T-TAMER: Provably Taming Trade-offs in ML Serving | Paper Citation Record

Observation fb6343a1-80f6-490c-9329-bda5530792ee · outbound

This paper cites write newline.

T-TAMER: Provably Taming Trade-offs in ML Serving write newline

Reference 1

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Observation 23bd1092-9e57-4840-a89c-917985cd4c01 · outbound

This paper cites Submodular stochastic probing on matroids.

T-TAMER: Provably Taming Trade-offs in ML Serving Submodular stochastic probing on matroids

Reference 2

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Observation 2a11a320-c3d3-4d66-9024-cf22ae9c2d4c · outbound

This paper cites Boggart: Towards \ General-Purpose \ acceleration of retrospective video analytics.

T-TAMER: Provably Taming Trade-offs in ML Serving Boggart: Towards \ General-Purpose \ acceleration of retrospective video analytics

Reference 3

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Observation ebbe8a99-6502-41d6-8946-1045140b3ea5 · outbound

This paper cites Self-improving algorithms.

T-TAMER: Provably Taming Trade-offs in ML Serving Self-improving algorithms

Reference 4

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Observation 7ef7bb3e-cee3-422b-9550-9ba825a539f4 · outbound

This paper cites Learning to prune: Speeding up repeated computations.

T-TAMER: Provably Taming Trade-offs in ML Serving Learning to prune: Speeding up repeated computations

Reference 5

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Observation 52d788a9-f27c-4068-a607-759336df9795 · outbound

This paper cites The pandora's box problem with sequential inspections.

T-TAMER: Provably Taming Trade-offs in ML Serving The pandora's box problem with sequential inspections

Reference 7

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Observation 18463dc4-40d8-44d9-9da5-fba86c6aebea · outbound

This paper cites Ordered consumer search.

T-TAMER: Provably Taming Trade-offs in ML Serving Ordered consumer search

Reference 8

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This paper cites Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems.

T-TAMER: Provably Taming Trade-offs in ML Serving Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems

Reference 9

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Observation 08f60662-af61-4b2f-b5a7-62a12d24e677 · outbound

This paper cites Learning to branch.

T-TAMER: Provably Taming Trade-offs in ML Serving Learning to branch

Reference 10

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Observation 32864ce3-504d-408e-b615-2e16f8c6aff9 · outbound

This paper cites How much data is sufficient to learn high-performing algorithms? Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC) 2021, 2019.

T-TAMER: Provably Taming Trade-offs in ML Serving How much data is sufficient to learn high-performing algorithms? Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC) 2021, 2019

Reference 11

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Observation 1066eefd-8111-4aa0-9ff8-95067945fabc · outbound

This paper cites The design and price of information.

T-TAMER: Provably Taming Trade-offs in ML Serving The design and price of information

Reference 12

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Observation adf729e6-316c-42f8-bd8f-459d7ab8b2e4 · outbound

This paper cites Pandora’s problem with deadlines.

T-TAMER: Provably Taming Trade-offs in ML Serving Pandora’s problem with deadlines

Reference 13

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Observation eeb2b253-390a-4fa3-bb04-d92531e60ce9 · outbound

This paper cites Random search for hyper-parameter optimization.

T-TAMER: Provably Taming Trade-offs in ML Serving Random search for hyper-parameter optimization

Reference 14

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Observation 6a612459-60e2-4f73-afd4-ba8972d73cc7 · outbound

This paper cites Pandora’s problem with nonobligatory inspection: Optimal structure and a ptas.

T-TAMER: Provably Taming Trade-offs in ML Serving Pandora’s problem with nonobligatory inspection: Optimal structure and a ptas

Reference 15

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Observation f7ee7980-a8a1-46ce-8075-081ec243f483 · outbound

This paper cites Pandora's problem with nonobligatory inspection.

T-TAMER: Provably Taming Trade-offs in ML Serving Pandora's problem with nonobligatory inspection

Reference 16

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Observation f4bf8be9-2ef7-4b37-a926-9baa0d6fc74c · outbound

This paper cites Prophet inequalities with limited information.

T-TAMER: Provably Taming Trade-offs in ML Serving Prophet inequalities with limited information

Reference 17

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This paper cites Pandora's box problem with order constraints.

T-TAMER: Provably Taming Trade-offs in ML Serving Pandora's box problem with order constraints

Reference 18

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Observation 772f49d3-2b57-4246-96c1-07f1885a4086 · outbound

This paper cites Query strategies for priced information.

T-TAMER: Provably Taming Trade-offs in ML Serving Query strategies for priced information

Reference 19

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Observation db6f309a-26a6-4d7f-97fe-aa5cc3d083fe · outbound

This paper cites Hartline, David L.

T-TAMER: Provably Taming Trade-offs in ML Serving Hartline, David L

Reference 20

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This paper cites Revenue maximization for query pricing.

T-TAMER: Provably Taming Trade-offs in ML Serving Revenue maximization for query pricing

Reference 21

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Observation 962d60f9-0c82-4b4b-bd2f-a794a536a532 · outbound

This paper cites Pandora's box with correlations: Learning and approximation.

T-TAMER: Provably Taming Trade-offs in ML Serving Pandora's box with correlations: Learning and approximation

Reference 22

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Observation 9a07cf87-6b27-4a47-9491-ea1ab7df80d7 · outbound

This paper cites Approximating Pandora's Box with Correlations.

T-TAMER: Provably Taming Trade-offs in ML Serving Approximating Pandora's Box with Correlations

Reference 23

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Observation f2715cd3-318b-4df7-85da-8750894a6c30 · outbound

This paper cites Combinatorial Selection with Costly Information.

T-TAMER: Provably Taming Trade-offs in ML Serving Combinatorial Selection with Costly Information

Reference 24

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This paper cites Sequential information maximization: When is greedy near-optimal? In Conference on Learning Theory, pp.\ 338--363.

T-TAMER: Provably Taming Trade-offs in ML Serving Sequential information maximization: When is greedy near-optimal? In Conference on Learning Theory, pp.\ 338--363

Reference 25

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Observation ad56ede8-da04-4de0-a393-10818b5d791b · outbound

This paper cites Submodular surrogates for value of information.

T-TAMER: Provably Taming Trade-offs in ML Serving Submodular surrogates for value of information

Reference 26

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Observation 37c9a80b-2eff-4069-aafa-093b539127c4 · outbound

This paper cites Self-improving algorithms for convex hulls.

T-TAMER: Provably Taming Trade-offs in ML Serving Self-improving algorithms for convex hulls

Reference 27

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This paper cites an unresolved cited work.

T-TAMER: Provably Taming Trade-offs in ML Serving Unresolved cited work

Reference 28

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Observation 20a6e1ed-93a8-4c5d-bdf2-4f62a125d295 · outbound

This paper cites Prophet inequalities and posted pricing mechanisms.

T-TAMER: Provably Taming Trade-offs in ML Serving Prophet inequalities and posted pricing mechanisms

Reference 29

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Observation 75e8fa34-4c1b-47d5-800b-aa5926559696 · outbound

This paper cites Apparate: Rethinking early exits to tame latency-throughput tensions in ml serving.

T-TAMER: Provably Taming Trade-offs in ML Serving Apparate: Rethinking early exits to tame latency-throughput tensions in ml serving

Reference 30

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Observation 0495b7d8-94ba-41f0-a6fd-cb33bca179a8 · outbound

This paper cites A Unified Approach to Routing and Cascading for LLMs.

T-TAMER: Provably Taming Trade-offs in ML Serving A Unified Approach to Routing and Cascading for LLMs

Reference 31

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Observation e233cc4a-217b-4c02-8f8a-78878412ad14 · outbound

This paper cites Product ranking on online platforms.

T-TAMER: Provably Taming Trade-offs in ML Serving Product ranking on online platforms

Reference 32

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

T-TAMER: Provably Taming Trade-offs in ML Serving Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 33

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Observation 490a933a-dd96-409c-938f-2f328d7023a5 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

T-TAMER: Provably Taming Trade-offs in ML Serving Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 34

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Observation 567a3e74-b83c-4e4b-9334-beb15626cbc7 · outbound

This paper cites Competitive information design for pandora's box.

T-TAMER: Provably Taming Trade-offs in ML Serving Competitive information design for pandora's box

Reference 35

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T-TAMER: Provably Taming Trade-offs in ML Serving Whether or not to open pandora's box

Reference 36

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T-TAMER: Provably Taming Trade-offs in ML Serving Prophet inequalities with unknown distributions

Reference 37

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T-TAMER: Provably Taming Trade-offs in ML Serving Markov processes: characterization and convergence

Reference 38

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T-TAMER: Provably Taming Trade-offs in ML Serving Online stochastic matching: Beating 1 - 1/e

Reference 39

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T-TAMER: Provably Taming Trade-offs in ML Serving Pandora box problem with nonobligatory inspection: Hardness and approximation scheme

Reference 40

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T-TAMER: Provably Taming Trade-offs in ML Serving Bandit algorithms for prophet inequality and pandora's box

Reference 41

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T-TAMER: Provably Taming Trade-offs in ML Serving Online learning for min sum set cover and pandora’s box

Reference 42

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T-TAMER: Provably Taming Trade-offs in ML Serving Weitzman's Rule for Pandora's Box with Correlations

Reference 43

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T-TAMER: Provably Taming Trade-offs in ML Serving Unresolved cited work

Reference 44

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T-TAMER: Provably Taming Trade-offs in ML Serving Unresolved cited work

Reference 45

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T-TAMER: Provably Taming Trade-offs in ML Serving Asking the right questions: Model-driven optimization using probes

Reference 46

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T-TAMER: Provably Taming Trade-offs in ML Serving Deep learning, volume 1

Reference 47

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T-TAMER: Provably Taming Trade-offs in ML Serving Dynamic recursive neural network

Reference 48

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T-TAMER: Provably Taming Trade-offs in ML Serving Sorting and selection with structured costs

Reference 49

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T-TAMER: Provably Taming Trade-offs in ML Serving A stochastic probing problem with applications

Reference 50

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T-TAMER: Provably Taming Trade-offs in ML Serving Algorithms and adaptivity gaps for stochastic probing

Reference 51

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T-TAMER: Provably Taming Trade-offs in ML Serving Adaptivity gaps for stochastic probing: Submodular and xos functions

Reference 52

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This paper cites The markovian price of information.

T-TAMER: Provably Taming Trade-offs in ML Serving The markovian price of information

Reference 53

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T-TAMER: Provably Taming Trade-offs in ML Serving A pac approach to application-specific algorithm selection

Reference 54

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T-TAMER: Provably Taming Trade-offs in ML Serving Dynamic neural networks: A survey

Reference 55

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T-TAMER: Provably Taming Trade-offs in ML Serving Hyperparameter optimization: a spectral approach

Reference 56

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Observation 9d5ed59d-f9fb-4298-9bca-eeb662db9f39 · outbound

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T-TAMER: Provably Taming Trade-offs in ML Serving Deep residual learning for image recognition

Reference 57

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T-TAMER: Provably Taming Trade-offs in ML Serving Focus: Querying large video datasets with low latency and low cost

Reference 58

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T-TAMER: Provably Taming Trade-offs in ML Serving Non-stochastic best arm identification and hyperparameter optimization

Reference 59

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T-TAMER: Provably Taming Trade-offs in ML Serving Shallow-deep networks: Understanding and mitigating network overthinking

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Observation 285ea9df-5685-4a71-9f7f-bc9b4923e3b0 · outbound

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T-TAMER: Provably Taming Trade-offs in ML Serving Delegated search approximates efficient search

Reference 61

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Observation 15b8bec4-eca4-403a-afb2-60c0c9594e07 · outbound

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T-TAMER: Provably Taming Trade-offs in ML Serving Krakovski

Reference 62

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Observation 144b177f-2b6f-4f4b-85ae-2eb50442a4d0 · outbound

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T-TAMER: Provably Taming Trade-offs in ML Serving Descending price optimally coordinates search

Reference 63

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Observation ba02536e-941b-4446-bd72-420be9c9b23a · outbound

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T-TAMER: Provably Taming Trade-offs in ML Serving Efficiency through procrastination: Approximately optimal algorithm configuration with runtime guarantees

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Reference 65

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Reference 66

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Reference 67

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Reference 68

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Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Reference 76

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Reference 77

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Reference 78

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Observation dee2a638-0758-4c97-9ca1-36906f0d0756 · outbound

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Reference 79

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Reference 80

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Reference 81

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Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Observation b97de473-2a3c-4c91-a2e9-c10da7889dcb · outbound

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Reference 88

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Reference 89

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Reference 90

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Observation b6bb6e07-42b1-4c0a-a134-e8926ad70e96 · outbound

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Reference 91

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Reference 92

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Reference 93

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Reference 94

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Reference 95

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Reference 96

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T-TAMER: Provably Taming Trade-offs in ML Serving Berxit: Early exiting for bert with better fine-tuning and extension to regression

Reference 97

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Observation 54e2a028-9ce8-437c-82c9-b871889ae3d1 · outbound

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Reference 98

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T-TAMER: Provably Taming Trade-offs in ML Serving Mechanism design via correlation gap

Reference 99

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Reference 100

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Reference 101

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