CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models

Recommendations for economic evaluations of cell and gene therapies: a systematic literature review with critical appraisal

Objective: No consensus exists on the ideal methodology to evaluate the economic impact and value of new, potentially curative gene therapies. We aimed to identify and describe published methodologic recommendations for the economic evaluation of gene therapies and assess whether these recommendations have been applied in published evaluations. Methods: This study was conducted in three stages: a systematic literature review of methodologic recommendations for economic evaluation of gene therapies; an assessment of the appropriateness of recommendations; and a review to assess the degree to which the recommendations were applied in published evaluations. Results: A total of 2,888 references were screened, 83 articles were reviewed to assess eligibility, and 20 papers were included. Fifty recommendations were identified, and 21 reached consensus thresholds. Most evaluations were based on naive treatment comparisons and did not apply consensus recommendations. Innovative payment mechanisms for gene therapies were rarely considered. The only widely applied recommendations related to modeling choices and methods. Conclusions: Methodological recommendations for economic evaluations of gene therapies are generally not being followed. Assessing the applicability and impact of the recommendations from this study may facilitate the implementation of consensus recommendations in future evaluations

Secretary and Online Matching Problems with Machine Learned Advice

The classical analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. Often this is not an issue with machine learning approaches, which shine in exploiting patterns in past inputs in order to predict the future. However, such predictions, although usually accurate, can be arbitrarily poor. Inspired by a recent line of work, we augment three well-known online settings with machine learned predictions about the future, and develop algorithms that take them into account. In particular, we study the following online selection problems: (i) the classical secretary problem, (ii) online bipartite matching and (iii) the graphic matroid secretary problem. Our algorithms still come with a worst-case performance guarantee in the case that predictions are subpar while obtaining an improved competitive ratio (over the best-known classical online algorithm for each problem) when the predictions are sufficiently accurate. For each algorithm, we establish a trade-off between the competitive ratios obtained in the two respective cases

Secretary and Online Matching Problems with Machine Learned Advice

The classical analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. Often this is not an issue with machine learning approaches, which shine in exploiting patterns in past inputs in order to predict the future. However, such predictions, although usually accurate, can be arbitrarily poor. Inspired by a recent line of work, we augment three well-known online settings with machine learned predictions about the future, and develop algorithms that take them into account. In particular, we study the following online selection problems: (i) the classical secretary problem, (ii) online bipartite matching and (iii) the graphic matroid secretary problem. Our algorithms still come with a worst-case performance guarantee in the case that predictions are subpar while obtaining an improved competitive ratio (over the best-known classical online algorithm for each problem) when the predictions are sufficiently accurate. For each algorithm, we establish a trade-off between the competitive ratios obtained in the two respective cases

On the Structure of Learnability Beyond P/Poly

Motivated by the goal of showing stronger structural results about the complexity of learning, we study the learnability of strong concept classes beyond P/poly, such as PSPACE/poly and EXP/poly. We show the following: 1) (Unconditional Lower Bounds for Learning) Building on [Adam R. Klivans et al., 2013], we prove unconditionally that BPE/poly cannot be weakly learned in polynomial time over the uniform distribution, even with membership and equivalence queries. 2) (Robustness of Learning) For the concept classes EXP/poly and PSPACE/poly, we show unconditionally that worst-case and average-case learning are equivalent, that PAC-learnability and learnability over the uniform distribution are equivalent, and that membership queries do not help in either case. 3) (Reducing Succinct Search to Decision for Learning) For the decision problems R_{Kt} and R_{KS} capturing the complexity of learning EXP/poly and PSPACE/poly respectively, we show a succinct search to decision reduction: for each of these problems, the problem is in BPP iff there is a probabilistic polynomial-time algorithm computing circuits encoding proofs for positive instances of the problem. This is shown via a more general result giving succinct search to decision results for PSPACE, EXP and NEXP, which might be of independent interest. 4) (Implausibility of Oblivious Strongly Black-Box Reductions showing NP-hardness of learning NP/poly) We define a natural notion of hardness of learning with respect to oblivious strongly black-box reductions. We show that learning PSPACE/poly is PSPACE-hard with respect to oblivious strongly black-box reductions. On the other hand, if learning NP/poly is NP-hard with respect to oblivious strongly black-box reductions, the Polynomial Hierarchy collapses

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Black-box use of One-way Functions is Useless for Optimal Fair Coin-Tossing

A two-party fair coin-tossing protocol guarantees output delivery to the honest party even when the other party aborts during the protocol execution. Cleve (STOC--1986) demonstrated that a computationally bounded fail-stop adversary could alter the output distribution of the honest party by (roughly) $1/r$ (in the statistical distance) in an $r$-message coin-tossing protocol. An optimal fair coin-tossing protocol ensures that no adversary can alter the output distribution beyond $1/r$. In a seminal result, Moran, Naor, and Segev (TCC--2009) constructed the first optimal fair coin-tossing protocol using (unfair) oblivious transfer protocols. Whether the existence of oblivious transfer protocols is a necessary hardness of computation assumption for optimal fair coin-tossing remains among the most fundamental open problems in theoretical cryptography. The results of Impagliazzo and Luby (FOCS–1989) and Cleve and Impagliazzo (1993) prove that optimal fair coin-tossing implies the necessity of one-way functions\u27 existence; a significantly weaker hardness of computation assumption compared to the existence of secure oblivious transfer protocols. However, the sufficiency of the existence of one-way functions is not known. Towards this research endeavor, our work proves a black-box separation of optimal fair coin-tossing from the existence of one-way functions. That is, the black-box use of one-way functions cannot enable optimal fair coin-tossing. Following the standard Impagliazzo and Rudich (STOC--1989) approach of proving black-box separations, our work considers any $r$-message fair coin-tossing protocol in the random oracle model where the parties have unbounded computational power. We demonstrate a fail-stop attack strategy for one of the parties to alter the honest party\u27s output distribution by $1/\sqrt r$ by making polynomially-many additional queries to the random oracle. As a consequence, our result proves that the $r$-message coin-tossing protocol of Blum (COMPCON--1982) and Cleve (STOC--1986), which uses one-way functions in a black-box manner, is the best possible protocol because an adversary cannot change the honest party\u27s output distribution by more than $1/\sqrt r$. Several previous works, for example, Dachman--Soled, Lindell, Mahmoody, and Malkin (TCC--2011), Haitner, Omri, and Zarosim (TCC--2013), and Dachman--Soled, Mahmoody, and Malkin (TCC--2014), made partial progress on proving this black-box separation assuming some restrictions on the coin-tossing protocol. Our work diverges significantly from these previous approaches to prove this black-box separation in its full generality. The starting point is the recently introduced potential-based inductive proof techniques for demonstrating large gaps in martingales in the information-theoretic plain model. Our technical contribution lies in identifying a global invariant of communication protocols in the random oracle model that enables the extension of this technique to the random oracle model