diff --git a/Project.toml b/Project.toml
index e580847c..4d78bbd4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ApproximateGPs"
 uuid = "298c2ebc-0411-48ad-af38-99e88101b606"
 authors = ["JuliaGaussianProcesses Team"]
-version = "0.3.3"
+version = "0.3.4"
 
 [deps]
 AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
diff --git a/examples/c-comparisons/script.jl b/examples/c-comparisons/script.jl
index 011c17fd..287ef47a 100644
--- a/examples/c-comparisons/script.jl
+++ b/examples/c-comparisons/script.jl
@@ -127,10 +127,10 @@ lf2.f.kernel
 # Finally, we need to construct again the (approximate) posterior given the
 # observations for the latent GP with optimised hyperparameters:
 
-f_post2 = posterior(LaplaceApproximation(; f_init=objective.f), lf2(X), Y)
+f_post2 = posterior(LaplaceApproximation(; f_init=objective.cache.f), lf2(X), Y)
 
-# By passing `f_init=objective.f` we let the Laplace approximation "warm-start"
-# at the last point of the inner-loop Newton optimisation; `objective.f` is a
+# By passing `f_init=objective.cache.f` we let the Laplace approximation "warm-start"
+# at the last point of the inner-loop Newton optimisation; `objective.cache` is a
 # field on the `objective` closure.
 
 # Let's plot samples from the approximate posterior for the optimised hyperparameters:
diff --git a/src/LaplaceApproximationModule.jl b/src/LaplaceApproximationModule.jl
index d43615f7..e9e968b6 100644
--- a/src/LaplaceApproximationModule.jl
+++ b/src/LaplaceApproximationModule.jl
@@ -75,13 +75,25 @@ closure passes its arguments to `build_latent_gp`, which must return the
 - `newton_maxiter=100`: maximum number of Newton steps.
 """
 function build_laplace_objective(build_latent_gp, xs, ys; kwargs...)
-    # TODO assumes type of `xs` will be same as `mean(lfx.fx)`
-    f = similar(xs, length(xs))  # will be mutated in-place to "warm-start" the Newton steps
-    return build_laplace_objective!(f, build_latent_gp, xs, ys; kwargs...)
+    cache = LaplaceObjectiveCache(nothing)
+    # cache.f will be mutated in-place to "warm-start" the Newton steps
+    # f should be similar(mean(lfx.fx)), but to construct lfx we would need the arguments
+    # so we set it to `nothing` initially, and set it to mean(lfx.fx) within the objective
+    return build_laplace_objective!(cache, build_latent_gp, xs, ys; kwargs...)
+end
+
+function build_laplace_objective!(f_init::Vector, build_latent_gp, xs, ys; kwargs...)
+    return build_laplace_objective!(
+        LaplaceObjectiveCache(f_init), build_latent_gp, xs, ys; kwargs...
+    )
+end
+
+mutable struct LaplaceObjectiveCache
+    f::Union{Nothing,Vector}
 end
 
 function build_laplace_objective!(
-    f,
+    cache::LaplaceObjectiveCache,
     build_latent_gp,
     xs,
     ys;
@@ -98,16 +110,18 @@ function build_laplace_objective!(
             # Zygote does not like the try/catch within @info etc.
             @debug "Objective arguments: $args"
             # Zygote does not like in-place assignments either
-            if initialize_f
-                f .= mean(lfx.fx)
+            if cache.f === nothing
+                cache.f = mean(lfx.fx)
+            elseif initialize_f
+                cache.f .= mean(lfx.fx)
             end
         end
         f_opt, lml = laplace_f_and_lml(
-            lfx, ys; f_init=f, maxiter=newton_maxiter, callback=newton_callback
+            lfx, ys; f_init=cache.f, maxiter=newton_maxiter, callback=newton_callback
         )
         ignore_derivatives() do
             if newton_warmstart
-                f .= f_opt
+                cache.f .= f_opt
                 initialize_f = false
             end
         end
diff --git a/test/LaplaceApproximationModule.jl b/test/LaplaceApproximationModule.jl
index a3f705c2..547b60f3 100644
--- a/test/LaplaceApproximationModule.jl
+++ b/test/LaplaceApproximationModule.jl
@@ -23,7 +23,7 @@
         )
 
         lf = build_latent_gp(training_results.minimizer)
-        f_post = posterior(LaplaceApproximation(; f_init=objective.f), lf(xs), ys)
+        f_post = posterior(LaplaceApproximation(; f_init=objective.cache.f), lf(xs), ys)
         return f_post, training_results
     end
 
@@ -208,4 +208,14 @@
         res = res_array[end]
         @test res.q isa MvNormal
     end
+
+    @testset "GitHub issue #109" begin
+        build_latent_gp() = LatentGP(GP(SEKernel()), BernoulliLikelihood(), 1e-8)
+
+        x = ColVecs(randn(2, 5))
+        _, y = rand(build_latent_gp()(x))
+
+        objective = build_laplace_objective(build_latent_gp, x, y)
+        _ = objective()  # check that it works
+    end
 end