19,259 research outputs found
Unconditional Lower Bounds against Advice
We show several unconditional lower bounds for exponential time classes against polynomial time classes with advice, including: 1. For any constant c, NEXP ̸ ⊆ P NP[nc
Consistency of circuit lower bounds with bounded theories
Proving that there are problems in that require
boolean circuits of super-linear size is a major frontier in complexity theory.
While such lower bounds are known for larger complexity classes, existing
results only show that the corresponding problems are hard on infinitely many
input lengths. For instance, proving almost-everywhere circuit lower bounds is
open even for problems in . Giving the notorious difficulty of
proving lower bounds that hold for all large input lengths, we ask the
following question: Can we show that a large set of techniques cannot prove
that is easy infinitely often? Motivated by this and related
questions about the interaction between mathematical proofs and computations,
we investigate circuit complexity from the perspective of logic.
Among other results, we prove that for any parameter it is
consistent with theory that computational class , where is one of
the pairs: and , and , and
. In other words, these theories cannot establish
infinitely often circuit upper bounds for the corresponding problems. This is
of interest because the weaker theory already formalizes
sophisticated arguments, such as a proof of the PCP Theorem. These consistency
statements are unconditional and improve on earlier theorems of [KO17] and
[BM18] on the consistency of lower bounds with
Learning and Visceral Temptation in Dynamic Savings Experiments
In models of optimal savings with income uncertainty and habit formation, people
should save early to create a buffer stock, to cushion bad income draws and limit
the negative internality from habit formation. In experiments in this setting,
people save too little initially, but learn to save optimally within four repeated
lifecycles, or 1-2 lifecycles with “social learning.” Using beverage rewards (cola)
to create visceral temptation, thirsty subjects who consume immediately
overspend compared to subjects who only drink after time delay. The relative
overspending of immediate-consumption subjects is consistent with hyperbolic
discounting and dual-self models. Estimates of the present-bias choices are
β=0.6-0.7, which are consistent with other studies (albeit over different time
horizons)
Limits to Non-Malleability
There have been many successes in constructing explicit non-malleable codes for various classes of tampering functions in recent years, and strong existential results are also known. In this work we ask the following question:
When can we rule out the existence of a non-malleable code for a tampering class ??
First, we start with some classes where positive results are well-known, and show that when these classes are extended in a natural way, non-malleable codes are no longer possible. Specifically, we show that no non-malleable codes exist for any of the following tampering classes:
- Functions that change d/2 symbols, where d is the distance of the code;
- Functions where each input symbol affects only a single output symbol;
- Functions where each of the n output bits is a function of n-log n input bits.
Furthermore, we rule out constructions of non-malleable codes for certain classes ? via reductions to the assumption that a distributional problem is hard for ?, that make black-box use of the tampering functions in the proof. In particular, this yields concrete obstacles for the construction of efficient codes for NC, even assuming average-case variants of P ? NC
A Second-order Bound with Excess Losses
We study online aggregation of the predictions of experts, and first show new
second-order regret bounds in the standard setting, which are obtained via a
version of the Prod algorithm (and also a version of the polynomially weighted
average algorithm) with multiple learning rates. These bounds are in terms of
excess losses, the differences between the instantaneous losses suffered by the
algorithm and the ones of a given expert. We then demonstrate the interest of
these bounds in the context of experts that report their confidences as a
number in the interval [0,1] using a generic reduction to the standard setting.
We conclude by two other applications in the standard setting, which improve
the known bounds in case of small excess losses and show a bounded regret
against i.i.d. sequences of losses
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