Let V be the vector space of functions from X to R whose moment generating function is bounded in a neighborhood of the origin. Given a function class F⊂V, and i.i.d. samples X1,…,Xn of a random variable X∈X such that Ef(X)=0 for all
f∈F, a fundamental problem in the theory of empirical processes is finding tail bounds for
the empirical average Enf(X)def=n−1∑ni=1f(Xi) that hold simultaneously for all f∈F. Such tail bounds are predominantly
expressed as the sum of a term depending on some notion of “complexity” of the function class (Rademacher
complexity, V.C. dimension, etc), and a term that depends on the worst-case deviations of the random
variables f(X) as f varies in the function class.
For any single function f∈V, the Chernoff bound provides a concentration inequality that depends on the deviations of the considered function f as captured by the corresponding cumulant-generating function. Howerver, the tail bounds that hold uniformly for all functions of a function class, say F, are commonly expressed in terms of the worst-case deviations across F. It is therefore natural to ask, if we can derive tail bounds for Enf that hold for every function in F, but consider instance-dependent deviations rather than the worst-case deviations.
In the case of Gaussian random variables and the class of linear functions defined by inner products with
unit vectors, Lugosi and Mendelson [LM20] derived such instance-dependent tail bounds, as a benchmark for their main goal of designing a direction-dependent robust mean estimator. They showed that it can provide significantly sharper inequalities compared to the conventional tail bounds.
In [Bah22], we showed how to derive instance-dependent tail bounds in a much broader
setting described as follows. For r≥0, we define the functionals Tr:V→R≥0 as
Tr(f)def=inf and show that \overline{T}_r(f) = \max\{T_r(f), T_r(-f)\}\,\nonumber defines a seminorm over \mathbb{V}. Furthermore, let A be a
map from
\mathcal{F} to at most e^k functions in \mathcal{F}, such that A[0]=0 and T_r(A[f]) \le T_r(f)\nonumber\,, for every f\in \mathcal{F}. Define the set
\mathcal{A} = \lbrace f - A[f]\colon\, f\in \mathcal{F}\rbrace\,.\nonumber
In [Bah22] we define a certain generalization of Talagrand's (truncated)
\gamma_\alpha functionals as
\gamma(\mathcal A;r, \underline{\ell}) = \inf_{(\mathcal A_i)_{i\ge 0}}\sup_{a\in \mathcal A} \sum_{\ell \ge \underline{\ell}} \overline{T}_{r + (r+1/n)2^{\ell-\underline{\ell}}}(a - \mathcal A_\ell)\,,
where the infimum is taken over an increasing admissible sequence of subsets of \mathcal A and \overline{T}_s(a -\mathcal A_\ell) denotes the distance of a\in \mathcal A from \mathcal A_\ell as measured by \overline{T}_s(\cdot). We show that with probability at least
1-2e^{-nr}, for every f\in \mathcal{F} we have
\mathbb{E}_n f(X) - \overline{T}_{r+k/n}(f)\le 2\gamma(\mathcal{A};\, r, \lfloor \log_2(nr/5) \rfloor) +\mathrm{rad}_{2r+1/n}(\mathcal{A})\,, \nonumber
where \mathrm{rad}_s\left(\mathcal{A}\right) is the radius of \mathcal{A} as measured by \overline{T}_s(\cdot).
Estimation of mean of a random variable is a fundamental problem of statistics since many estimation
problems such as density estimation, regression, \ldots, can be posed as mean estimation. The most
benign and pedagogical example one can imagine is perhaps estimation of the mean of a random Gaussian vector
in \mathbb{R}^d, from a finite number of i.i.d. samples. The sample mean, the average of the
observed samples, turns out to achieve the best possible confidence interval in the Gaussian setting.
Unfortunately, the sample mean loses its glory as soon as we start dealing with heavy-tailed distributions
or adversarially manipulated samples. In such unfavorable scenarios, the fundamental questions are then,
“How close can we get to the ideal confidence intervals?, With what kind of mean estimators?,
and Whether such estimators are computationally tractable?” There has been great strides in
this line of research recently; we highly recommend the great survey papers [LM19] and
[DK19] for more details on these recent advances as well as the historical background.
In [Bah20], we propose a competing mean estimator (in a separable Banach space) based
on empirical characteristic function. For the sake of simpler exposition, we explain the ideas in the
usual Euclidean setting. Given i.i.d. copies X_1,\dotsc,X_n of a random vector X in
\mathbb{R}^d, we want to estimate the mean of X denoted, by \mu_\star with error measured in the
\ell_2-norm. The only restriction on the law of X is that the corresponding covariance
matrix, \varSigma_\star, exists and is bounded. Furthermore, we may face strong contamination
of the samples, where for some \eta\in(0,1/2), an adversary may arbitrarily manipulate up to \eta
n samples.
With the empirical characteristic function denoted by
\varphi_n(w) = \frac{1}{n}\sum_{i=1}^n \exp(\langle w, X_i\rangle)\nonumber\,,
our proposed estimator is
\widehat{\mu} \in \operatorname*{argmin}_\mu \max_{w\,:\,{\Vert w\Vert}\le r_n} \langle w, \mu\rangle -
\mathrm{Im}(\varphi_n(w))\,,\nonumber
for appropriately chosen radius r_n>0. In particular, in the non-adversarial setting, we show that by
choosing r_n = \frac{22\log(1/\delta)}{n\epsilon}, with probability at least 1-\delta the estimator
achieves the bound \|\hat{\mu}-\mu_\star\|\le \epsilon, if the prescribed \epsilon obeys
\begin{align*} \epsilon & \ge
\max\Big\{96\sqrt{\frac{\mathrm{tr}(\varSigma_\star)}{n}}+12\sqrt{\frac{\|\varSigma_\star\|_{\mathrm{op}}\log(1/\delta)}{n}},\\
& \hphantom{\max\Big\{
}\quad9{\left(\frac{\log(1/\delta)}{n}\right)}^{2/3}\|\mu_\star\|\Big\}\,.\end{align*} We also prove a
similar statement in the adversarial setting. Basically, the effect of contamination in terms of the
contamination parameter \eta\in(0,1/2) is an additional term of order
\sqrt{\eta\|\varSigma_\star\|_{\mathrm{op}}} that appears in the confidence interval. Both of the
derived confidence intervals have an undesirable dependence on \|\mu_\star\| that, even though vanishing
at a rate n^{-2/3}, prevents achieving the ideal purely sub-Gaussian rate. In [Bah20] we discuss ways to diminish this nuisance. Furthermore, we discuss how the
estimator can be made oblivious to the accuracy level \epsilon. The main drawback of the estimator is
that, superficially, it does not appear to be computationally tractable.
Consider the trajectory (\boldsymbol{u}_0, \boldsymbol{x}_0=\boldsymbol{0}),(\boldsymbol{u}_1,
\boldsymbol{x}_1),\dotsc,(\boldsymbol{u}_T, \boldsymbol{x}_T)\in\mathbb{R}^n\times\mathbb{R}^p of
input-state pairs generated
by the recursion
\begin{align}
\boldsymbol{x}_{t}=\nabla
f(\boldsymbol{A}_\star\boldsymbol{x}_{t-1}+\boldsymbol{B}_\star\boldsymbol{u}_{t-1})\,,\label{eq:RNN}
\end{align}
where \nabla f is the gradient of a given differentiable and convex function f, and
\boldsymbol{A}_\star\in\mathbb{R}^{n\times n}, \boldsymbol{B}_\star\in\mathbb{R}^{n\times p} are model
parametrs
we want to estimate. This observation model generalizes some common recurrent neural net (RNN) models.
In particular,
an RNN with Rectified Linear Unit (ReLU) activations can be expressed in the above form by choosing
\begin{align*}
f(\boldsymbol{x})\overset{\mathrm{\scriptscriptstyle def}}{=} \frac{1}{2}\sum_{i=1}^n
{\left(\max\left\{x_i,0\right\}\right)}^2\,.
\end{align*}
Let \beta>0 be a sufficiently large normalizing parameter. We can express the recursion \eqref{eq:RNN}
equivalently in
terms of
\begin{align*}
\boldsymbol{C}_\star=\begin{bmatrix} \boldsymbol{A}_\star & \beta^{-1}\boldsymbol{B}_\star\end{bmatrix}
\end{align*}
and
\begin{align*}
\boldsymbol{z}_t=\begin{bmatrix}\boldsymbol{x}_t\\ \beta\boldsymbol{u}_t\end{bmatrix}\,.
\end{align*}
Namely, for t\ge 1 we have the recursion
\begin{align*}
\boldsymbol{z}_t=\begin{bmatrix}\nabla f(\boldsymbol{C}_\star\boldsymbol{z}_{t-1})\\
\beta \boldsymbol{u}_t \end{bmatrix}\,,
\end{align*}
and at t=0 we have
\begin{align*}
\boldsymbol{z}_0=\begin{bmatrix} \boldsymbol{0}\\
\beta \boldsymbol{u}_0 \end{bmatrix}\,.
\end{align*}
It follows from the convexity of f and \eqref{eq:RNN} that
\begin{align*}
f(\boldsymbol{C}\boldsymbol{z}_{t-1})-\langle\boldsymbol{x}_t, \boldsymbol{C}\boldsymbol{z}_{t-1}\rangle \ge
f(\boldsymbol{C}_\star\boldsymbol{z}_{t-1})-\langle\boldsymbol{x}_t, \boldsymbol{C}_\star\boldsymbol{z}_{t-1}
\rangle\,.
\end{align*}
Therefore, we can formulate a convex program as an estimator for \boldsymbol{C}_\star:
\begin{align*}
\widehat{\boldsymbol{C}} = \operatorname*{argmin}_{\boldsymbol{C}}\ \sum_{t=1}^T
f(\boldsymbol{C}\boldsymbol{z}_{t-1})-\langle\boldsymbol{x}_t, \boldsymbol{C}\boldsymbol{z}_{t-1}\rangle\,.
\end{align*}
In [BR19], under some additional assumptions on the nonlinearity and the distribution of
the input, we showed that,
with high probability, a time horizon T that scales with n+p, up to logarithmic factors, suffices for
exact recovery
of \boldsymbol{C}_\star from a single trajectory.
We provide a computationally tractable method to address a large class of parametric regression problems that
involve difference of convex (DC) observation functions. This class of regression problems
are much broader than those with the ``convex observation'' model considered in Part (I) because many
non-convex functions can be written in DC form as well.
In the considered observation model, we measure \boldsymbol{x}_\star\in\mathbb{R}^N indirectly as
\begin{align*}
y_m & = f_m(\boldsymbol{x}_\star) - g_m(\boldsymbol{x}_\star) + \xi_m\,, & m=1,\dotsc,M\,,
\end{align*}
where the pairs of functions (f_m,\,g_m) are i.i.d. copies of a pair of random convex functions
(f,\,g), and \xi_m
denotes the noise as in Part (I). Assuming that we have access to
\begin{align}
\boldsymbol{a}_0 & \approx \frac{1}{2M}\sum_{m=1}^M\nabla f_m(\boldsymbol{x}_\star) + \nabla
g_m(\boldsymbol{x}_\star)\,,\label{a0-approx}
\end{align}
where the approximation has a certain precise form, then we formulate the estimator
\widehat{\boldsymbol{x}} of \boldsymbol{x}_\star as
\begin{align*}
\widehat{\boldsymbol{x}} & = \operatorname*{argmax}_{\boldsymbol{x}}\ \langle\boldsymbol{a}_0,
\boldsymbol{x}\rangle - \frac{1}{M}\sum_{m=1}^M \max \left\lbrace f_m(\boldsymbol{x}) - y_m,
g_m(\boldsymbol{x}) \right\rbrace\,.
\end{align*}
Under mild non-degeneracy and regularity assumptions, we established in [Bah18]
a sample complexity for guaranteed accuracy of \widehat{\boldsymbol{x}} using a PAC-Bayesian analysis. To
provide a concrete example we also studied bilinear regression with standard Gaussian factors as a
special case; for this problem we also described a method to create a vector \boldsymbol{a}_0 that
provably satisfies the required approximation \eqref{a0-approx}.
Consider the problem of estimating an N-dimensional signal \boldsymbol{x}_\star from
observations of the form
\begin{align}
y_m & = f_m(\boldsymbol{x}_\star) + \xi_m\,, & m=1,2,\dotsc,M\,,
\label{cvxreg}
\end{align}
where the functions f_m are i.i.d. copies of a randomly drawn convex function f, and the noise terms
are represented by \xi_m. Despite the convexity assumption on the functions f_m, the observation
model \eqref{cvxreg} is quite general and includes many standard statistical models as the special case
including generalized linear models and single hidden layer neural nets. I addressed the
general regression problem \eqref{cvxreg} in [BR17b] building upon ideas I developed in
[BR17a] to address a special case often known as phase retrieval. It is more
illustrative to begin my explanation with this special case as well.
A new approach to phase retrieval
The problem of phase retrieval appears in areas such as imaging and optics where the sensors often measure
only intensities; the sign- (or phase-) information is generally lost. The observation model in the phase
retrieval problem, assuming no noise in the measurements, can be abstracted as the system of quadratic
equations
\begin{equation*}
\begin{aligned}
y_1 & = |\boldsymbol{a}_1^*\boldsymbol{x}_\star|^2\\
y_2 & = |\boldsymbol{a}_2^*\boldsymbol{x}_\star|^2\\
\vdots & \qquad\vdots\\
y_M & = |\boldsymbol{a}_M^*\boldsymbol{x}_\star|^2\,,
\end{aligned}
\end{equation*}
for \boldsymbol{x}_\star\in\mathbb{C}^N. Clearly, this model corresponds to \eqref{cvxreg} with
f_m(\boldsymbol{x}) = |\boldsymbol{a}_m^*\boldsymbol{x}|^2 for i.i.d. draws of \boldsymbol{a}_m, and
\xi_m=0. In [BR17a], I formulated a new estimator for this problem as the convex
program
\begin{equation}
\begin{aligned}
\operatorname*{argmax}_{\boldsymbol{x}}\ & \mathrm{Re}(\boldsymbol{a}_0^*\boldsymbol{x}) & \\
\text{subject to}\ & |\boldsymbol{a}_m^*\boldsymbol{x}|^2 \le y_m, & m=1,2,\dotsc,M\,,
\end{aligned} \label{linmax}
\end{equation}
where \boldsymbol{a}_0 denotes an “anchor vector” that obeys
\begin{equation*}
|\boldsymbol{a}_0^*\boldsymbol{x}_\star| \ge \delta \left\lVert\boldsymbol{a}_0\right\rVert_2
\left\lVert\boldsymbol{x}_\star\right\rVert_2\,,
\end{equation*}
for some absolute constant \delta \in (0,1]. The anchor vector can be constructed from random
observations similar to initializations in some non-convex methods (e.g., Wirtinger Flow); the details can be found in the paper.
Here, I explain the geometric intuition behind \eqref{linmax}
in the case of real-valued variables for clarity. As illustrated in Figure 1, each of
the constraints in \eqref{linmax} form a slab of feasible points, whose intersection is a convex polytope
\mathcal{K}. Clearly, \boldsymbol{x}_\star is an extreme point of \mathcal{K}. The solution to
\eqref{linmax} is also always an extreme point of \mathcal{K}, since it is a solution to linear
maximization over the convex body \mathcal{K}. The key observation is that if the anchor vector
\boldsymbol{a}_0 has a non-trivial component in the direction of \boldsymbol{x}_\star, we can expect
that the extreme point found by \eqref{linmax} coincides with \boldsymbol{x}_\star.
Figure 1. Geometry of slabs intersecting at \boldsymbol{x}_\star and their
positioning with respect to the anchor \boldsymbol{a}_0
Using classic results from statistical learning theory, I showed that with high probability
\begin{equation*}
M = C_\delta N
\end{equation*}
independent random measurements suffice to recover \boldsymbol{x}_\star
using \eqref{linmax}, with C_\delta being an absolute constant depending only on \delta. Robustness
under specific noise models is also addressed in the paper.
Why does \eqref{linmax} matter?
Previous convex relaxations for phase retrieval (e.g., PhaseLift, and PhaseCut) were based on the idea of lifting and
semidefinite programming (SDP). While lifting-based methods are technically computationally
tractable, their dependence on SDP prohibits their scalability. In contrast, \eqref{linmax} operates in the
natural domain of the problem and competes with non-convex methods for phase retrieval (e.g., Wirtinger Flow). It also benefits from versatility,
flexibility, and robustness that is associated with convex programming. More importantly, as discussed
below, the principles used in formulation and analysis of \eqref{linmax} apply in a more general setting.
What about the general case \eqref{cvxreg}?
In [BR17b], I proposed the convex program
\begin{equation}
\begin{aligned}
\operatorname*{argmax}_{\boldsymbol{x}}\ & \langle \boldsymbol{a}_0,\boldsymbol{x}\rangle \\
\text{subject to}\ & \sum_{m=1}^M \max\{f_m(\boldsymbol{x})-y_m, 0\} \le \varepsilon\, ,
\end{aligned} \label{anchored_reg}
\end{equation}
where \boldsymbol{a}_0 is an “anchor vector” that obeys
\begin{equation*}
\langle\boldsymbol{a}_0,\boldsymbol{x}_\star\rangle \ge \delta
\left\lVert\boldsymbol{a}_0\right\rVert_2\left\lVert\boldsymbol{x}_\star\right\rVert_2\,,
\end{equation*}
as an estimator for the general regression problem \eqref{cvxreg}. Some schemes for constructing the anchor
from the measurements are described in the paper, but we omit the discussion for brevity. Furthermore, to
avoid technical details, here I state the main result of the paper (i.e., Theorem 2.1) on the sample complexity of
\eqref{anchored_reg} in an informal way. In the proved bound, there are two important quantities. The first
quantity, \mathfrak{C}_M(\mathcal{A}_\delta), measures the “size” of a set
\mathcal{A}_\delta that depends only on \boldsymbol{x}_\star and \delta, with respect to the
randomness in the gradients \nabla f_m(\boldsymbol{x}_\star). The second quantity,
p_\tau(\mathcal{A}_\delta), is some measure of the “eccentricity” of the random vector
\nabla f_m(\boldsymbol{x}_\star) with respect to the set \mathcal{A}_\delta in terms of a parameter
\tau. Ignoring some details, the result established in [BR17b] basically states
that
\begin{equation*}
M \gtrsim \left(\frac{\mathfrak{C}_M(\mathcal{A}_\delta)}{\tau p_\tau(\mathcal{A}_\delta)}\right)^2\,,
\end{equation*}
measurements are sufficient to guarantee that \eqref{anchored_reg} yields an accurate estimate.
An important characteristic of networks in many applications is their connectivity which is often a
crucial factor in the performance of the network. An interesting and important problem is then to measure
robustness of the connectivity under some form of perturbation of the network. Site percolation, or
simply random removal of nodes as illustrated in Figure 2, is one of these
perturbation models that is studied in mathematics and statistical physics.
Figure 2. Under a site percolation, the surviving subgraph is no longer
connected
Algebraic connectivity of a graph is a an analytical measure of connectivity that is also related to
the conductance of the graph through the Cheeger's inequality. Formally, the algebraic connectivity of a
graph with the adjacency matrix \boldsymbol{A} can be defined as the second smallest eigenvalue
of the graph Laplacian \boldsymbol{L} = \boldsymbol{D} - \boldsymbol{A} where
\boldsymbol{D}=\mathrm{diag}(\boldsymbol{A}\boldsymbol{1}) is the diagonal matrix of the vertex degrees.
In [BRT18], using tools from random matrix theory I derived a lower bound for algebraic
connectivity of a graph that survives from a generally non-homogeneous site percolation. In the special case
of homogeneous site percolation over a certain class of regular graphs, our analytical result virtually
coincides with the state-of-the-art that is established using refined combinatorial arguments.
S. Bahmani, “Variational tail bounds for norms of random vectors and matrices,” 2025. arXiv
S. Bahmani, “A fundamental accuracy—robustness trade-off in regression and classification,” 2024. arXiv
S. Bahmani, “Instance-dependent uniform tail bounds for
empirical processes,” 2022. arXiv
2024
S. Kim, S. Bahmani, K. Lee, “Max-linear regression by convex programming,”IEEE Trans. Info. Theory, 2024. IEEEXplorearXiv
2021
S. Bahmani, K. Lee, “Low-rank matrix estimation from rank-one
projections by unlifted convex optimization”, SIAM J. on Matrix Analysis
and Applications, 2021. arXivSIAM
S. Bahmani, “Nearly optimal robust mean estimation via empirical
characteristic function,”Bernoulli, 27(3): 2139–2158, 2021. arXivProj. Euclid
B. Ancelin, S. Bahmani, J. Romberg, “Decentralized
feature-distributed optimization for generalized linear models,”, 2021. arXiv
2020
S. Bahmani, J. Romberg, “Convex programming for estimation in
nonlinear recurrent models,”Journal of Machine Learning Research, (235):1–20,
2020. arXivJMLRCode
K. Lee, S. Bahmani, J. Romberg, Y. Eldar, “Phase retrieval of
low-rank matrices by anchored regression,”Information and Inference: A
Journal of the IMA, 2020. arXivOxford Journals
2019
S. Bahmani, “Estimation from nonlinear observations via convex
programming, with application to bilinear regression,”Electronic J. of Statistics,
13(1): 1978–2011. 2019. arXivProj. Euclid
2018
S. Bahmani and J. Romberg, “Solving
equations of random convex functions via anchored regression,”Foundations of Computational Mathematics, 19(4):813–841, 2019. arXivSpringer
S. Bahmani, J. Romberg, P. Tetali, “Algebraic
connectivity under site percolation in finite weighted graphs,”IEEE Trans. on Network Science and Engineering, 5(2):86–91, 2018.
arXivIEEEXplore
2017
S. Bahmani and J. Romberg, “A
flexible convex relaxation for phase retrieval,”Electronic Journal of Statistics,
11(2):5254–5281, 2017. (This is
an extended version of the AISTATS’17 paper.) Proj. Euclid
S. Bahmani and J. Romberg, “Phase
retrieval meets statistical learning theory: A flexible convex
relaxation,”In Proceedings of the 20th
International Conference on Artificial Intelligence and
Statistics (AISTATS'17), vol. 54 of Proceedings of Machine
Learning Research , pp. 252–260. (Best paper award)
arXivPMLR
2016
S. Bahmani and J. Romberg, “Near-optimal
estimation of simultaneously sparse and low-rank matrices from
nested linear measurements,”Information and
Inference: A Journal of the IMA 5(3):331–351, 2016. arXivOxford
Journals
S. Bahmani, P. Boufounos, and B. Raj, “Learning
model-based sparsity via projected gradient descent,”IEEE Trans. Info. Theory, 62(4):2092–2099, 2016. arXivIEEEXplore
2015
S. Bahmani and J. Romberg, “Sketching
for simultaneously sparse and low-rank covariance matrices,”
in Computational Advances in Multi-Sensor Adaptive Processing
(CAMSAP'15), IEEE 6th International Workshop on, pp.
357–360, Cancun, Mexico, Dec. 2015.arXivIEEEXplore
S. Bahmani and J. Romberg,
“Efficient compressive phase retrieval with constrained
sensing vectors,” in Advances in Neural
Information Processing Systems (NIPS'15), vol. 28, pp.
523–531, Montréal, Canada, Dec. 2015. arXivNIPS
S. Bahmani and J. Romberg, “Lifting
for blind deconvolution in random mask imaging:
Identifiability and convex relaxation,”SIAM
Journal on Imaging Sciences, 8(4):2203–2238, 2015. arXivSIAM
S. Bahmani and J. Romberg, “Compressive
deconvolution in random mask imaging,”IEEE
Trans. on Computational Imaging, 1(4):236–246, 2015. arXivIEEEXplore
2013
S. Bahmani, B. Raj, and P. T. Boufounos, “Greedy
sparsity-constrained optimization,”Journal of
Machine Learning Research, 14(3):807–841, 2013. arXivJMLRCode
S. Bahmani, P. Boufounos, and B. Raj, “Robust
1-bit compressive sensing via gradient support pursuit,”
Apr. 2013. arXiv
2012
S. Bahmani, B. Raj, “A
unifying analysis of projected gradient descent for
\ell_p-constrained least squares,”Applied
and Computational Harmonic Analysis, 34(3):366–378, 2012. arXivElsevier
2011
S. Bahmani, P. Boufonos, and B. Raj, “Greedy
sparsity-constrained optimization,” in Conf.
Record of the 45th Asilomar Conference on Signals, Systems, and
Computers (ASILOMAR'11), pp. 1148–1152, Pacific Grove, CA,
Nov. 2011.
IEEEXploreSlidesCode
2010
S. Bahmani, I. Bajić, and A. HajShirmohammadi, “Joint decoding
of
unequally protected
JPEG2000 images and Reed-Solomon codes,”IEEE
Trans. Image Processing, 19(10):2693–2704, Oct. 2010. IEEEXplore
2009
S. Bahmani, I. Bajić, and A. HajShirmohammadi, “Improved joint
source channel decoding of
JPEG2000 images and Reed-Solomon codes,” Proc.
IEEE ICC'09, Dresden, Germany, Jun. 2009. IEEEXplore
2008
S. Bahmani, I. Bajić, A. HajShirmohammadi, “Joint
source channel decoding of JPEG2000 images with unequal loss
protection,”Proc. IEEE ICASSP'08, pp.
1365–1368, Las Vegas, NV, Mar. 2008. IEEEXplore
Thesis
S. Bahmani, Algorithms
for sparsity-constrained optimization, PhD dissertation,
Department of Electrical & Computer Engineernig, Carnegie
Mellon University, Pittsburgh, PA, Feb. 2013. PDF