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Timothy Brown

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11 papers
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11

NeurIPS Conference 2001 Conference Paper

Switch Packet Arbitration via Queue-Learning

  • Timothy Brown

In packet switches, packets queue at switch inputs and contend for out- puts. The contention arbitration policy directly affects switch perfor- mance. The best policy depends on the current state of the switch and current traffic patterns. This problem is hard because the state space, possible transitions, and set of actions all grow exponentially with the size of the switch. We present a reinforcement learning formulation of the problem that decomposes the value function into many small inde- pendent value functions and enables an efficient action selection.

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NeurIPS Conference 2000 Conference Paper

Direct Classification with Indirect Data

  • Timothy Brown

We classify an input space according to the outputs of a real-valued function. The function is not given, but rather examples of the function. We contribute a consistent classifier that avoids the un(cid: 173) necessary complexity of estimating the function.

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NeurIPS Conference 1999 Conference Paper

Low Power Wireless Communication via Reinforcement Learning

  • Timothy Brown

This paper examines the application of reinforcement learning to a wire(cid: 173) less communication problem. The problem requires that channel util(cid: 173) ity be maximized while simultaneously minimizing battery usage. We present a solution to this multi-criteria problem that is able to signifi(cid: 173) cantly reduce power consumption. The solution uses a variable discount factor to capture the effects of battery usage.

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NeurIPS Conference 1998 Conference Paper

Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning

  • Timothy Brown
  • Hui Tong
  • Satinder Singh

This paper examines the application of reinforcement learning to a telecommunications networking problem. The problem requires that rev(cid: 173) enue be maximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives.

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NeurIPS Conference 1996 Conference Paper

Adaptive Access Control Applied to Ethernet Data

  • Timothy Brown

This paper presents a method that decides which combinations of traffic can be accepted on a packet data link, so that quality of service (QoS) constraints can be met. The method uses samples of QoS results at dif(cid: 173) ferent load conditions to build a neural network decision function. Pre(cid: 173) vious similar approaches to the problem have a significant bias. This bias is likely to occur in any real system and results in accepting loads that miss QoS targets by orders of magnitude. Preprocessing the data to either remove the bias or provide a confidence level, the method was applied to sources based on difficult-to-analyze ethernet data traces. With this data, the method produces an accurate access control function that dramatically outperforms analytic alternatives. Interestingly, the results depend on throwing away more than 99% of the data.

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