118 results on '"Jingge Zhu"'
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2. Compute-Forward Multiple Access for Gaussian Fast Fading Channels.
3. GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks.
4. On the Generalization for Transfer Learning: An Information-Theoretic Analysis.
5. Compute-Forward Multiple Access for Gaussian MIMO Channels.
6. Coded Distributed Image Classification.
7. Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks.
8. Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes.
9. On the Value of Stochastic Side Information in Online Learning.
10. Hardware-Limited Non-Uniform Task-Based Quantizers.
11. CFMA for Gaussian MIMO Multiple Access Channels.
12. Learning Channel Codes from Data: Performance Guarantees in the Finite Blocklength Regime.
13. Committed Private Information Retrieval.
14. Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints.
15. Learning-Based Adaptive Control for Stochastic Linear Systems With Input Constraints.
16. Design and Analysis of Hardware-Limited Non-Uniform Task-Based Quantizers.
17. A Linear Physical-Layer Network Coding Based Multiple Access Approach.
18. A Learning-Based Approach to Approximate Coded Computation.
19. Fast Rate Generalization Error Bounds: Variations on a Theme.
20. On the Capacity-Achieving Input of Channels With Phase Quantization.
21. Capacity Bounds for One-Bit MIMO Gaussian Channels With Analog Combining.
22. On the Capacity-Achieving Input of the Gaussian Channel With Polar Quantization.
23. On the tightness of information-theoretic bounds on generalization error of learning algorithms.
24. Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge.
25. Is Phase Shift Keying Optimal for Channels with Phase-Quantized Output?
26. Short Blocklength Distribution Matching by Linear Programming.
27. Local Differential Privacy for Multi-Agent Distributed Optimal Power Flow.
28. Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful.
29. Information-theoretic analysis for transfer learning.
30. On Minimizing Symbol Error Rate Over Fading Channels With Low-Resolution Quantization.
31. On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis.
32. Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints.
33. Ancestral Colorings of Perfect Binary Trees With Applications in Private Retrieval of Merkle Proofs.
34. An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications.
35. A Bayesian approach to (online) transfer learning: Theory and algorithms.
36. Coded Control over Lossy Networks.
37. Communication Versus Computation: Duality for Multiple-Access Channels and Source Coding.
38. Compute-Forward Multiple Access (CFMA): Practical Implementations.
39. On the Capacity-Achieving Input of Channels with Phase Quantization.
40. A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms.
41. On the duality between multiple-access codes and computation codes.
42. A sequential approximation framework for coded distributed optimization.
43. Compute-forward multiple access (CFMA) with nested LDPC codes.
44. Typical sumsets of linear codes.
45. Typical sumsets of lattice points.
46. Gaussian Multiple Access via Compute-and-Forward.
47. Analysis of Nakamoto Consensus, Revisited.
48. On lattice codes for Gaussian interference channels.
49. Compute-and-forward using nested linear codes for the Gaussian MAC.
50. Gaussian (dirty) multiple access channels: A compute-and-forward perspective.
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