NDLI: High-confidence predictions under adversarial uncertainty

Please wait, while we are loading the content...

High-confidence predictions under adversarial uncertainty

Learning hurdles for sleeping experts

Restriction access

Mechanism design with approximate valuations

Quantum strategic game theory

The curse of simultaneity

No justified complaints: on fair sharing of multiple resources

From randomizing polynomials to parallel algorithms

Practical verified computation with streaming interactive proofs

Paging for multi-core shared caches

Noise vs computational intractability in dynamics

Distribution free evolvability of polynomial functions over all convex loss functions

Algorithms on evolving graphs

Towards deterministic tree code constructions

Linear time decoding of regular expander codes

List decoding subspace codes from insertions and deletions

Bounds on locally testable codes with unique tests

Approximately optimal mechanism design via differential privacy

Fairness through awareness

Dynamics of prisoner's dilemma and the evolution of cooperation on networks

Crowdsourced Bayesian auctions

Super-polynomial quantum speed-ups for boolean evaluation trees with hidden structure

Quantum interactive proofs with weak error bounds

Quantum money from knots

Quantum rejection sampling

(Leveled) fully homomorphic encryption without bootstrapping

From extractable collision resistance to succinct non-interactive arguments of knowledge, and back again

Targeted malleability: homomorphic encryption for restricted computations

Sherali-Adams relaxations and indistinguishability in counting logics

Graph densification

Spectral sparsification via random spanners

Multicommodity flows and cuts in polymatroidal networks

On the degree of univariate polynomials over the integers

On persistent homotopy, knotted complexes and the Alexander module

Compressed matrix multiplication

Gadgets and anti-gadgets leading to a complexity dichotomy

On beating the hybrid argument

Linear programming, width-1 CSPs, and robust satisfaction

Marginal hitting sets imply super-polynomial lower bounds for permanent

High-confidence predictions under adversarial uncertainty

Content Provider	ACM Digital Library
Author	Drucker, Andrew
Abstract	We study the setting in which the bits of an unknown infinite binary sequence x are revealed sequentially to an observer. We show that very limited assumptions about x allow one to make successful predictions about unseen bits of x. First, we study the problem of successfully predicting a single 0 from among the bits of x. In our model we have only one chance to make a prediction, but may do so at a time of our choosing. This model is applicable to a variety of situations in which we want to perform an action of fixed duration, and need to predict a "safe" time-interval to perform it. Letting $N_{t}$ denote the number of 1s among the first t bits of x, we say that x is "ε-weakly sparse" if liminf $(N_{t}/t)$ ≤ ε. Our main result is a randomized algorithm that, given any ε-weakly sparse sequence x, predicts a 0 of x with success probability as close as desired to 1 -- ε. Thus we can perform this task with essentially the same success probability as under the much stronger assumption that each bit of x takes the value 1 independently with probability ε. We apply this result to show how to successfully predict a bit (0 or 1) under a broad class of possible assumptions on the sequence x. The assumptions are stated in terms of the behavior of a finite automaton M reading the bits of x. We also propose and solve a variant of the well-studied "ignorant forecasting" problem. For every ε > 0, we give a randomized forecasting algorithm $S_{ε}$ that, given sequential access to a binary sequence x, makes a prediction of the form: "A p fraction of the next N bits will be 1s." (The algorithm gets to choose p, N, and the time of the prediction.) For any fixed sequence x, the forecast fraction p is accurate to within ±ε with probability 1 ™ ε.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450311151
DOI	10.1145/2090236.2090237
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-01-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Prediction Ignorant forecasting Binary sequences
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

High-confidence predictions under adversarial uncertainty

High-Confidence Predictions under Adversarial Uncertainty

High-Confidence Predictions under Adversarial Uncertainty

Sequential prediction of binary sequence with side information only

QoS routing under adversarial binary uncertainty

On prediction of individual sequences relative to a set of experts

Prediction of binary sequences using finite memory

Confidence and prediction intervals for neural network ensembles

Addressing the importance of uncertainty in climatic predictions based on statistical methods

High-confidence predictions under adversarial uncertainty

Similar Documents

High-confidence predictions under adversarial uncertainty

High-Confidence Predictions under Adversarial Uncertainty

High-Confidence Predictions under Adversarial Uncertainty

Sequential prediction of binary sequence with side information only

QoS routing under adversarial binary uncertainty

On prediction of individual sequences relative to a set of experts

Prediction of binary sequences using finite memory

Confidence and prediction intervals for neural network ensembles

Addressing the importance of uncertainty in climatic predictions based on statistical methods

High-confidence predictions under adversarial uncertainty