns the wrong proxy, when many judges fail to add independent evidence, and how strategic assistance can reshape the distribution being evaluated. The final chapters turn these results into safety-assurance objects: a claim must state its threat class, evaluation law, completeness class, review budget, premises, residual uncertainty, and expiry conditions. Throughout, formal guarantees are kept separate from measurements, heuristic models, engineering assumptions, and empirical findings. Designed for advanced undergraduate and graduate courses, the volume combines rigorous derivations with worked system cases, CPU-only computational labs, structured exercises, research bridges, provenance tags, and reusable assurance templates. The result is a technical guide to a central question in advanced AI safety: not merely how to supervise systems beyond human expertise, but how to know what that supervision is entitled to claim. …

Taschenbuch. Condition: Neu. Neuware - What changes when a trained model leaves the training cluster and becomes a deployed AI system The optimization problem changes with it. Threat models, serving budgets, evaluation protocols, users, tools, and feedback loops now shape what can be optimized-and what must be guaranteed, measured, compressed, routed, or constrained. This graduate-level text develops a mathematical account of those deployment-induced decisions. It begins with robust value functions, minimax structure, adversarial training dynamics, distributionally robust optimization, robust generalization, and certification. It then turns to the system-level problems that dominate modern foundation-model deployment: curvature-aware compression and rate-distortion tradeoffs; evaluation as a problem of estimands, information, and adversarial search; threat models and reachability for language models and agents; and inference-time optimization through sampling, verification, speculative decoding, routing, cascades, and cache decisions. The final chapters study deployment games in which resources can improve quality while also expanding adversarial exposure, responsive environments in which users change the data distribution, and lifecycle questions involving continual learning, machine unlearning, privacy, and feedback into the next training run. Rather than serving as an adversarial-ML catalog or an LLM serving handbook, Deployment-Time Optimization for AI Systems focuses on durable mathematical objects: value functions, feasible sets, rate-distortion frontiers, estimands, reachability sets, and shadow prices. It is designed for graduate students, researchers, and engineers who want to reason rigorously about how foundation models become deployed systems under statistical, adversarial, computational, economic, and lifecycle constraints.…